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Found 521 papers, 159 papers with code
Towards Generalizeable Semantic Product Search by Text Similarity Pre-training on Search Click Logs
Recently, semantic search has been successfully applied to E-commerce product search and the learned semantic space for query and product encoding are expected to generalize well to unseen queries or products.
HARD-Net: Hardness-AwaRe Discrimination Network for 3D Early Activity Prediction
In this paper, we propose a novel Hardness-AwaRe Discrimination Network (HARD-Net) to specifically investigate the relationships between the similar activity pairs that are hard to be discriminated.
Ranked #4 on
Skeleton Based Action Recognition
on UAV-Human
Unsupervised Multi-View CNN for Salient View Selection of 3D Objects and Scenes
We present an unsupervised 3D deep learning framework based on a ubiquitously true proposition named by us view-object consistency as it states that a 3D object and its projected 2D views always belong to the same object class.
GeoReF: Geometric Alignment Across Shape Variation for Category-level Object Pose Refinement
Object pose refinement is essential for robust object pose estimation.
DetCLIPv3: Towards Versatile Generative Open-vocabulary Object Detection
This is followed by a fine-tuning stage that leverages a small number of high-resolution samples to further enhance detection performance.
Fast Gradient Computation for Gromov-Wasserstein Distance
In this way, the original computational bottleneck is broken and the new entropic solution can be obtained with total quadratic time, which is almost optimal complexity.
JailbreakLens: Visual Analysis of Jailbreak Attacks Against Large Language Models
Addressing these concerns necessitates a comprehensive analysis of jailbreak prompts to evaluate LLMs' defensive capabilities and identify potential weaknesses.
Accel-NASBench: Sustainable Benchmarking for Accelerator-Aware NAS
We present a technique that allows searching for training proxies that reduce the cost of benchmark construction by significant margins, making it possible to construct realistic NAS benchmarks for large-scale datasets.
Map Optical Properties to Subwavelength Structures Directly via a Diffusion Model
The inverse design methods proposed for these subwavelength structures are vital to the development of new photonic devices.
BatSort: Enhanced Battery Classification with Transfer Learning for Battery Sorting and Recycling
Battery recycling is a critical process for minimizing environmental harm and resource waste for used batteries.
A diffusion MRI tractography atlas for concurrent white matter mapping across Eastern and Western populations
However, a comprehensive investigation into WM fiber tracts between Eastern and Western populations is challenged due to the lack of a cross-population WM atlas and the large site-specific variability of dMRI data.
Decoupled Pseudo-labeling for Semi-Supervised Monocular 3D Object Detection
Additionally, we present a DepthGradient Projection (DGP) module to mitigate optimization conflicts caused by noisy depth supervision of pseudo-labels, effectively decoupling the depth gradient and removing conflicting gradients.
Gradient-based Sampling for Class Imbalanced Semi-supervised Object Detection
To tackle the confirmation bias from incorrect pseudo labels of minority classes, the class-rebalancing sampling module resamples unlabeled data following the guidance of the gradient-based reweighting module.
SFOD: Spiking Fusion Object Detector
Thereby, we establish state-of-the-art classification results based on SNNs, achieving 93. 7\% accuracy on the NCAR dataset.
LayerDiff: Exploring Text-guided Multi-layered Composable Image Synthesis via Layer-Collaborative Diffusion Model
Specifically, an inter-layer attention module is designed to encourage information exchange and learning between layers, while a text-guided intra-layer attention module incorporates layer-specific prompts to direct the specific-content generation for each layer.
OpenOcc: Open Vocabulary 3D Scene Reconstruction via Occupancy Representation
We model the geometric structure of the scene with occupancy representation and distill the pre-trained open vocabulary model into a 3D language field via volume rendering for zero-shot inference.
TRELM: Towards Robust and Efficient Pre-training for Knowledge-Enhanced Language Models
KEPLMs are pre-trained models that utilize external knowledge to enhance language understanding.
Affective Behaviour Analysis via Integrating Multi-Modal Knowledge
Affective Behavior Analysis aims to facilitate technology emotionally smart, creating a world where devices can understand and react to our emotions as humans do.
Re-Search for The Truth: Multi-round Retrieval-augmented Large Language Models are Strong Fake News Detectors
The proliferation of fake news has had far-reaching implications on politics, the economy, and society at large.
OneVOS: Unifying Video Object Segmentation with All-in-One Transformer Framework
Contemporary Video Object Segmentation (VOS) approaches typically consist stages of feature extraction, matching, memory management, and multiple objects aggregation.
Query-guided Prototype Evolution Network for Few-Shot Segmentation
To address this, we present the Query-guided Prototype Evolution Network (QPENet), a new method that integrates query features into the generation process of foreground and background prototypes, thereby yielding customized prototypes attuned to specific queries.
ClickVOS: Click Video Object Segmentation
To address these limitations, we propose the setting named Click Video Object Segmentation (ClickVOS) which segments objects of interest across the whole video according to a single click per object in the first frame.
Aligning Large Language Models for Controllable Recommendations
Inspired by the exceptional general intelligence of Large Language Models (LLMs), researchers have begun to explore their application in pioneering the next generation of recommender systems - systems that are conversational, explainable, and controllable.
Popeye: A Unified Visual-Language Model for Multi-Source Ship Detection from Remote Sensing Imagery
Ship detection needs to identify ship locations from remote sensing (RS) scenes.
SMAUG: A Sliding Multidimensional Task Window-Based MARL Framework for Adaptive Real-Time Subtask Recognition
Instead of making behavioral decisions directly from the exponentially expanding joint observational-action space, subtask-based multi-agent reinforcement learning (MARL) methods enable agents to learn how to tackle different subtasks.
Hierarchical Reinforcement Learning
Multi-agent Reinforcement Learning
+4
StaPep: an open-source tool for the structure prediction and feature extraction of hydrocarbon-stapled peptides
Many tools exist for extracting structural and physiochemical descriptors from linear peptides to predict their properties, but similar tools for hydrocarbon-stapled peptides are lacking. Here, we present StaPep, a Python-based toolkit designed for generating 2D/3D structures and calculating 21 distinct features for hydrocarbon-stapled peptides. The current version supports hydrocarbon-stapled peptides containing 2 non-standard amino acids (norleucine and 2-aminoisobutyric acid) and 6 nonnatural anchoring residues (S3, S5, S8, R3, R5 and R8). Then we established a hand-curated dataset of 201 hydrocarbon-stapled peptides and 384 linear peptides with sequence information and experimental membrane permeability, to showcase StaPep's application in artificial intelligence projects. A machine learning-based predictor utilizing above calculated features was developed with AUC of 0. 85, for identifying cell-penetrating hydrocarbon-stapled peptides. StaPep's pipeline spans data retrieval, cleaning, structure generation, molecular feature calculation, and machine learning model construction for hydrocarbon-stapled peptides. The source codes and dataset are freely available on Github: https://github. com/dahuilangda/stapep_package.
Explainable Session-based Recommendation via Path Reasoning
This paper explores providing explainability for session-based recommendation (SR) by path reasoning.
Lemur: Log Parsing with Entropy Sampling and Chain-of-Thought Merging
Log parsing, which entails transforming raw log messages into structured templates, constitutes a critical phase in the automation of log analytics.
Don't Forget Your Reward Values: Language Model Alignment via Value-based Calibration
While Reinforcement Learning from Human Feedback (RLHF) significantly enhances the generation quality of Large Language Models (LLMs), recent studies have raised concerns regarding the complexity and instability associated with the Proximal Policy Optimization (PPO) algorithm, proposing a series of order-based calibration methods as viable alternatives.
Reading Relevant Feature from Global Representation Memory for Visual Object Tracking
Therefore, using all features in the template and memory can lead to redundancy and impair tracking performance.
Gaussian Process Neural Additive Models
Deep neural networks have revolutionized many fields, but their black-box nature also occasionally prevents their wider adoption in fields such as healthcare and finance, where interpretable and explainable models are required.
Do Large Language Models Understand Logic or Just Mimick Context?
Over the past few years, the abilities of large language models (LLMs) have received extensive attention, which have performed exceptionally well in complicated scenarios such as logical reasoning and symbolic inference.
Pattern-wise Transparent Sequential Recommendation
However, achieving both model transparency and recommendation performance simultaneously is challenging, especially for models that take the entire sequence of items as input without screening.
Random Projection Layers for Multidimensional Time Series Forecasting
All-Multi-Layer Perceptron (all-MLP) mixer models have been shown to be effective for time series forecasting problems.
Translating Images to Road Network:A Non-Autoregressive Sequence-to-Sequence Approach
Instead, our work establishes a unified representation of both types of data domain by projecting both Euclidean and non-Euclidean data into an integer series called RoadNet Sequence.
Understanding the Role of Cross-Entropy Loss in Fairly Evaluating Large Language Model-based Recommendation
Large language models (LLMs) have gained much attention in the recommendation community; some studies have observed that LLMs, fine-tuned by the cross-entropy loss with a full softmax, could achieve state-of-the-art performance already.
LaneGraph2Seq: Lane Topology Extraction with Language Model via Vertex-Edge Encoding and Connectivity Enhancement
Accurate extraction of lane graphs relies on precisely estimating vertex and edge information within the DAG.
EarthGPT: A Universal Multi-modal Large Language Model for Multi-sensor Image Comprehension in Remote Sensing Domain
Multi-modal large language models (MLLMs) have demonstrated remarkable success in vision and visual-language tasks within the natural image domain.
Fortifying Ethical Boundaries in AI: Advanced Strategies for Enhancing Security in Large Language Models
Recent advancements in large language models (LLMs) have significantly enhanced capabilities in natural language processing and artificial intelligence.
Forging Vision Foundation Models for Autonomous Driving: Challenges, Methodologies, and Opportunities
The rise of large foundation models, trained on extensive datasets, is revolutionizing the field of AI.
PUPAE: Intuitive and Actionable Explanations for Time Series Anomalies
In this work we introduce a domain agnostic counterfactual explanation technique to produce explanations for time series anomalies.
Contrastive Learning with Negative Sampling Correction
To correct the negative sampling bias, we propose a novel contrastive learning method named Positive-Unlabeled Contrastive Learning (PUCL).
Fine-Grained Embedding Dimension Optimization During Training for Recommender Systems
Huge embedding tables in modern Deep Learning Recommender Models (DLRM) require prohibitively large memory during training and inference.
Knowledge-enhanced Multi-perspective Video Representation Learning for Scene Recognition
With the explosive growth of video data in real-world applications, a comprehensive representation of videos becomes increasingly important.
Holistic Autonomous Driving Understanding by Bird's-Eye-View Injected Multi-Modal Large Models
BEV-InMLLM integrates multi-view, spatial awareness, and temporal semantics to enhance MLLMs' capabilities on NuInstruct tasks.
Has Your Pretrained Model Improved? A Multi-head Posterior Based Approach
The emergence of pre-trained models has significantly impacted Natural Language Processing (NLP) and Computer Vision to relational datasets.
Towards a Simultaneous and Granular Identity-Expression Control in Personalized Face Generation
We devise a novel diffusion model that can undertake the task of simultaneously face swapping and reenactment.
Enhancing dysarthria speech feature representation with empirical mode decomposition and Walsh-Hadamard transform
This algorithm effectively addresses the challenges of the imbalanced dataset and non-linearity in dysarthric speech and simultaneously provides a robust representation of the local pathological features of the vocal folds and tracts.
Symbolic Cognitive Diagnosis via Hybrid Optimization for Intelligent Education Systems
The SCD framework incorporates the symbolic tree to explicably represent the complicated student-exercise interaction function, and utilizes gradient-based optimization methods to effectively learn the student and exercise parameters.
PanGu-Draw: Advancing Resource-Efficient Text-to-Image Synthesis with Time-Decoupled Training and Reusable Coop-Diffusion
Current large-scale diffusion models represent a giant leap forward in conditional image synthesis, capable of interpreting diverse cues like text, human poses, and edges.
SERF: Fine-Grained Interactive 3D Segmentation and Editing with Radiance Fields
Although significant progress has been made in the field of 2D-based interactive editing, fine-grained 3D-based interactive editing remains relatively unexplored.
Automatic bony structure segmentation and curvature estimation on ultrasound cervical spine images -- a feasibility study
In this study, a portable ultrasound imaging system was applied to acquire cervical spine image volume.
DMT: Comprehensive Distillation with Multiple Self-supervised Teachers
Numerous self-supervised learning paradigms, such as contrastive learning and masked image modeling, have been proposed to acquire powerful and general representations from unlabeled data.
Design, construction and evaluation of emotional multimodal pathological speech database
The automatic recognition tested on speech and glottal data, with average accuracy of 78% for controls and 60% for patients in audio, while 51% for controls and 38% for patients in glottal data, indicating an influence of the disease on emotional expression.
Native Language Identification with Large Language Models
We present the first experiments on Native Language Identification (NLI) using LLMs such as GPT-4.
BIVDiff: A Training-Free Framework for General-Purpose Video Synthesis via Bridging Image and Video Diffusion Models
Now text-to-image foundation models are widely applied to various downstream image synthesis tasks, such as controllable image generation and image editing, while downstream video synthesis tasks are less explored for several reasons.
Interpretable Knowledge Tracing via Response Influence-based Counterfactual Reasoning
Knowledge tracing (KT) plays a crucial role in computer-aided education and intelligent tutoring systems, aiming to assess students' knowledge proficiency by predicting their future performance on new questions based on their past response records.
SimulFlow: Simultaneously Extracting Feature and Identifying Target for Unsupervised Video Object Segmentation
We evaluate our method on several benchmark datasets and achieve state-of-the-art results.
Towards Better Parameter-Efficient Fine-Tuning for Large Language Models: A Position Paper
This paper delves into the pressing need in Parameter-Efficient Fine-Tuning (PEFT) for Large Language Models (LLMs).
Soft Random Sampling: A Theoretical and Empirical Analysis
Soft random sampling (SRS) is a simple yet effective approach for efficient training of large-scale deep neural networks when dealing with massive data.
Multi-Resolution Planar Region Extraction for Uneven Terrains
This paper studies the problem of extracting planar regions in uneven terrains from unordered point cloud measurements.
MS-Former: Memory-Supported Transformer for Weakly Supervised Change Detection with Patch-Level Annotations
How to capture the semantic variations associated with the changed and unchanged regions from the patch-level annotations to obtain promising change results is the critical challenge for the weakly supervised change detection task.
Scaling User Modeling: Large-scale Online User Representations for Ads Personalization in Meta
To address these challenges, we present Scaling User Modeling (SUM), a framework widely deployed in Meta's ads ranking system, designed to facilitate efficient and scalable sharing of online user representation across hundreds of ads models.
From Complex to Simple: Unraveling the Cognitive Tree for Reasoning with Small Language Models
Reasoning is a distinctive human capacity, enabling us to address complex problems by breaking them down into a series of manageable cognitive steps.
VoxNeRF: Bridging Voxel Representation and Neural Radiance Fields for Enhanced Indoor View Synthesis
Creating high-quality view synthesis is essential for immersive applications but continues to be problematic, particularly in indoor environments and for real-time deployment.
From Input to Output: A Multi-layer Knowledge Distillation Framework for Compressing Recommendation Models
In this paper, we decompose recommendation models into three layers, i. e., the input layer, the intermediate layer, and the output layer, and address deficiencies layer by layer.
Everything of Thoughts: Defying the Law of Penrose Triangle for Thought Generation
To address these limitations, we introduce a novel thought prompting approach called "Everything of Thoughts" (XoT) to defy the law of "Penrose triangle of existing thought paradigms.
Temporal Treasure Hunt: Content-based Time Series Retrieval System for Discovering Insights
To facilitate this investigation, we introduce a CTSR benchmark dataset that comprises time series data from a variety of domains, such as motion, power demand, and traffic.
Sketching Multidimensional Time Series for Fast Discord Mining
In this work, we propose a sketch for discord mining among multi-dimensional time series.
Ego-Network Transformer for Subsequence Classification in Time Series Data
The ego-networks of all subsequences collectively form a time series subsequence graph, and we introduce an algorithm to efficiently construct this graph.
Time Series Synthesis Using the Matrix Profile for Anonymization
As a result, unmodified data mining tools can obtain near-identical performance on the synthesized time series as on the original time series.
Visual Analytics for Efficient Image Exploration and User-Guided Image Captioning
On the one hand, by visually examining the captions automatically generated from language-image models for an image dataset, we gain deeper insights into the semantic underpinnings of the visual contents, unearthing data biases that may be entrenched within the dataset.
Lightweight super resolution network for point cloud geometry compression
This paper presents an approach for compressing point cloud geometry by leveraging a lightweight super-resolution network.
PET Tracer Conversion among Brain PET via Variable Augmented Invertible Network
Meanwhile, the invertible network iteratively estimates the resultant DOPA PET data and compares it to the reference DOPA PET data.
BM2CP: Efficient Collaborative Perception with LiDAR-Camera Modalities
Collaborative perception enables agents to share complementary perceptual information with nearby agents.
Learning Interpretable Rules for Scalable Data Representation and Classification
Rule-based models, e. g., decision trees, are widely used in scenarios demanding high model interpretability for their transparent inner structures and good model expressivity.
Parallel compressive super-resolution imaging with wide field-of-view based on physics enhanced network
By training the network with the prior OTF of an arbitrary 128x128-pixel region and fine-tuning the network with other OTFs within rest regions of FOV, we realize both mask optimization and super-resolution imaging with up to 1020x1500 wide FOV.
FATA-Trans: Field And Time-Aware Transformer for Sequential Tabular Data
FATA-Trans is field- and time-aware for sequential tabular data.
Explore the Effect of Data Selection on Poison Efficiency in Backdoor Attacks
In this study, we focus on improving the poisoning efficiency of backdoor attacks from the sample selection perspective.
BioT5: Enriching Cross-modal Integration in Biology with Chemical Knowledge and Natural Language Associations
Recent advancements in biological research leverage the integration of molecules, proteins, and natural language to enhance drug discovery.
Ranked #2 on
Text-based de novo Molecule Generation
on ChEBI-20
HI-SLAM: Monocular Real-time Dense Mapping with Hybrid Implicit Fields
For global consistency, we propose an efficient Sim(3)-based pose graph bundle adjustment (PGBA) approach to run online loop closing and mitigate the pose and scale drift.
An Efficient Content-based Time Series Retrieval System
A Content-based Time Series Retrieval (CTSR) system is an information retrieval system for users to interact with time series emerged from multiple domains, such as finance, healthcare, and manufacturing.
Toward a Foundation Model for Time Series Data
A foundation model is a machine learning model trained on a large and diverse set of data, typically using self-supervised learning-based pre-training techniques, that can be adapted to various downstream tasks.
Multitask Learning for Time Series Data with 2D Convolution
In this paper, we investigate the application of MTL to the time series classification (TSC) problem.
Feature Interaction Aware Automated Data Representation Transformation
Creating an effective representation space is crucial for mitigating the curse of dimensionality, enhancing model generalization, addressing data sparsity, and leveraging classical models more effectively.
Revealing the Power of Spatial-Temporal Masked Autoencoders in Multivariate Time Series Forecasting
To address these issues, we propose Spatial-Temporal Masked Autoencoders (STMAE), an MTS forecasting framework that leverages masked autoencoders to enhance the performance of spatial-temporal baseline models.
Graph-enhanced Optimizers for Structure-aware Recommendation Embedding Evolution
Embedding plays a critical role in modern recommender systems because they are virtual representations of real-world entities and the foundation for subsequent decision models.
PanoVOS: Bridging Non-panoramic and Panoramic Views with Transformer for Video Segmentation
Our dataset poses new challenges in panoramic VOS and we hope that our PanoVOS can advance the development of panoramic segmentation/tracking.
Learning Segment Similarity and Alignment in Large-Scale Content Based Video Retrieval
SSAN is based on two newly proposed modules in video retrieval: (1) An efficient Self-supervised Keyframe Extraction (SKE) module to reduce redundant frame features, (2) A robust Similarity Pattern Detection (SPD) module for temporal alignment.
Multi-view Fuzzy Representation Learning with Rules based Model
Second, a new regularization method based on L_(2, 1)-norm regression is proposed to mine the consistency information between views, while the geometric structure of the data is preserved through the Laplacian graph.
An Empirical Study of Attention Networks for Semantic Segmentation
What's more, various methods utilize attention in semantic segmentation, but the conclusion of these methods is lacking.
SoccerNet 2023 Challenges Results
More information on the tasks, challenges, and leaderboards are available on https://www. soccer-net. org.
HiLM-D: Towards High-Resolution Understanding in Multimodal Large Language Models for Autonomous Driving
For the first time, we leverage singular multimodal large language models (MLLMs) to consolidate multiple autonomous driving tasks from videos, i. e., the Risk Object Localization and Intention and Suggestion Prediction (ROLISP) task.
Reuse and Diffuse: Iterative Denoising for Text-to-Video Generation
Conditioned on an initial video clip with a small number of frames, additional frames are iteratively generated by reusing the original latent features and following the previous diffusion process.
Towards High-Fidelity Text-Guided 3D Face Generation and Manipulation Using only Images
In this paper, we propose a text-guided 3D faces generation method, refer as TG-3DFace, for generating realistic 3D faces using text guidance.
Examining User-Friendly and Open-Sourced Large GPT Models: A Survey on Language, Multimodal, and Scientific GPT Models
Generative pre-trained transformer (GPT) models have revolutionized the field of natural language processing (NLP) with remarkable performance in various tasks and also extend their power to multimodal domains.
Accel-GCN: High-Performance GPU Accelerator Design for Graph Convolution Networks
Utilizing these principles, we formulated a kernel for sparse matrix multiplication (SpMM) in GCNs that employs block-level partitioning and combined warp strategy.
GrowCLIP: Data-aware Automatic Model Growing for Large-scale Contrastive Language-Image Pre-training
Compared with the existing methods, GrowCLIP improves 2. 3% average top-1 accuracy on zero-shot image classification of 9 downstream tasks.
DiffDis: Empowering Generative Diffusion Model with Cross-Modal Discrimination Capability
DiffDis first formulates the image-text discriminative problem as a generative diffusion process of the text embedding from the text encoder conditioned on the image.
Point-aware Interaction and CNN-induced Refinement Network for RGB-D Salient Object Detection
By integrating complementary information from RGB image and depth map, the ability of salient object detection (SOD) for complex and challenging scenes can be improved.
Frequency Perception Network for Camouflaged Object Detection
Camouflaged object detection (COD) aims to accurately detect objects hidden in the surrounding environment.
SDDNet: Style-guided Dual-layer Disentanglement Network for Shadow Detection
Despite significant progress in shadow detection, current methods still struggle with the adverse impact of background color, which may lead to errors when shadows are present on complex backgrounds.
MEDOE: A Multi-Expert Decoder and Output Ensemble Framework for Long-tailed Semantic Segmentation
The proposed two-sage framework comprises a multi-expert decoder (MED) and a multi-expert output ensemble (MOE).
Beyond Semantics: Learning a Behavior Augmented Relevance Model with Self-supervised Learning
Drawing inspiration from this, we devise a novel Behavior Augmented Relevance Learning model for Alipay Search (BARL-ASe) that leverages neighbor queries of target item and neighbor items of target query to complement target query-item semantic matching.
Gaussian-based Probabilistic Deep Supervision Network for Noise-Resistant QoS Prediction
Quality of Service (QoS) prediction is an essential task in recommendation systems, where accurately predicting unknown QoS values can improve user satisfaction.
Dynamic Token-Pass Transformers for Semantic Segmentation
Vision transformers (ViT) usually extract features via forwarding all the tokens in the self-attention layers from top to toe.
Knowledge-aware Collaborative Filtering with Pre-trained Language Model for Personalized Review-based Rating Prediction
For the first issue, to utilize rich knowledge, KCF-PLM develops a transformer network to model the interactions of the extracted aspects w. r. t.
EmbeddingTree: Hierarchical Exploration of Entity Features in Embedding
The tool helps users discover nuance features of data entities, perform feature denoising/injecting in embedding training, and generate embeddings for unseen entities.
Robust Positive-Unlabeled Learning via Noise Negative Sample Self-correction
To mitigate the impact of label uncertainty and improve the robustness of learning with positive and unlabeled data, we propose a new robust PU learning method with a training strategy motivated by the nature of human learning: easy cases should be learned first.
Computational Approaches for Traditional Chinese Painting: From the "Six Principles of Painting" Perspective
Second, we created a four-stage framework to illustrate the purposes of TCP applications.
Learning and Evaluating Human Preferences for Conversational Head Generation
In this paper, we propose a novel learning-based evaluation metric named Preference Score (PS) for fitting human preference according to the quantitative evaluations across different dimensions.
Semi-DETR: Semi-Supervised Object Detection with Detection Transformers
Specifically, we propose a Stage-wise Hybrid Matching strategy that combines the one-to-many assignment and one-to-one assignment strategies to improve the training efficiency of the first stage and thus provide high-quality pseudo labels for the training of the second stage.
Visual Analytics For Machine Learning: A Data Perspective Survey
The past decade has witnessed a plethora of works that leverage the power of visualization (VIS) to interpret machine learning (ML) models.
Contrastive Graph Pooling for Explainable Classification of Brain Networks
Our contributions underscore the potential of ContrastPool for advancing the understanding of brain networks and neurodegenerative conditions.
CityTrack: Improving City-Scale Multi-Camera Multi-Target Tracking by Location-Aware Tracking and Box-Grained Matching
Targets in urban traffic scenes often undergo occlusion, illumination changes, and perspective changes, making it difficult to associate targets across different cameras accurately.
Interactive Conversational Head Generation
Based on ViCo and ViCo-X, we define three novel tasks targeting the interaction modeling during the face-to-face conversation: 1) responsive listening head generation making listeners respond actively to the speaker with non-verbal signals, 2) expressive talking head generation guiding speakers to be aware of listeners' behaviors, and 3) conversational head generation to integrate the talking/listening ability in one interlocutor.
Cross-Element Combinatorial Selection for Multi-Element Creative in Display Advertising
The effectiveness of ad creatives is greatly influenced by their visual appearance.
SUGAR: Spherical Ultrafast Graph Attention Framework for Cortical Surface Registration
Cortical surface registration plays a crucial role in aligning cortical functional and anatomical features across individuals.
Understanding recent deep-learning techniques for identifying collective variables of molecular dynamics
High-dimensional metastable molecular system can often be characterised by a few features of the system, i. e. collective variables (CVs).
Deep Equilibrium Multimodal Fusion
Multimodal fusion integrates the complementary information present in multiple modalities and has gained much attention recently.
A Theory of Complex Adaptive Learning Behavior in Complex Adaptive Systems and a Non-Localized Wave Equation in Quantum Mechanics
Keywords: complex adaptive systems, complex adaptive learning, universal law, non-localized wave equation, interactively coherent entanglement, interactively coherent adaptation PACS: 89. 75.-k (Complex Systems); 89. 65. Gh (Economics, Econophysics, Financial Markets, Business and Management); 03. 65. Ud (Entanglement and Quantum Nonlocality)
A Collaborative Transfer Learning Framework for Cross-domain Recommendation
The disadvantage of the former is that the data from other domains is not utilized by a single domain model, while the latter leverage all the data from different domains, but the fine-tuned model of transfer learning may trap the model in a local optimum of the source domain, making it difficult to fit the target domain.
FlowFace++: Explicit Semantic Flow-supervised End-to-End Face Swapping
The discriminator is shape-aware and relies on a semantic flow-guided operation to explicitly calculate the shape discrepancies between the target and source faces, thus optimizing the face swapping network to generate highly realistic results.
Visual-Aware Text-to-Speech
Dynamically synthesizing talking speech that actively responds to a listening head is critical during the face-to-face interaction.
CMLM-CSE: Based on Conditional MLM Contrastive Learning for Sentence Embeddings
Traditional comparative learning sentence embedding directly uses the encoder to extract sentence features, and then passes in the comparative loss function for learning.
PUGAN: Physical Model-Guided Underwater Image Enhancement Using GAN with Dual-Discriminators
Among existing UIE methods, Generative Adversarial Networks (GANs) based methods perform well in visual aesthetics, while the physical model-based methods have better scene adaptability.
ScrollTimes: Tracing the Provenance of Paintings as a Window into History
The study of cultural artifact provenance, tracing ownership and preservation, holds significant importance in archaeology and art history.
Object Detection in Hyperspectral Image via Unified Spectral-Spatial Feature Aggregation
S2ADet comprises a hyperspectral information decoupling (HID) module, a two-stream feature extraction network, and a one-stage detection head.
A Proxy-Free Strategy for Practically Improving the Poisoning Efficiency in Backdoor Attacks
Poisoning efficiency is a crucial factor in poisoning-based backdoor attacks.
Approximate Maximum-Likelihood RIS-Aided Positioning
In this paper, we develop a RIS-aided positioning framework to locate a UE in environments where the LOS path may or may not be available.
E2E-LOAD: End-to-End Long-form Online Action Detection
Furthermore, we propose a novel and efficient inference mechanism that accelerates heavy spatial-temporal exploration.
NFTVis: Visual Analysis of NFT Performance
A non-fungible token (NFT) is a data unit stored on the blockchain.
PDT: Pretrained Dual Transformers for Time-aware Bipartite Graphs
Pre-training on large models is prevalent and emerging with the ever-growing user-generated content in many machine learning application categories.
Can LLMs like GPT-4 outperform traditional AI tools in dementia diagnosis? Maybe, but not today
In this paper, we explore the potential of LLMs such as GPT-4 to outperform traditional AI tools in dementia diagnosis.
Hybrid Driven Learning for Channel Estimation in Intelligent Reflecting Surface Aided Millimeter Wave Communications
Intelligent reflecting surfaces (IRS) have been proposed in millimeter wave (mmWave) and terahertz (THz) systems to achieve both coverage and capacity enhancement, where the design of hybrid precoders, combiners, and the IRS typically relies on channel state information.
Referred by Multi-Modality: A Unified Temporal Transformer for Video Object Segmentation
In this paper, we propose MUTR, a Multi-modal Unified Temporal transformer for Referring video object segmentation.
MRN: Multiplexed Routing Network for Incremental Multilingual Text Recognition
In this paper, we propose the Incremental MLTR (IMLTR) task in the context of incremental learning (IL), where different languages are introduced in batches.
Ranked #1 on
Incremental Learning
on MLT17
MolXPT: Wrapping Molecules with Text for Generative Pre-training
Considering that text is the most important record for scientific discovery, in this paper, we propose MolXPT, a unified language model of text and molecules pre-trained on SMILES (a sequence representation of molecules) wrapped by text.
Ranked #1 on
Molecular Property Prediction
on ClinTox
LLaMA-Adapter V2: Parameter-Efficient Visual Instruction Model
This strategy effectively alleviates the interference between the two tasks of image-text alignment and instruction following and achieves strong multi-modal reasoning with only a small-scale image-text and instruction dataset.
Ranked #6 on
Visual Question Answering (VQA)
on InfiMM-Eval
Instruction Following
Optical Character Recognition (OCR)
+7
SACANet: scene-aware class attention network for semantic segmentation of remote sensing images
In this paper, we integrate both scene-aware and class attentions to propose a scene-aware class attention network (SACANet) for semantic segmentation of remote sensing images.
STNet: Spatial and Temporal feature fusion network for change detection in remote sensing images
As an important task in remote sensing image analysis, remote sensing change detection (RSCD) aims to identify changes of interest in a region from spatially co-registered multi-temporal remote sensing images, so as to monitor the local development.
OpenLane-V2: A Topology Reasoning Benchmark for Unified 3D HD Mapping
Accurately depicting the complex traffic scene is a vital component for autonomous vehicles to execute correct judgments.
Network Pruning Spaces
Statistically, the structure of a winning subnetwork guarantees an approximately optimal ratio in this regime.
Frequency Decomposition to Tap the Potential of Single Domain for Generalization
Then, a new method that learns through multiple frequency domains is proposed.
DetCLIPv2: Scalable Open-Vocabulary Object Detection Pre-training via Word-Region Alignment
This paper presents DetCLIPv2, an efficient and scalable training framework that incorporates large-scale image-text pairs to achieve open-vocabulary object detection (OVD).
RSPT: Reconstruct Surroundings and Predict Trajectories for Generalizable Active Object Tracking
However, building a generalizable active tracker that works robustly across different scenarios remains a challenge, especially in unstructured environments with cluttered obstacles and diverse layouts.
HS-Pose: Hybrid Scope Feature Extraction for Category-level Object Pose Estimation
In this paper, we focus on the problem of category-level object pose estimation, which is challenging due to the large intra-category shape variation.
ByteTrackV2: 2D and 3D Multi-Object Tracking by Associating Every Detection Box
We propose a hierarchical data association strategy to mine the true objects in low-score detection boxes, which alleviates the problems of object missing and fragmented trajectories.
Ambiguity-Resistant Semi-Supervised Learning for Dense Object Detection
It employs a "divide-and-conquer" strategy and separately exploits positives for the classification and localization task, which is more robust to the assignment ambiguity.
Ranked #1 on
Semi-Supervised Object Detection
on COCO 10% labeled data
(detector metric)
How Does Attention Work in Vision Transformers? A Visual Analytics Attempt
Multi-head self-attentions are then applied to the sequence to learn the attention between patches.
Multi-modal Facial Affective Analysis based on Masked Autoencoder
The CVPR 2023 Competition on Affective Behavior Analysis in-the-wild (ABAW) is dedicated to providing high-quality and large-scale Aff-wild2 for the recognition of commonly used emotion representations, such as Action Units (AU), basic expression categories(EXPR), and Valence-Arousal (VA).
SpatialFormer: Semantic and Target Aware Attentions for Few-Shot Learning
Then we derive two specific attention modules, named SpatialFormer Semantic Attention (SFSA) and SpatialFormer Target Attention (SFTA), to enhance the target object regions while reduce the background distraction.
LoG-CAN: local-global Class-aware Network for semantic segmentation of remote sensing images
We present LoG-CAN, a multi-scale semantic segmentation network with a global class-aware (GCA) module and local class-aware (LCA) modules to remote sensing images.
CapDet: Unifying Dense Captioning and Open-World Detection Pretraining
Besides, our CapDet also achieves state-of-the-art performance on dense captioning tasks, e. g., 15. 44% mAP on VG V1. 2 and 13. 98% on the VG-COCO dataset.
Joint Task and Data Oriented Semantic Communications: A Deep Separate Source-channel Coding Scheme
This paper proposes a deep separate source-channel coding (DSSCC) framework for the joint task and data oriented semantic communications (JTD-SC) and utilizes the variational autoencoder approach to solve the rate-distortion problem with semantic distortion.
Entity-Level Text-Guided Image Manipulation
In the image manipulation phase, SeMani adopts a generative model to synthesize new images conditioned on the entity-irrelevant regions and target text descriptions.
Simulation-to-reality UAV Fault Diagnosis with Deep Learning
Accurate diagnosis of propeller faults is crucial for ensuring the safe and efficient operation of quadrotors.
RIS-Position and Orientation Estimation in MIMO-OFDM Systems with Practical Scatterers
A two-step positioning scheme is exploited, where the channel parameters are first acquired, and the position-related parameters are then estimated.
Language-Driven Anchors for Zero-Shot Adversarial Robustness
Previous researches mainly focus on improving adversarial robustness in the fully supervised setting, leaving the challenging domain of zero-shot adversarial robustness an open question.
Deep-learning-based on-chip rapid spectral imaging with high spatial resolution
Spectral imaging extends the concept of traditional color cameras to capture images across multiple spectral channels and has broad application prospects.
EPR-Net: Constructing non-equilibrium potential landscape via a variational force projection formulation
We present EPR-Net, a novel and effective deep learning approach that tackles a crucial challenge in biophysics: constructing potential landscapes for high-dimensional non-equilibrium steady-state (NESS) systems.
Machine Learning for Large-Scale Optimization in 6G Wireless Networks
The sixth generation (6G) wireless systems are envisioned to enable the paradigm shift from "connected things" to "connected intelligence", featured by ultra high density, large-scale, dynamic heterogeneity, diversified functional requirements and machine learning capabilities, which leads to a growing need for highly efficient intelligent algorithms.
CFCG: Semi-Supervised Semantic Segmentation via Cross-Fusion and Contour Guidance Supervision
Current state-of-the-art semi-supervised semantic segmentation (SSSS) methods typically adopt pseudo labeling and consistency regularization between multiple learners with different perturbations.
Translating Images to Road Network: A Non-Autoregressive Sequence-to-Sequence Approach
The extraction of road network is essential for the generation of high-definition maps since it enables the precise localization of road landmarks and their interconnections.
Data-free Knowledge Distillation for Fine-grained Visual Categorization
Our approach utilizes an adversarial distillation framework with attention generator, mixed high-order attention distillation, and semantic feature contrast learning.
Data-free Knowledge Distillation
Fine-Grained Visual Categorization
+1
WaterMask: Instance Segmentation for Underwater Imagery
Underwater image instance segmentation is a fundamental and critical step in underwater image analysis and understanding.
Ranked #1 on
Instance Segmentation
on UIIS
A Deep Learning Method for Real-time Bias Correction of Wind Field Forecasts in the Western North Pacific
In this study, we developed the Multi-Task-Double Encoder Trajectory Gated Recurrent Unit (MT-DETrajGRU) model, which uses an improved double-encoder forecaster architecture to model the spatiotemporal sequence of the U and V components of the wind field; we designed a multi-task learning loss function to correct wind speed and wind direction simultaneously using only one model.
Circular Accessible Depth: A Robust Traversability Representation for UGV Navigation
In this paper, we present the Circular Accessible Depth (CAD), a robust traversability representation for an unmanned ground vehicle (UGV) to learn traversability in various scenarios containing irregular obstacles.
Semantic optical fiber communication system
The current optical communication systems minimize bit or symbol errors without considering the semantic meaning behind digital bits, thus transmitting a lot of unnecessary information.
Differentiating Student Feedbacks for Knowledge Tracing
In computer-aided education and intelligent tutoring systems, knowledge tracing (KT) raises attention due to the development of data-driven learning methods, which aims to predict students' future performance given their past question response sequences to trace their knowledge states.
Adaptive Low-Precision Training for Embeddings in Click-Through Rate Prediction
To this end, we formulate a novel quantization training paradigm to compress the embeddings from the training stage, termed low-precision training (LPT).
Low-rank Tensor Assisted K-space Generative Model for Parallel Imaging Reconstruction
Although recent deep learning methods, especially generative models, have shown good performance in fast magnetic resonance imaging, there is still much room for improvement in high-dimensional generation.
Matrix Profile XXVII: A Novel Distance Measure for Comparing Long Time Series
PRCIS is a distance measure for long time series, which exploits recent progress in our ability to summarize time series with dictionaries.
G-MAP: General Memory-Augmented Pre-trained Language Model for Domain Tasks
Recently, domain-specific PLMs have been proposed to boost the task performance of specific domains (e. g., biomedical and computer science) by continuing to pre-train general PLMs with domain-specific corpora.
Dynamic Graph Node Classification via Time Augmentation
Node classification on dynamic graphs is challenging for two reasons.
FlowFace: Semantic Flow-guided Shape-aware Face Swapping
Unlike most previous methods that focus on transferring the source inner facial features but neglect facial contours, our FlowFace can transfer both of them to a target face, thus leading to more realistic face swapping.
Quantized Wasserstein Procrustes Alignment of Word Embedding Spaces
Optimal Transport (OT) provides a useful geometric framework to estimate the permutation matrix under unsupervised cross-lingual word embedding (CLWE) models that pose the alignment task as a Wasserstein-Procrustes problem.
3D-TOGO: Towards Text-Guided Cross-Category 3D Object Generation
Here, we make the first attempt to achieve generic text-guided cross-category 3D object generation via a new 3D-TOGO model, which integrates a text-to-views generation module and a views-to-3D generation module.
Global Meets Local: Effective Multi-Label Image Classification via Category-Aware Weak Supervision
This paper builds a unified framework to perform effective noisy-proposal suppression and to interact between global and local features for robust feature learning.
RIS-Assisted Self-Interference Mitigation for In-Band Full-Duplex Transceivers
The wireless in-band full-duplex (IBFD) technology can in theory double the system capacity over the conventional frequency division duplex (FDD) or time-division duplex (TDD) alternatives.
LVOS: A Benchmark for Long-term Video Object Segmentation
The videos in our LVOS last 1. 59 minutes on average, which is 20 times longer than videos in existing VOS datasets.
Leveraging the Hints: Adaptive Bidding in Repeated First-Price Auctions
In the past four years, a particularly important development in the digital advertising industry is the shift from second-price auctions to first-price auctions for online display ads.
Rethinking the Metric in Few-shot Learning: From an Adaptive Multi-Distance Perspective
In this paper, for the first time, we investigate the contributions of different distance metrics, and propose an adaptive fusion scheme, bringing significant improvements in few-shot classification.
Facial Action Unit Detection and Intensity Estimation from Self-supervised Representation
As a fine-grained and local expression behavior measurement, facial action unit (FAU) analysis (e. g., detection and intensity estimation) has been documented for its time-consuming, labor-intensive, and error-prone annotation.
Global-to-local Expression-aware Embeddings for Facial Action Unit Detection
Then, AU feature maps and their corresponding AU masks are multiplied to generate AU masked features focusing on local facial region.
Facial Action Units Detection Aided by Global-Local Expression Embedding
We find that AUs and facial expressions are highly associated, and existing facial expression datasets often contain a large number of identities.
Learning Point-Language Hierarchical Alignment for 3D Visual Grounding
This paper presents a novel hierarchical alignment model (HAM) that learns multi-granularity visual and linguistic representations in an end-to-end manner.
Slippage-robust Gaze Tracking for Near-eye Display
In recent years, head-mounted near-eye display devices have become the key hardware foundation for virtual reality and augmented reality.
ISTA-Inspired Network for Image Super-Resolution
Inspired by the mathematical analysis, the ISTA block is developed to conduct the optimization in an end-to-end manner.
Repainting and Imitating Learning for Lane Detection
In this paper, we target at finding an enhanced feature space where the lane features are distinctive while maintaining a similar distribution of lanes in the wild.
SoccerNet 2022 Challenges Results
The SoccerNet 2022 challenges were the second annual video understanding challenges organized by the SoccerNet team.
Collaboration of Pre-trained Models Makes Better Few-shot Learner
In this paper, we propose CoMo, a Collaboration of pre-trained Models that incorporates diverse prior knowledge from various pre-training paradigms for better few-shot learning.
DetCLIP: Dictionary-Enriched Visual-Concept Paralleled Pre-training for Open-world Detection
We further design a concept dictionary~(with descriptions) from various online sources and detection datasets to provide prior knowledge for each concept.
Provably Uncertainty-Guided Universal Domain Adaptation
It fully utilizes the relationship between a target sample and its neighbors in the source domain to avoid the influence of domain misalignment.
SENDER: SEmi-Nonlinear Deep Efficient Reconstructor for Extraction Canonical, Meta, and Sub Functional Connectivity in the Human Brain
Deep Linear and Nonlinear learning methods have already been vital machine learning methods for investigating the hierarchical features such as functional connectivity in the human brain via functional Magnetic Resonance signals; however, there are three major shortcomings: 1).
A detail-enhanced sampling strategy in Hadamard single-pixel imaging
Hadamard single-pixel imaging (HSI) is an appealing imaging technique due to its features of low hardware complexity and industrial cost.
Dual Representation Learning for One-Step Clustering of Multi-View Data
Meanwhile, to make the representation learning more specific to the clustering task, a one-step learning framework is proposed to integrate representation learning and clustering partition as a whole.
A Circular Window-based Cascade Transformer for Online Action Detection
In this paper, we advocate a novel and efficient principle for online action detection.
Robustness to Unbounded Smoothness of Generalized SignSGD
We also compare these algorithms with popular optimizers on a set of deep learning tasks, observing that we can match the performance of Adam while beating the others.
