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Found 72 papers, 26 papers with code
NTIRE 2024 Challenge on Image Super-Resolution ($\times$4): Methods and Results
This paper reviews the NTIRE 2024 challenge on image super-resolution ($\times$4), highlighting the solutions proposed and the outcomes obtained.
Empowering Segmentation Ability to Multi-modal Large Language Models
Multi-modal large language models (MLLMs) can understand image-language prompts and demonstrate impressive reasoning ability.
CasSR: Activating Image Power for Real-World Image Super-Resolution
In particular, we develop a cascaded controllable diffusion model that aims to optimize the extraction of information from low-resolution images.
XPSR: Cross-modal Priors for Diffusion-based Image Super-Resolution
Diffusion-based methods, endowed with a formidable generative prior, have received increasing attention in Image Super-Resolution (ISR) recently.
Uncertainty-driven and Adversarial Calibration Learning for Epicardial Adipose Tissue Segmentation
we propose a novel feature latent space multilevel supervision network (SPDNet) with uncertainty-driven and adversarial calibration learning to enhance segmentation for more accurate EAT volume estimation.
Uni-RLHF: Universal Platform and Benchmark Suite for Reinforcement Learning with Diverse Human Feedback
It is crucial to consider diverse human feedback types and various learning methods in different environments.
Coupling Graph Neural Networks with Fractional Order Continuous Dynamics: A Robustness Study
In this work, we rigorously investigate the robustness of graph neural fractional-order differential equation (FDE) models.
PosDiffNet: Positional Neural Diffusion for Point Cloud Registration in a Large Field of View with Perturbations
We leverage a graph neural partial differential equation (PDE) based on Beltrami flow to obtain high-dimensional features and position embeddings for point clouds.
Gemini: A Family of Highly Capable Multimodal Models
This report introduces a new family of multimodal models, Gemini, that exhibit remarkable capabilities across image, audio, video, and text understanding.
Ranked #1 on
Multi-task Language Understanding
on MMLU
(using extra training data)
DistilVPR: Cross-Modal Knowledge Distillation for Visual Place Recognition
The utilization of multi-modal sensor data in visual place recognition (VPR) has demonstrated enhanced performance compared to single-modal counterparts.
Urban Region Embedding via Multi-View Contrastive Prediction
Recently, learning urban region representations utilizing multi-modal data (information views) has become increasingly popular, for deep understanding of the distributions of various socioeconomic features in cities.
Multi-Defendant Legal Judgment Prediction via Hierarchical Reasoning
Multiple defendants in a criminal fact description generally exhibit complex interactions, and cannot be well handled by existing Legal Judgment Prediction (LJP) methods which focus on predicting judgment results (e. g., law articles, charges, and terms of penalty) for single-defendant cases.
CSAM: A 2.5D Cross-Slice Attention Module for Anisotropic Volumetric Medical Image Segmentation
Both 3D and purely 2D deep learning-based segmentation methods are deficient in dealing with such volumetric data since the performance of 3D methods suffers when confronting anisotropic data, and 2D methods disregard crucial volumetric information.
RobustMat: Neural Diffusion for Street Landmark Patch Matching under Challenging Environments
For autonomous vehicles (AVs), visual perception techniques based on sensors like cameras play crucial roles in information acquisition and processing.
Optimization Landscape of Policy Gradient Methods for Discrete-time Static Output Feedback
Despite the absence of convexity, we leverage these properties to derive novel findings regarding convergence (and nearly dimension-free rate) to stationary points for three policy gradient methods, including the vanilla policy gradient method, the natural policy gradient method, and the Gauss-Newton method.
Adversarial Robustness in Graph Neural Networks: A Hamiltonian Approach
Graph neural networks (GNNs) are vulnerable to adversarial perturbations, including those that affect both node features and graph topology.
SRN-SZ: Deep Leaning-Based Scientific Error-bounded Lossy Compression with Super-resolution Neural Networks
The fast growth of computational power and scales of modern super-computing systems have raised great challenges for the management of exascale scientific data.
RPG-Palm: Realistic Pseudo-data Generation for Palmprint Recognition
Palmprint recently shows great potential in recognition applications as it is a privacy-friendly and stable biometric.
PartDiff: Image Super-resolution with Partial Diffusion Models
This observation inspired us to propose the Partial Diffusion Model (PartDiff), which diffuses the image to an intermediate latent state instead of pure random noise, where the intermediate latent state is approximated by the latent of diffusing the low-resolution image.
NTIRE 2023 Quality Assessment of Video Enhancement Challenge
61 participating teams submitted their prediction results during the development phase, with a total of 3168 submissions.
Improving Offline-to-Online Reinforcement Learning with Q-Ensembles
Offline reinforcement learning (RL) is a learning paradigm where an agent learns from a fixed dataset of experience.
Node Embedding from Neural Hamiltonian Orbits in Graph Neural Networks
In the graph node embedding problem, embedding spaces can vary significantly for different data types, leading to the need for different GNN model types.
Graph Neural Convection-Diffusion with Heterophily
Graph neural networks (GNNs) have shown promising results across various graph learning tasks, but they often assume homophily, which can result in poor performance on heterophilic graphs.
Iteratively Learning Representations for Unseen Entities with Inter-Rule Correlations
Recent work on knowledge graph completion (KGC) focused on learning embeddings of entities and relations in knowledge graphs.
Leveraging Label Non-Uniformity for Node Classification in Graph Neural Networks
We introduce the key notion of label non-uniformity, which is derived from the Wasserstein distance between the softmax distribution of the logits and the uniform distribution.
Zoom-VQA: Patches, Frames and Clips Integration for Video Quality Assessment
Video quality assessment (VQA) aims to simulate the human perception of video quality, which is influenced by factors ranging from low-level color and texture details to high-level semantic content.
Distributional Signals for Node Classification in Graph Neural Networks
In graph neural networks (GNNs), both node features and labels are examples of graph signals, a key notion in graph signal processing (GSP).
HypLiLoc: Towards Effective LiDAR Pose Regression with Hyperbolic Fusion
LiDAR relocalization plays a crucial role in many fields, including robotics, autonomous driving, and computer vision.
Node Embedding from Hamiltonian Information Propagation in Graph Neural Networks
Graph neural networks (GNNs) have achieved success in various inference tasks on graph-structured data.
Quality-aware Pre-trained Models for Blind Image Quality Assessment
Blind image quality assessment (BIQA) aims to automatically evaluate the perceived quality of a single image, whose performance has been improved by deep learning-based methods in recent years.
A Pattern Discovery Approach to Multivariate Time Series Forecasting
To capture the temporal and multivariate correlations among subsequences, we design a pattern discovery model, that constructs correlations via diverse pattern functions.
Adaptive Control with Global Exponential Stability for Parameter-Varying Nonlinear Systems under Unknown Control Gains
It is nontrivial to achieve exponential stability even for time-invariant nonlinear systems with matched uncertainties and persistent excitation (PE) condition.
Learning an Efficient Multimodal Depth Completion Model
With the wide application of sparse ToF sensors in mobile devices, RGB image-guided sparse depth completion has attracted extensive attention recently, but still faces some problems.
Fractional Vegetation Cover Estimation using Hough Lines and Linear Iterative Clustering
This paper presents a new image processing algorithm to determine the amount of vegetation cover present in a given area, called fractional vegetation cover.
Video Polyp Segmentation: A Deep Learning Perspective
We present the first comprehensive video polyp segmentation (VPS) study in the deep learning era.
Ranked #2 on
Video Polyp Segmentation
on SUN-SEG-Easy (Unseen)
ContrastMask: Contrastive Learning to Segment Every Thing
In this framework, annotated masks of seen categories and pseudo masks of unseen categories serve as a prior for contrastive learning, where features from the mask regions (foreground) are pulled together, and are contrasted against those from the background, and vice versa.
Geometric Synthesis: A Free lunch for Large-scale Palmprint Recognition Model Pretraining
In this paper, by observing that palmar creases are the key information to deep-learning-based palmprint recognition, we propose to synthesize training data by manipulating palmar creases.
Multi-domain Collaborative Feature Representation for Robust Visual Object Tracking
Jointly exploiting multiple different yet complementary domain information has been proven to be an effective way to perform robust object tracking.
Adaptive Feature Alignment for Adversarial Training
Based on this observation, we propose the adaptive feature alignment (AFA) to generate features of arbitrary attacking strengths.
Exploring Autoencoder-based Error-bounded Compression for Scientific Data
(1) We provide an in-depth investigation of the characteristics of various autoencoder models and develop an error-bounded autoencoder-based framework in terms of the SZ model.
FakeMix Augmentation Improves Transparent Object Detection
However, these methods usually encounter boundary-related imbalance problem, leading to limited generation capability.
cuSZ: An Efficient GPU-Based Error-Bounded Lossy Compression Framework for Scientific Data
To the best of our knowledge, cuSZ is the first error-bounded lossy compressor on GPUs for scientific data.
Distributed, Parallel, and Cluster Computing
Dependency Aware Filter Pruning
Convolutional neural networks (CNNs) are typically over-parameterized, bringing considerable computational overhead and memory footprint in inference.
FT-CNN: Algorithm-Based Fault Tolerance for Convolutional Neural Networks
(1) We propose several systematic ABFT schemes based on checksum techniques and analyze their fault protection ability and runtime thoroughly. Unlike traditional ABFT based on matrix-matrix multiplication, our schemes support any convolution implementations.
Deep Hough Transform for Semantic Line Detection
In addition to the proposed method, we design an evaluation metric to assess the quality of line detection and construct a large scale dataset for the line detection task.
Ranked #2 on
Line Detection
on NKL
Structured Sparsification with Joint Optimization of Group Convolution and Channel Shuffle
Recent advances in convolutional neural networks(CNNs) usually come with the expense of excessive computational overhead and memory footprint.
Deep Differentiable Random Forests for Age Estimation
Both of them connect split nodes to the top layer of convolutional neural networks (CNNs) and deal with inhomogeneous data by jointly learning input-dependent data partitions at the split nodes and age distributions at the leaf nodes.
RegularFace: Deep Face Recognition via Exclusive Regularization
We consider the face recognition task where facial images of the same identity (person) is expected to be closer in the representation space, while different identities be far apart.
Translate-to-Recognize Networks for RGB-D Scene Recognition
Empirically, we verify that this new semi-supervised setting is able to further enhance the performance of recognition network.
Res2Net: A New Multi-scale Backbone Architecture
We evaluate the Res2Net block on all these models and demonstrate consistent performance gains over baseline models on widely-used datasets, e. g., CIFAR-100 and ImageNet.
Ranked #2 on
Image Classification
on GasHisSDB
When to Finish? Optimal Beam Search for Neural Text Generation (modulo beam size)
In neural text generation such as neural machine translation, summarization, and image captioning, beam search is widely used to improve the output text quality.
Hifi: Hierarchical feature integration for skeleton detection
In natural images, the scales (thickness) of object skeletons may dramatically vary among objects and object parts.
Optimizing the F-measure for Threshold-free Salient Object Detection
By reformulating the standard F-measure we propose the relaxed F-measure which is differentiable w. r. t the posterior and can be easily appended to the back of CNNs as the loss function.
Hi-Fi: Hierarchical Feature Integration for Skeleton Detection
In natural images, the scales (thickness) of object skeletons may dramatically vary among objects and object parts, making object skeleton detection a challenging problem.
Ranked #2 on
Object Skeleton Detection
on SK-LARGE
Deep Regression Forests for Age Estimation
Age estimation from facial images is typically cast as a nonlinear regression problem.
Ranked #6 on
Age Estimation
on FGNET
OSU Multimodal Machine Translation System Report
This paper describes Oregon State University's submissions to the shared WMT'17 task "multimodal translation task I".
Jointly Trained Sequential Labeling and Classification by Sparse Attention Neural Networks
In order to utilize the potential benefits from their correlations, we propose a jointly trained model for learning the two tasks simultaneously via Long Short-Term Memory (LSTM) networks.
Joint Syntacto-Discourse Parsing and the Syntacto-Discourse Treebank
Discourse parsing has long been treated as a stand-alone problem independent from constituency or dependency parsing.
Label Distribution Learning Forests
This paper presents label distribution learning forests (LDLFs) - a novel label distribution learning algorithm based on differentiable decision trees, which have several advantages: 1) Decision trees have the potential to model any general form of label distributions by a mixture of leaf node predictions.
Ranked #11 on
Age Estimation
on MORPH album2 (Caucasian)
Textual Entailment with Structured Attentions and Composition
We show that it is beneficial to extend the attention model to tree nodes between premise and hypothesis.
DeepSkeleton: Learning Multi-task Scale-associated Deep Side Outputs for Object Skeleton Extraction in Natural Images
By observing the relationship between the receptive field sizes of the different layers in the network and the skeleton scales they can capture, we introduce two scale-associated side outputs to each stage of the network.
Object Skeleton Extraction in Natural Images by Fusing Scale-associated Deep Side Outputs
Object skeleton is a useful cue for object detection, complementary to the object contour, as it provides a structural representation to describe the relationship among object parts.
Type-Driven Incremental Semantic Parsing with Polymorphism
Semantic parsing has made significant progress, but most current semantic parsers are extremely slow (CKY-based) and rather primitive in representation.
Ranked #4 on
Semantic Parsing
on ATIS
