Search Results for author:
Found 94 papers, 43 papers with code
Edit3K: Universal Representation Learning for Video Editing Components
Each video in our dataset is rendered by various image/video materials with a single editing component, which supports atomic visual understanding of different editing components.
Tri-Perspective View Decomposition for Geometry-Aware Depth Completion
Depth completion is a vital task for autonomous driving, as it involves reconstructing the precise 3D geometry of a scene from sparse and noisy depth measurements.
Progressive Divide-and-Conquer via Subsampling Decomposition for Accelerated MRI
Starting from decomposing the original maximum-a-posteriori problem of accelerated MRI, we present a rigorous derivation of the proposed PDAC framework, which could be further unfolded into an end-to-end trainable network.
Learning to Edit: Aligning LLMs with Knowledge Editing
Knowledge editing techniques, aiming to efficiently modify a minor proportion of knowledge in large language models (LLMs) without negatively impacting performance across other inputs, have garnered widespread attention.
Make a Cheap Scaling: A Self-Cascade Diffusion Model for Higher-Resolution Adaptation
Diffusion models have proven to be highly effective in image and video generation; however, they still face composition challenges when generating images of varying sizes due to single-scale training data.
DiffTOP: Differentiable Trajectory Optimization for Deep Reinforcement and Imitation Learning
The key to our approach is to leverage the recent progress in differentiable trajectory optimization, which enables computing the gradients of the loss with respect to the parameters of trajectory optimization.
RL-VLM-F: Reinforcement Learning from Vision Language Foundation Model Feedback
Reward engineering has long been a challenge in Reinforcement Learning (RL) research, as it often requires extensive human effort and iterative processes of trial-and-error to design effective reward functions.
Let's Negotiate! A Survey of Negotiation Dialogue Systems
Negotiation is a crucial ability in human communication.
MT-Eval: A Multi-Turn Capabilities Evaluation Benchmark for Large Language Models
Large language models (LLMs) are increasingly relied upon for complex multi-turn conversations across diverse real-world applications.
YODA: Teacher-Student Progressive Learning for Language Models
With the teacher's guidance, the student learns to iteratively refine its answer with feedback, and forms a robust and comprehensive understanding of the posed questions.
Importance-Aware Data Augmentation for Document-Level Neural Machine Translation
Document-level neural machine translation (DocNMT) aims to generate translations that are both coherent and cohesive, in contrast to its sentence-level counterpart.
UniMS-RAG: A Unified Multi-source Retrieval-Augmented Generation for Personalized Dialogue Systems
To better plan and incorporate the use of multiple sources in generating personalized response, we firstly decompose it into three sub-tasks: Knowledge Source Selection, Knowledge Retrieval, and Response Generation.
G-LLaVA: Solving Geometric Problem with Multi-Modal Large Language Model
We first analyze the limitations of current Multimodal Large Language Models (MLLMs) in this area: they struggle to accurately comprehending basic geometric elements and their relationships.
Data Management For Large Language Models: A Survey
Data plays a fundamental role in the training of Large Language Models (LLMs).
A Survey of the Evolution of Language Model-Based Dialogue Systems
This survey delves into the historical trajectory of dialogue systems, elucidating their intricate relationship with advancements in language models by categorizing this evolution into four distinct stages, each marked by pivotal LM breakthroughs: 1) Early_Stage: characterized by statistical LMs, resulting in rule-based or machine-learning-driven dialogue_systems; 2) Independent development of TOD and ODD based on neural_language_models (NLM; e. g., LSTM and GRU), since NLMs lack intrinsic knowledge in their parameters; 3) fusion between different types of dialogue systems with the advert of pre-trained_language_models (PLMs), starting from the fusion between four_sub-tasks_within_TOD, and then TOD_with_ODD; and 4) current LLM-based_dialogue_system, wherein LLMs can be used to conduct TOD and ODD seamlessly.
SinSR: Diffusion-Based Image Super-Resolution in a Single Step
Extensive experiments conducted on synthetic and real-world datasets demonstrate that the proposed method can achieve comparable or even superior performance compared to both previous SOTA methods and the teacher model, in just one sampling step, resulting in a remarkable up to x10 speedup for inference.
RoboGen: Towards Unleashing Infinite Data for Automated Robot Learning via Generative Simulation
We present RoboGen, a generative robotic agent that automatically learns diverse robotic skills at scale via generative simulation.
FollowBench: A Multi-level Fine-grained Constraints Following Benchmark for Large Language Models
To fill this research gap, in this paper, we propose FollowBench, a Multi-level Fine-grained Constraints Following Benchmark for LLMs.
M4LE: A Multi-Ability Multi-Range Multi-Task Multi-Domain Long-Context Evaluation Benchmark for Large Language Models
In this paper, we propose M4LE, a Multi-ability, Multi-range, Multi-task, Multi-domain benchmark for Long-context Evaluation.
LRRU: Long-short Range Recurrent Updating Networks for Depth Completion
Existing deep learning-based depth completion methods generally employ massive stacked layers to predict the dense depth map from sparse input data.
SELF: Self-Evolution with Language Feedback
SELF initiates with a meta-skill learning process that equips the LLMs with capabilities for self-feedback and self-refinement.
Does My Dog ''Speak'' Like Me? The Acoustic Correlation between Pet Dogs and Their Human Owners
How hosts language influence their pets' vocalization is an interesting yet underexplored problem.
Towards Lexical Analysis of Dog Vocalizations via Online Videos
This study presents a data-driven investigation into the semantics of dog vocalizations via correlating different sound types with consistent semantics.
When Large Language Models Meet Citation: A Survey
Such information could be incorporated into LLMs pre-training and improve the text representation in LLMs.
Decomposed Guided Dynamic Filters for Efficient RGB-Guided Depth Completion
The decomposed filters not only maintain the favorable properties of guided dynamic filters as being content-dependent and spatially-variant, but also reduce model parameters and hardware costs, as the learned adaptors are decoupled with the number of feature channels.
A Dual-Direction Attention Mixed Feature Network for Facial Expression Recognition
In recent years, facial expression recognition (FER) has garnered significant attention within the realm of computer vision research.
Ranked #1 on
Facial Expression Recognition (FER)
on AffectNet
Facial Expression Recognition
Facial Expression Recognition (FER)
+1
Investigating the Learning Behaviour of In-context Learning: A Comparison with Supervised Learning
Large language models (LLMs) have shown remarkable capacity for in-context learning (ICL), where learning a new task from just a few training examples is done without being explicitly pre-trained.
Aligning Large Language Models with Human: A Survey
(2) Training methodologies: a detailed review of the prevailing training methods employed for LLM alignment.
ExposureDiffusion: Learning to Expose for Low-light Image Enhancement
Different from a vanilla diffusion model that has to perform Gaussian denoising, with the injected physics-based exposure model, our restoration process can directly start from a noisy image instead of pure noise.
Ranked #1 on
Image Denoising
on Image Denoising on SID x300
Separate-and-Aggregate: A Transformer-based Patch Refinement Model for Knowledge Graph Completion
Previous KGC methods typically represent knowledge graph entities and relations as trainable continuous embeddings and fuse the embeddings of the entity $h$ (or $t$) and relation $r$ into hidden representations of query $(h, r, ?
Enhancing Low-Light Images Using Infrared-Encoded Images
In this work, we propose a novel approach to increase the visibility of images captured under low-light environments by removing the in-camera infrared (IR) cut-off filter, which allows for the capture of more photons and results in improved signal-to-noise ratio due to the inclusion of information from the IR spectrum.
Dipping PLMs Sauce: Bridging Structure and Text for Effective Knowledge Graph Completion via Conditional Soft Prompting
However, the fine-tuned PLMs often overwhelmingly focus on the textual information and overlook structural knowledge.
Beyond Learned Metadata-based Raw Image Reconstruction
Besides, we propose a novel design of the context model, which can better predict the order masks of encoding/decoding based on both the sRGB image and the masks of already processed features.
Unifying Large Language Models and Knowledge Graphs: A Roadmap
In this article, we present a forward-looking roadmap for the unification of LLMs and KGs.
Source Code Data Augmentation for Deep Learning: A Survey
This paper fills this gap by conducting a comprehensive and integrative survey of data augmentation for source code, wherein we systematically compile and encapsulate existing literature to provide a comprehensive overview of the field.
Turning Flowchart into Dialog: Augmenting Flowchart-grounded Troubleshooting Dialogs via Synthetic Data Generation
Flowchart-grounded troubleshooting dialogue (FTD) systems, which follow the instructions of a flowchart to diagnose users' problems in specific domains (e. g., vehicle, laptop), have been gaining research interest in recent years.
SocialDial: A Benchmark for Socially-Aware Dialogue Systems
To the best of our knowledge, SocialDial is the first socially-aware dialogue dataset that covers multiple social factors and has fine-grained labels.
Cultural Vocal Bursts Intensity Prediction
Synthetic Data Generation
The Second Monocular Depth Estimation Challenge
This paper discusses the results for the second edition of the Monocular Depth Estimation Challenge (MDEC).
Text with Knowledge Graph Augmented Transformer for Video Captioning
Meanwhile, the internal stream is designed to exploit the multi-modality information in videos (e. g., the appearance of video frames, speech transcripts, and video captions) to ensure the quality of caption results.
Ranked #7 on
Video Captioning
on YouCook2
Backdoor Attacks Against Deep Image Compression via Adaptive Frequency Trigger
Extensive experiments show that with our trained trigger injection models and simple modification of encoder parameters (of the compression model), the proposed attack can successfully inject several backdoors with corresponding triggers in a single image compression model.
Temporal Coherent Test-Time Optimization for Robust Video Classification
To exploit information in video with self-supervised learning, TeCo uses global content from video clips and optimizes models for entropy minimization.
Raw Image Reconstruction with Learned Compact Metadata
While raw images exhibit advantages over sRGB images (e. g., linearity and fine-grained quantization level), they are not widely used by common users due to the large storage requirements.
Evaluating the Efficacy of Skincare Product: A Realistic Short-Term Facial Pore Simulation
In this paper, we propose the first simulation model to reveal facial pore changes after using skincare products.
Removing Image Artifacts From Scratched Lens Protectors
Removing image artifacts from the scratched lens protector is inherently challenging due to the occasional flare artifacts and the co-occurring interference within mixed artifacts.
Boundary-Aware Divide and Conquer: A Diffusion-Based Solution for Unsupervised Shadow Removal
Recent deep learning methods have achieved superior results in shadow removal.
Let's Negotiate! A Survey of Negotiation Dialogue Systems
Negotiation is one of the crucial abilities in human communication, and there has been a resurgent research interest in negotiation dialogue systems recently, which goal is to empower intelligent agents with such ability that can efficiently help humans resolve conflicts or reach beneficial agreements.
ShadowDiffusion: When Degradation Prior Meets Diffusion Model for Shadow Removal
Recent deep learning methods have achieved promising results in image shadow removal.
CU-Net: LiDAR Depth-Only Completion With Coupled U-Net
We find that existing depth-only methods can obtain satisfactory results in the areas where the measurement points are almost accurate and evenly distributed (denoted as normal areas), while the performance is limited in the areas where the foreground and background points are overlapped due to occlusion (denoted as overlap areas) and the areas where there are no measurement points around (denoted as blank areas) since the methods have no reliable input information in these areas.
Knowledge Is Flat: A Seq2Seq Generative Framework for Various Knowledge Graph Completion
To remedy the KG structure information loss from the "flat" text, we further improve the input representations of entities and relations, and the inference algorithm in KG-S2S.
Visual Haptic Reasoning: Estimating Contact Forces by Observing Deformable Object Interactions
Robotic manipulation of highly deformable cloth presents a promising opportunity to assist people with several daily tasks, such as washing dishes; folding laundry; or dressing, bathing, and hygiene assistance for individuals with severe motor impairments.
Dual-Stream Transformer for Generic Event Boundary Captioning
This paper describes our champion solution for the CVPR2022 Generic Event Boundary Captioning (GEBC) competition.
SC-Transformer++: Structured Context Transformer for Generic Event Boundary Detection
This report presents the algorithm used in the submission of Generic Event Boundary Detection (GEBD) Challenge at CVPR 2022.
KnowDA: All-in-One Knowledge Mixture Model for Data Augmentation in Low-Resource NLP
This paper focuses on the data augmentation for low-resource NLP tasks where the training set is limited.
Structured Context Transformer for Generic Event Boundary Detection
To capture temporal context information of each frame, we design the structure context transformer (SC-Transformer) by re-partitioning input frame sequence.
NTIRE 2022 Challenge on Efficient Super-Resolution: Methods and Results
The aim was to design a network for single image super-resolution that achieved improvement of efficiency measured according to several metrics including runtime, parameters, FLOPs, activations, and memory consumption while at least maintaining the PSNR of 29. 00dB on DIV2K validation set.
PromDA: Prompt-based Data Augmentation for Low-Resource NLU Tasks
This paper focuses on the Data Augmentation for low-resource Natural Language Understanding (NLU) tasks.
Attention-based Feature Decomposition-Reconstruction Network for Scene Text Detection
In this paper, we propose attention-based feature decomposition-reconstruction network for scene text detection, which utilizes contextual information and low-level feature to enhance the performance of segmentation-based text detector.
FabricFlowNet: Bimanual Cloth Manipulation with a Flow-based Policy
We introduce FabricFlowNet (FFN), a cloth manipulation policy that leverages flow as both an input and as an action representation to improve performance.
Disentangled Feature Representation for Few-shot Image Classification
In this work, we propose a novel Disentangled Feature Representation framework, dubbed DFR, for few-shot learning applications.
Variational Disentanglement for Domain Generalization
Domain generalization aims to learn an invariant model that can generalize well to the unseen target domain.
Low-Light Image Enhancement with Normalizing Flow
To enhance low-light images to normally-exposed ones is highly ill-posed, namely that the mapping relationship between them is one-to-many.
Ranked #3 on
Low-Light Image Enhancement
on Sony-Total-Dark
Mention Flags (MF): Constraining Transformer-based Text Generators
In this paper, we propose Mention Flags (MF), which traces whether lexical constraints are satisfied in the generated outputs in an S2S decoder.
Neural Rule-Execution Tracking Machine For Transformer-Based Text Generation
Sequence-to-Sequence (S2S) neural text generation models, especially the pre-trained ones (e. g., BART and T5), have exhibited compelling performance on various natural language generation tasks.
A Deep Learning-based Quality Assessment and Segmentation System with a Large-scale Benchmark Dataset for Optical Coherence Tomographic Angiography Image
The large-scale OCTA dataset is available at https://doi. org/10. 5281/zenodo. 5111975, https://doi. org/10. 5281/zenodo. 5111972.
Embracing the Dark Knowledge: Domain Generalization Using Regularized Knowledge Distillation
Though convolutional neural networks are widely used in different tasks, lack of generalization capability in the absence of sufficient and representative data is one of the challenges that hinder their practical application.
Learning Visible Connectivity Dynamics for Cloth Smoothing
Robotic manipulation of cloth remains challenging for robotics due to the complex dynamics of the cloth, lack of a low-dimensional state representation, and self-occlusions.
Privacy-Preserving Constrained Domain Generalization via Gradient Alignment
However, due to the issue of limited dataset availability and the strict legal and ethical requirements for patient privacy protection, the broad applications of medical imaging classification driven by DNN with large-scale training data have been largely hindered.
ECOL-R: Encouraging Copying in Novel Object Captioning with Reinforcement Learning
In this paper, we focus on this challenge and propose the ECOL-R model (Encouraging Copying of Object Labels with Reinforced Learning), a copy-augmented transformer model that is encouraged to accurately describe the novel object labels.
SoftGym: Benchmarking Deep Reinforcement Learning for Deformable Object Manipulation
Further, we evaluate a variety of algorithms on these tasks and highlight challenges for reinforcement learning algorithms, including dealing with a state representation that has a high intrinsic dimensionality and is partially observable.
ROLL: Visual Self-Supervised Reinforcement Learning with Object Reasoning
Current image-based reinforcement learning (RL) algorithms typically operate on the whole image without performing object-level reasoning.
f-IRL: Inverse Reinforcement Learning via State Marginal Matching
Our method outperforms adversarial imitation learning methods in terms of sample efficiency and the required number of expert trajectories on IRL benchmarks.
Domain Generalization for Medical Imaging Classification with Linear-Dependency Regularization
Recently, we have witnessed great progress in the field of medical imaging classification by adopting deep neural networks.
Light Can Hack Your Face! Black-box Backdoor Attack on Face Recognition Systems
In this paper, we propose a novel black-box backdoor attack technique on face recognition systems, which can be conducted without the knowledge of the targeted DNN model.
Heterogeneous Domain Generalization via Domain Mixup
One of the main drawbacks of deep Convolutional Neural Networks (DCNN) is that they lack generalization capability.
Meta-SAC: Auto-tune the Entropy Temperature of Soft Actor-Critic via Metagradient
Our method is built upon the Soft Actor-Critic (SAC) algorithm, which uses an "entropy temperature" that balances the original task reward and the policy entropy, and hence controls the trade-off between exploitation and exploration.
Learning to Scan: A Deep Reinforcement Learning Approach for Personalized Scanning in CT Imaging
It is more desirable to design a personalized scanning strategy for each subject to obtain better reconstruction result.
A game method for improving the interpretability of convolution neural network
The reason is, we believe that: the network is essentially a perceptual model.
UniDual: A Unified Model for Image and Video Understanding
It consists of a shared 2D spatial convolution followed by two parallel point-wise convolutional layers, one devoted to images and the other one used for videos.
How to best use Syntax in Semantic Role Labelling
There are many different ways in which external information might be used in an NLP task.
Beyond Exponentially Discounted Sum: Automatic Learning of Return Function
In reinforcement learning, Return, which is the weighted accumulated future rewards, and Value, which is the expected return, serve as the objective that guides the learning of the policy.
Learning to Discretize: Solving 1D Scalar Conservation Laws via Deep Reinforcement Learning
Conservation laws are considered to be fundamental laws of nature.
Neural Constituency Parsing of Speech Transcripts
This paper studies the performance of a neural self-attentive parser on transcribed speech.
nocaps: novel object captioning at scale
To encourage the development of image captioning models that can learn visual concepts from alternative data sources, such as object detection datasets, we present the first large-scale benchmark for this task.
How to improve the interpretability of kernel learning
In this paper, a quantitative index of the interpretability is proposed and its rationality is proved, and equilibrium problem between the interpretability and the generalization performance is analyzed.
How far from automatically interpreting deep learning
In other words, there is a gap between the deep learning model and the cognitive mode.
Deep Reinforcement Learning for Green Security Games with Real-Time Information
Green Security Games (GSGs) have been proposed and applied to optimize patrols conducted by law enforcement agencies in green security domains such as combating poaching, illegal logging and overfishing.
Scenes-Objects-Actions: A Multi-Task, Multi-Label Video Dataset
The videos retrieved by the search engines are then veried for correctness by human annotators.
Concept Mask: Large-Scale Segmentation from Semantic Concepts
Then, we refine and extend the embedding network to predict an attention map, using a curated dataset with bounding box annotations on 750 concepts.
Recognizing and Curating Photo Albums via Event-Specific Image Importance
Automatic organization of personal photos is a problem with many real world ap- plications, and can be divided into two main tasks: recognizing the event type of the photo collection, and selecting interesting images from the collection.
Skeleton Key: Image Captioning by Skeleton-Attribute Decomposition
Furthermore, our algorithm can generate descriptions with varied length, benefiting from the separate control of the skeleton and attributes.
Event-Specific Image Importance
In this paper, we show that the selection of important images is consistent among different viewers, and that this selection process is related to the event type of the album.
