Search Results for author:
Found 89 papers, 43 papers with code
Wavelet-Based Dual-Branch Network for Image Demoiréing
When smartphone cameras are used to take photos of digital screens, usually moire patterns result, severely degrading photo quality.
Focus on Your Target: A Dual Teacher-Student Framework for Domain-adaptive Semantic Segmentation
Currently, a popular UDA framework lies in self-training which endows the model with two-fold abilities: (i) learning reliable semantics from the labeled images in the source domain, and (ii) adapting to the target domain via generating pseudo labels on the unlabeled images.
DIRE for Diffusion-Generated Image Detection
We find that existing detectors struggle to detect images generated by diffusion models, even if we include generated images from a specific diffusion model in their training data.
BEST: BERT Pre-Training for Sign Language Recognition with Coupling Tokenization
In this work, we are dedicated to leveraging the BERT pre-training success and modeling the domain-specific statistics to fertilize the sign language recognition~(SLR) model.
Recurrent Contour-based Instance Segmentation with Progressive Learning
In this work, we propose a novel deep network architecture, i. e., PolySnake, for contour-based instance segmentation.
Ranked #1 on
Semantic Contour Prediction
on Sbd val
CLIP2GAN: Towards Bridging Text with the Latent Space of GANs
The key idea of our CLIP2GAN is to bridge the output feature embedding space of CLIP and the input latent space of StyleGAN, which is realized by introducing a mapping network.
Hand-Object Interaction Image Generation
In this work, we are dedicated to a new task, i. e., hand-object interaction image generation, which aims to conditionally generate the hand-object image under the given hand, object and their interaction status.
SinDiffusion: Learning a Diffusion Model from a Single Natural Image
We present SinDiffusion, leveraging denoising diffusion models to capture internal distribution of patches from a single natural image.
Ranked #1 on
Image Generation
on Places50
DanZero: Mastering GuanDan Game with Reinforcement Learning
We compare it with 8 baseline AI programs which are based on heuristic rules and the results reveal the outstanding performance of DanZero.
Fine-grained Semantic Alignment Network for Weakly Supervised Temporal Language Grounding
In this work, we propose a novel candidate-free framework: Fine-grained Semantic Alignment Network (FSAN), for weakly supervised TLG.
UDoc-GAN: Unpaired Document Illumination Correction with Background Light Prior
To this end, we propose UDoc-GAN, the first framework to address the problem of document illumination correction under the unpaired setting.
Geometric Representation Learning for Document Image Rectification
In document image rectification, there exist rich geometric constraints between the distorted image and the ground truth one.
CMD: Self-supervised 3D Action Representation Learning with Cross-modal Mutual Distillation
In this work, we formulate the cross-modal interaction as a bidirectional knowledge distillation problem.
Low-Light Video Enhancement with Synthetic Event Guidance
Low-light video enhancement (LLVE) is an important yet challenging task with many applications such as photographing and autonomous driving.
Unified 2D and 3D Pre-Training of Molecular Representations
The model is pre-trained on three tasks: reconstruction of masked atoms and coordinates, 3D conformation generation conditioned on 2D graph, and 2D graph generation conditioned on 3D conformation.
Semantic Image Synthesis via Diffusion Models
Denoising Diffusion Probabilistic Models (DDPMs) have achieved remarkable success in various image generation tasks compared with Generative Adversarial Nets (GANs).
TransVG++: End-to-End Visual Grounding with Language Conditioned Vision Transformer
For another, we devise Language Conditioned Vision Transformer that removes external fusion modules and reuses the uni-modal ViT for vision-language fusion at the intermediate layers.
Stabilizing Voltage in Power Distribution Networks via Multi-Agent Reinforcement Learning with Transformer
Utilizing MARL algorithms to coordinate multiple control units in the grid, which is able to handle rapid changes of power systems, has been widely studied in active voltage control task recently.
Multi-agent Reinforcement Learning
reinforcement-learning
+2
Simultaneous Double Q-learning with Conservative Advantage Learning for Actor-Critic Methods
Actor-critic Reinforcement Learning (RL) algorithms have achieved impressive performance in continuous control tasks.
Multi-Target Active Object Tracking with Monte Carlo Tree Search and Target Motion Modeling
In this work, we are dedicated to multi-target active object tracking (AOT), where there are multiple targets as well as multiple cameras in the environment.
LDSA: Learning Dynamic Subtask Assignment in Cooperative Multi-Agent Reinforcement Learning
In this way, agents dealing with the same subtask share their learning of specific abilities and different subtasks correspond to different specific abilities.
Multi-agent Reinforcement Learning
reinforcement-learning
+3
Domain-Agnostic Prior for Transfer Semantic Segmentation
Unsupervised domain adaptation (UDA) is an important topic in the computer vision community.
DouZero+: Improving DouDizhu AI by Opponent Modeling and Coach-guided Learning
Recent years have witnessed the great breakthrough of deep reinforcement learning (DRL) in various perfect and imperfect information games.
Learning Enriched Illuminants for Cross and Single Sensor Color Constancy
Color constancy aims to restore the constant colors of a scene under different illuminants.
CTDS: Centralized Teacher with Decentralized Student for Multi-Agent Reinforcement Learning
In this way, CTDS balances the full utilization of global observation during training and the feasibility of decentralized execution for online inference.
Multi-agent Reinforcement Learning
reinforcement-learning
+3
Coach-assisted Multi-Agent Reinforcement Learning Framework for Unexpected Crashed Agents
To the best of our knowledge, this work is the first to study the unexpected crashes in the multi-agent system.
Multi-agent Reinforcement Learning
reinforcement-learning
+3
TAPE: Task-Agnostic Prior Embedding for Image Restoration
In this paper, we propose a novel approach that embeds a task-agnostic prior into a transformer.
MVP: Multimodality-guided Visual Pre-training
Recently, masked image modeling (MIM) has become a promising direction for visual pre-training.
Coordinate-Aligned Multi-Camera Collaboration for Active Multi-Object Tracking
In our approach, we regard each camera as an agent and address AMOT with a multi-agent reinforcement learning solution.
MCMARL: Parameterizing Value Function via Mixture of Categorical Distributions for Multi-Agent Reinforcement Learning
Specifically, we model both individual Q-values and global Q-value with categorical distribution.
Revisiting QMIX: Discriminative Credit Assignment by Gradient Entropy Regularization
In cooperative multi-agent systems, agents jointly take actions and receive a team reward instead of individual rewards.
Direct Molecular Conformation Generation
Molecular conformation generation aims to generate three-dimensional coordinates of all the atoms in a molecule and is an important task in bioinformatics and pharmacology.
Contextual Similarity Distillation for Asymmetric Image Retrieval
To this end, we propose a flexible contextual similarity distillation framework to enhance the small query model and keep its output feature compatible with that of large gallery model, which is crucial with asymmetric retrieval.
Learning Token-based Representation for Image Retrieval
Next, a refinement block is introduced to enhance the visual tokens with self-attention and cross-attention.
Unsupervised Person Re-Identification with Wireless Positioning under Weak Scene Labeling
To this end, we propose to explore unsupervised person re-identification with both visual data and wireless positioning trajectories under weak scene labeling, in which we only need to know the locations of the cameras.
DocScanner: Robust Document Image Rectification with Progressive Learning
The iterative refinements make DocScanner converge to a robust and superior rectification performance, while the lightweight recurrent architecture ensures the running efficiency.
Contextual Similarity Aggregation with Self-attention for Visual Re-ranking
Since our re-ranking model is not directly involved with the visual feature used in the initial retrieval, it is ready to be applied to retrieval result lists obtained from various retrieval algorithms.
DocTr: Document Image Transformer for Geometric Unwarping and Illumination Correction
Specifically, DocTr consists of a geometric unwarping transformer and an illumination correction transformer.
SignBERT: Pre-Training of Hand-Model-Aware Representation for Sign Language Recognition
To validate the effectiveness of our method on SLR, we perform extensive experiments on four public benchmark datasets, i. e., NMFs-CSL, SLR500, MSASL and WLASL.
Ranked #1 on
Sign Language Recognition
on WLASL100
(using extra training data)
Multi-Agent Reinforcement Learning with Shared Resource in Inventory Management
We consider inventory management (IM) problem for a single store with a large number of SKUs (stock keeping units) in this paper, where we need to make replenishment decisions for each SKU to balance its supply and demand.
Heredity-aware Child Face Image Generation with Latent Space Disentanglement
The main idea is to disentangle the latent space of a pre-trained generation model and precisely control the face attributes of child images with clear semantics.
Instance-wise Hard Negative Example Generation for Contrastive Learning in Unpaired Image-to-Image Translation
In this paper, we uncover that the negative examples play a critical role in the performance of contrastive learning for image translation.
Joint Inductive and Transductive Learning for Video Object Segmentation
In this work, we propose to integrate transductive and inductive learning into a unified framework to exploit the complementarity between them for accurate and robust video object segmentation.
Semantic Segmentation
Semi-Supervised Video Object Segmentation
+2
From Multi-View to Hollow-3D: Hallucinated Hollow-3D R-CNN for 3D Object Detection
To this end, in this work, we regard point clouds as hollow-3D data and propose a new architecture, namely Hallucinated Hollow-3D R-CNN ($\text{H}^2$3D R-CNN), to address the problem of 3D object detection.
Weakly Supervised Temporal Adjacent Network for Language Grounding
To this end, we introduce a novel weakly supervised temporal adjacent network (WSTAN) for temporal language grounding.
ATSO: Asynchronous Teacher-Student Optimization for Semi-Supervised Image Segmentation
Semi-supervised learning is a useful tool for image segmentation, mainly due to its ability in extracting knowledge from unlabeled data to assist learning from labeled data.
Model-Aware Gesture-to-Gesture Translation
Then, a transformation flow is calculated based on the correspondence of the source and target topology map.
Dual-view Molecule Pre-training
After pre-training, we can use either the Transformer branch (this one is recommended according to empirical results), the GNN branch, or both for downstream tasks.
Uformer: A General U-Shaped Transformer for Image Restoration
Powered by these two designs, Uformer enjoys a high capability for capturing both local and global dependencies for image restoration.
Ranked #1 on
Deblurring
on RSBlur
Exploring the Diversity and Invariance in Yourself for Visual Pre-Training Task
By simply pulling the different augmented views of each image together or other novel mechanisms, they can learn much unsupervised knowledge and significantly improve the transfer performance of pre-training models.
Improving Sign Language Translation with Monolingual Data by Sign Back-Translation
Finally, the synthetic parallel data serves as a strong supplement for the end-to-end training of the encoder-decoder SLT framework.
Ranked #3 on
Sign Language Translation
on CSL-Daily
TransVG: End-to-End Visual Grounding with Transformers
In this paper, we present a neat yet effective transformer-based framework for visual grounding, namely TransVG, to address the task of grounding a language query to the corresponding region onto an image.
Ranked #12 on
Referring Expression Comprehension
on RefCOCO
Transformer Meets Tracker: Exploiting Temporal Context for Robust Visual Tracking
In video object tracking, there exist rich temporal contexts among successive frames, which have been largely overlooked in existing trackers.
Ranked #18 on
Visual Object Tracking
on LaSOT
IOT: Instance-wise Layer Reordering for Transformer Structures
Based on this observation, in this work, we break the assumption of the fixed layer order in the Transformer and introduce instance-wise layer reordering into the model structure.
Learning Deep Local Features With Multiple Dynamic Attentions for Large-Scale Image Retrieval
To this end, we propose a novel deep local feature learning architecture to simultaneously focus on multiple discriminative local patterns in an image.
Voxel R-CNN: Towards High Performance Voxel-based 3D Object Detection
In this paper, we take a slightly different viewpoint -- we find that precise positioning of raw points is not essential for high performance 3D object detection and that the coarse voxel granularity can also offer sufficient detection accuracy.
Ranked #4 on
3D Object Detection
on KITTI Cars Moderate val
Contrastive Transformation for Self-supervised Correspondence Learning
It is worth mentioning that our method also surpasses the fully-supervised affinity representation (e. g., ResNet) and performs competitively against the recent fully-supervised algorithms designed for the specific tasks (e. g., VOT and VOS).
Temporal-Channel Transformer for 3D Lidar-Based Video Object Detection in Autonomous Driving
As a special design of this transformer, the information encoded in the encoder is different from that in the decoder, i. e. the encoder encodes temporal-channel information of multiple frames while the decoder decodes the spatial-channel information for the current frame in a voxel-wise manner.
Heterogeneous Contrastive Learning: Encoding Spatial Information for Compact Visual Representations
Contrastive learning has achieved great success in self-supervised visual representation learning, but existing approaches mostly ignored spatial information which is often crucial for visual representation.
Can Semantic Labels Assist Self-Supervised Visual Representation Learning?
Recently, contrastive learning has largely advanced the progress of unsupervised visual representation learning.
Masked Contrastive Representation Learning for Reinforcement Learning
During inference, the CNN encoder and the policy network are used to take actions, and the Transformer module is discarded.
Boosting Continuous Sign Language Recognition via Cross Modality Augmentation
Continuous sign language recognition (SLR) deals with unaligned video-text pair and uses the word error rate (WER), i. e., edit distance, as the main evaluation metric.
Global-local Enhancement Network for NMFs-aware Sign Language Recognition
Sign language recognition (SLR) is a challenging problem, involving complex manual features, i. e., hand gestures, and fine-grained non-manual features (NMFs), i. e., facial expression, mouth shapes, etc.
Vision Meets Wireless Positioning: Effective Person Re-identification with Recurrent Context Propagation
Existing person re-identification methods rely on the visual sensor to capture the pedestrians.
Unsupervised Deep Representation Learning for Real-Time Tracking
The advancement of visual tracking has continuously been brought by deep learning models.
Wavelet-Based Dual-Branch Network for Image Demoireing
When smartphone cameras are used to take photos of digital screens, usually moire patterns result, severely degrading photo quality.
Single Shot Video Object Detector
Single shot detectors that are potentially faster and simpler than two-stage detectors tend to be more applicable to object detection in videos.
Cascaded Regression Tracking: Towards Online Hard Distractor Discrimination
In the second stage, a discrete sampling based ridge regression is designed to double-check the remaining ambiguous hard samples, which serves as an alternative of fully-connected layers and benefits from the closed-form solver for efficient learning.
Incorporating BERT into Neural Machine Translation
While BERT is more commonly used as fine-tuning instead of contextual embedding for downstream language understanding tasks, in NMT, our preliminary exploration of using BERT as contextual embedding is better than using for fine-tuning.
Spatial-Temporal Multi-Cue Network for Continuous Sign Language Recognition
Our STMC network consists of a spatial multi-cue (SMC) module and a temporal multi-cue (TMC) module.
Ranked #3 on
Sign Language Recognition
on RWTH-PHOENIX-Weather 2014 T
(Word Error Rate (WER) metric)
Progressive Unsupervised Person Re-identification by Tracklet Association with Spatio-Temporal Regularization
In our approach, we first collect tracklet data within each camera by automatic person detection and tracking.
An End-to-End Foreground-Aware Network for Person Re-Identification
By presenting a target attention loss, the pedestrian features extracted from the foreground branch become more insensitive to the backgrounds, which greatly reduces the negative impacts of changing backgrounds on matching an identical across different camera views.
Relation Distillation Networks for Video Object Detection
In this paper, we introduce a new design to capture the interactions across the objects in spatio-temporal context.
Real-Time Correlation Tracking via Joint Model Compression and Transfer
In the distillation process, we propose a fidelity loss to enable the student network to maintain the representation capability of the teacher network.
Online Filter Clustering and Pruning for Efficient Convnets
Pruning filters is an effective method for accelerating deep neural networks (DNNs), but most existing approaches prune filters on a pre-trained network directly which limits in acceleration.
Progressive Learning of Low-Precision Networks
Recent years have witnessed the great advance of deep learning in a variety of vision tasks.
Soft Contextual Data Augmentation for Neural Machine Translation
While data augmentation is an important trick to boost the accuracy of deep learning methods in computer vision tasks, its study in natural language tasks is still very limited.
WIDER Face and Pedestrian Challenge 2018: Methods and Results
This paper presents a review of the 2018 WIDER Challenge on Face and Pedestrian.
Spatial and Temporal Mutual Promotion for Video-based Person Re-identification
How to explore the abundant spatial-temporal information in video sequences is the key to solve this problem.
Affinity Derivation and Graph Merge for Instance Segmentation
We present an instance segmentation scheme based on pixel affinity information, which is the relationship of two pixels belonging to a same instance.
Multi-Cue Correlation Filters for Robust Visual Tracking
By combining different types of features, our approach constructs multiple experts through Discriminative Correlation Filter (DCF) and each of them tracks the target independently.
Low-Latency Human Action Recognition with Weighted Multi-Region Convolutional Neural Network
Termed "Weighted Multi-Region Convolutional Neural Network" (WMR ConvNet), the proposed system is LSTM-free, and is based on 2D ConvNet that does not require the accumulation of video frames for 3D ConvNet filtering.
Visual Attribute-augmented Three-dimensional Convolutional Neural Network for Enhanced Human Action Recognition
Visual attributes in individual video frames, such as the presence of characteristic objects and scenes, offer substantial information for action recognition in videos.
Video-based Sign Language Recognition without Temporal Segmentation
Worse still, isolated SLR methods typically require strenuous labeling of each word separately in a sentence, severely limiting the amount of attainable training data.
Recent Advance in Content-based Image Retrieval: A Literature Survey
The explosive increase and ubiquitous accessibility of visual data on the Web have led to the prosperity of research activity in image search or retrieval.
Picking Deep Filter Responses for Fine-Grained Image Recognition
Recognizing fine-grained sub-categories such as birds and dogs is extremely challenging due to the highly localized and subtle differences in some specific parts.
SOM: Semantic Obviousness Metric for Image Quality Assessment
We propose to extract two types of features, one to measure the semantic obviousness of the image and the other to discover local characteristic.
Image Quality Estimation
No-Reference Image Quality Assessment
Bayes Merging of Multiple Vocabularies for Scalable Image Retrieval
Albeit simple, Bayes merging can be well applied in various merging tasks, and consistently improves the baselines on multi-vocabulary merging.
