Search Results for author:
Found 142 papers, 63 papers with code
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.
ROCO: A Roundabout Traffic Conflict Dataset
With the traffic conflict data collected, we discover that failure to yield to circulating vehicles when entering the roundabout is the largest contributing reason for traffic conflicts.
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
OA-BEV: Bringing Object Awareness to Bird's-Eye-View Representation for Multi-Camera 3D Object Detection
To this end, we propose OA-BEV, a network that can be plugged into the BEV-based 3D object detection framework to bring out the objects by incorporating object-aware pseudo-3D features and depth features.
Multi-Agent Reinforcement Learning with Shared Resources for Inventory Management
In this paper, we consider the inventory management (IM) problem where we need to make replenishment decisions for a large number of stock keeping units (SKUs) to balance their supply and demand.
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
Stare at What You See: Masked Image Modeling without Reconstruction
However, unlike the low-level features such as pixel values, we argue the features extracted by powerful teacher models already encode rich semantic correlation across regions in an intact image. This raises one question: is reconstruction necessary in Masked Image Modeling (MIM) with a teacher model?
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.
CLIP-ViP: Adapting Pre-trained Image-Text Model to Video-Language Representation Alignment
and 2) how to mitigate the impact of these factors?
Ranked #2 on
Video Retrieval
on ActivityNet
(using extra training data)
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.
Neighbor Correspondence Matching for Flow-based Video Frame Synthesis
Since the current frame is not available in video frame synthesis, NCM is performed in a current-frame-agnostic fashion to establish multi-scale correspondences in the spatial-temporal neighborhoods of each pixel.
Ranked #1 on
Video Frame Interpolation
on X4K1000FPS
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).
Self-Adaptive Label Augmentation for Semi-supervised Few-shot Classification
A major novelty of SALA is the task-adaptive metric, which can learn the metric adaptively for different tasks in an end-to-end fashion.
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
A Self-Paced Mixed Distillation Method for Non-Autoregressive Generation
To further illustrate the commercial value of our approach, we conduct experiments on three generation tasks in real-world advertisements applications.
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
Estimation of Reliable Proposal Quality for Temporal Action Detection
For the region perspective, we introduce Region Evaluate Module (REM) which uses a new and efficient sampling method for proposal feature representation containing more contextual information compared with point feature to refine category score and proposal boundary.
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.
Large-Scale Pre-training for Person Re-identification with Noisy Labels
Since theses ID labels automatically derived from tracklets inevitably contain noises, we develop a large-scale Pre-training framework utilizing Noisy Labels (PNL), which consists of three learning modules: supervised Re-ID learning, prototype-based contrastive learning, and label-guided contrastive learning.
Ranked #6 on
Person Re-Identification
on CUHK03
Learning Enriched Illuminants for Cross and Single Sensor Color Constancy
Color constancy aims to restore the constant colors of a scene under different illuminants.
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
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
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.
Representing Videos as Discriminative Sub-graphs for Action Recognition
For each action category, we execute online clustering to decompose the graph into sub-graphs on each scale through learning Gaussian Mixture Layer and select the discriminative sub-graphs as action prototypes for recognition.
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 Robust Policy against Disturbance in Transition Dynamics via State-Conservative Policy Optimization
Many existing algorithms learn robust policies by modeling the disturbance and applying it to source environments during training, which usually requires prior knowledge about the disturbance and control of simulators.
Radio-Assisted Human Detection
In this paper, we propose a radio-assisted human detection framework by incorporating radio information into the state-of-the-art detection methods, including anchor-based onestage detectors and two-stage detectors.
Learning Token-based Representation for Image Retrieval
Next, a refinement block is introduced to enhance the visual tokens with self-attention and cross-attention.
VPFNet: Improving 3D Object Detection with Virtual Point based LiDAR and Stereo Data Fusion
However, this approach often suffers from the mismatch between the resolution of point clouds and RGB images, leading to sub-optimal performance.
Dual Progressive Prototype Network for Generalized Zero-Shot Learning
This enables DPPN to produce visual representations with accurate attribute localization ability, which benefits the semantic-visual alignment and representation transferability.
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.
Equivalence Analysis between Counterfactual Regret Minimization and Online Mirror Descent
Follow-the-Regularized-Lead (FTRL) and Online Mirror Descent (OMD) are regret minimization algorithms for Online Convex Optimization (OCO), they are mathematically elegant but less practical in solving Extensive-Form Games (EFGs).
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.
One-shot Key Information Extraction from Document with Deep Partial Graph Matching
However, collecting and labeling a large dataset is time-consuming and is not a user-friendly requirement for many cloud platforms.
Learning Fine-Grained Motion Embedding for Landscape Animation
To tackle this problem, we propose a model named FGLA to generate high-quality and realistic videos by learning Fine-Grained motion embedding for Landscape Animation.
Discovering Representation Sprachbund For Multilingual Pre-Training
Then we cluster all the target languages into multiple groups and name each group as a representation sprachbund.
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.
Conditional DETR for Fast Training Convergence
Our approach, named conditional DETR, learns a conditional spatial query from the decoder embedding for decoder multi-head cross-attention.
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.
Supervised Off-Policy Ranking
Inspired by the two observations, in this work, we study a new problem, supervised off-policy ranking (SOPR), which aims to rank a set of target policies based on supervised learning by leveraging off-policy data and policies with known performance.
Revisiting Knowledge Distillation: An Inheritance and Exploration Framework
The inheritance part is learned with a similarity loss to transfer the existing learned knowledge from the teacher model to the student model, while the exploration part is encouraged to learn representations different from the inherited ones with a dis-similarity loss.
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.
Probing Inter-modality: Visual Parsing with Self-Attention for Vision-Language Pre-training
To tackle this, we propose a fully Transformer visual embedding for VLP to better learn visual relation and further promote inter-modal alignment.
Multi-Modal 3D Object Detection in Autonomous Driving: a Survey
In this survey, we first introduce the background of popular sensors used for self-driving, their data properties, and the corresponding object detection algorithms.
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
Probing Inter-modality: Visual Parsing with Self-Attention for Vision-and-Language Pre-training
To tackle this, we propose a fully Transformer visual embedding for VLP to better learn visual relation and further promote inter-modal alignment.
Understanding Chinese Video and Language via Contrastive Multimodal Pre-Training
The pre-trained neural models have recently achieved impressive performances in understanding multimodal content.
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
ProphetNet-X: Large-Scale Pre-training Models for English, Chinese, Multi-lingual, Dialog, and Code Generation
ProphetNet is a pre-training based natural language generation method which shows powerful performance on English text summarization and question generation tasks.
Task-Independent Knowledge Makes for Transferable Representations for Generalized Zero-Shot Learning
First, DCEN leverages task labels to cluster representations of the same semantic category by cross-modal contrastive learning and exploring semantic-visual complementarity.
Generating Diverse Structure for Image Inpainting With Hierarchical VQ-VAE
We propose a two-stage model for diverse inpainting, where the first stage generates multiple coarse results each of which has a different structure, and the second stage refines each coarse result separately by augmenting texture.
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.
Consistent Instance Classification for Unsupervised Representation Learning
The proposed consistent instance classification (ConIC) approach simultaneously optimizes the classification loss and an additional consistency loss explicitly penalizing the feature dissimilarity between the augmented views from the same instance.
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.
3D Local Convolutional Neural Networks for Gait Recognition
Second, different body parts possess different scales, and even the same part in different frames can appear at different locations and scales.
Ranked #2 on
Gait Recognition
on OUMVLP
BANG: Bridging Autoregressive and Non-autoregressive Generation with Large Scale Pretraining
In this paper, we propose BANG, a new pretraining model to Bridge the gap between Autoregressive (AR) and Non-autoregressive (NAR) Generation.
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).
Unsupervised Pre-training for Person Re-identification
In this paper, we present a large scale unlabeled person re-identification (Re-ID) dataset "LUPerson" and make the first attempt of performing unsupervised pre-training for improving the generalization ability of the learned person Re-ID feature representation.
Ranked #2 on
Person Re-Identification
on Market-1501
(using extra training data)
Promoting Stochasticity for Expressive Policies via a Simple and Efficient Regularization Method
However, in continuous action spaces, integrating entropy regularization with expressive policies is challenging and usually requires complex inference procedures.
Spatio-Temporal Inception Graph Convolutional Networks for Skeleton-Based Action Recognition
The topology of the adjacency graph is a key factor for modeling the correlations of the input skeletons.
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.
ProphetNet-Ads: A Looking Ahead Strategy for Generative Retrieval Models in Sponsored Search Engine
We build a dataset from a real-word sponsored search engine and carry out experiments to analyze different generative retrieval models.
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.
Improving Person Re-identification with Iterative Impression Aggregation
Not only does such a simple method improve the performance of the baseline models, it also achieves comparable performance with latest advanced re-ranking methods.
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.
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.
Efficient Integer-Arithmetic-Only Convolutional Neural Networks
Considering the tradeoff between activation quantization error and network learning ability, we set an empirical rule to tune the bound of each Bounded ReLU.
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.
Long Short-Term Relation Networks for Video Action Detection
It has been well recognized that modeling human-object or object-object relations would be helpful for detection task.
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)
Soft Hindsight Experience Replay
However, due to the brittleness of deterministic methods, HER and its variants typically suffer from a major challenge for stability and convergence, which significantly affects the final performance.
Deep Model-Based Reinforcement Learning via Estimated Uncertainty and Conservative Policy Optimization
In this paper, We propose a Policy Optimization method with Model-Based Uncertainty (POMBU)---a novel model-based approach---that can effectively improve the asymptotic performance using the uncertainty in Q-values.
Model-based Reinforcement Learning
reinforcement-learning
+1
A Generalization Theory based on Independent and Task-Identically Distributed Assumption
The derived generalization bound based on the ITID assumption identifies the significance of hypothesis invariance in guaranteeing generalization performance.
Quantization Networks
The proposed quantization function can be learned in a lossless and end-to-end manner and works for any weights and activations of neural networks in a simple and uniform way.
AETv2: AutoEncoding Transformations for Self-Supervised Representation Learning by Minimizing Geodesic Distances in Lie Groups
For this reason, we should use the geodesic to characterize how an image transform along the manifold of a transformation group, and adopt its length to measure the deviation between transformations.
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.
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.
Learn Interpretable Word Embeddings Efficiently with von Mises-Fisher Distribution
Results show that our model can outperform the dominant models consistently in these tasks.
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.
Deep Learning-Based Video Coding: A Review and A Case Study
For deep schemes, pixel probability modeling and auto-encoder are the two approaches, that can be viewed as predictive coding scheme and transform coding scheme, respectively.
Multimedia Image and Video Processing
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.
In Defense of the Classification Loss for Person Re-Identification
The recent research for person re-identification has been focused on two trends.
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.
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.
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.
To Create What You Tell: Generating Videos from Captions
In this paper, we present a novel Temporal GANs conditioning on Captions, namely TGANs-C, in which the input to the generator network is a concatenation of a latent noise vector and caption embedding, and then is transformed into a frame sequence with 3D spatio-temporal convolutions.
Towards Open-Set Identity Preserving Face Synthesis
We then recombine the identity vector and the attribute vector to synthesize a new face of the subject with the extracted attribute.
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.
Feature Selective Networks for Object Detection
The RoI-based sub-region attention map and aspect ratio attention map are selectively pooled from the banks, and then used to refine the original RoI features for RoI classification.
Neural network-based arithmetic coding of intra prediction modes in HEVC
In this paper, we propose an arithmetic coding strategy by training neural networks, and make preliminary studies on coding of the intra prediction modes in HEVC.
Multimedia
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.
CVAE-GAN: Fine-Grained Image Generation through Asymmetric Training
Our approach models an image as a composition of label and latent attributes in a probabilistic model.
A Convolutional Neural Network Approach for Half-Pel Interpolation in Video Coding
To further improve the coding efficiency, sub-pel motion compensation has been utilized, which requires interpolation of fractional samples.
Multimedia
Convolutional Neural Network-Based Block Up-sampling for Intra Frame Coding
A block can be down-sampled before being compressed by normal intra coding, and then up-sampled to its original resolution.
Multimedia
Projection based advanced motion model for cubic mapping for 360-degree video
This paper proposes a novel advanced motion model to handle the irregular motion for the cubic map projection of 360-degree video.
Video Captioning with Transferred Semantic Attributes
Automatically generating natural language descriptions of videos plays a fundamental challenge for computer vision community.
Comparative Deep Learning of Hybrid Representations for Image Recommendations
In many image-related tasks, learning expressive and discriminative representations of images is essential, and deep learning has been studied for automating the learning of such representations.
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
Semi-Supervised Domain Adaptation With Subspace Learning for Visual Recognition
In many real-world applications, we are often facing the problem of cross domain learning, i. e., to borrow the labeled data or transfer the already learnt knowledge from a source domain to a target domain.
Jointly Modeling Embedding and Translation to Bridge Video and Language
Our proposed LSTM-E consists of three components: a 2-D and/or 3-D deep convolutional neural networks for learning powerful video representation, a deep RNN for generating sentences, and a joint embedding model for exploring the relationships between visual content and sentence semantics.
Separable Kernel for Image Deblurring
In this paper, we deal with the image deblurring problem in a completely new perspective by proposing separable kernel to represent the inherent properties of the camera and scene system.
