Search Results for author:
Found 67 papers, 26 papers with code
Multi-target Tracking with Motion Context in Tensor Power Iteration
In this paper, we model interactions between neighbor targets by pair-wise motion context, and further encode such context into the global association optimization.
Towards Multi-view and Partially-Occluded Face Alignment
During each training stage, the SRD model learns a relational dictionary to capture consistent relationships between face appearance and shape, which are respectively modeled by the pose-indexed image features and the shape displacements for current estimated landmarks.
Multiple Object Tracking: A Literature Review
We inspect the recent advances in various aspects and propose some interesting directions for future research.
Shape Driven Kernel Adaptation in Convolutional Neural Network for Robust Facial Traits Recognition
Comprehensive experiments on WebFace, Morph II and MultiPIE databases well validate the effectiveness of the proposed kernel adaptation method and tree-structured convolutional architecture for facial traits recognition tasks, including identity, age and gender classification.
Local Subspace Collaborative Tracking
To address this, this paper presents a local subspace collaborative tracking method for robust visual tracking, where multiple linear and nonlinear subspaces are learned to better model the nonlinear relationship of object appearances.
Co-occurrence Feature Learning for Skeleton based Action Recognition using Regularized Deep LSTM Networks
Skeleton based action recognition distinguishes human actions using the trajectories of skeleton joints, which provide a very good representation for describing actions.
Online Human Action Detection using Joint Classification-Regression Recurrent Neural Networks
In this paper, we study the problem of online action detection from streaming skeleton data.
Deep Attributes Driven Multi-Camera Person Re-identification
And we propose a semi-supervised attribute learning framework which progressively boosts the accuracy of attributes only using a limited number of labeled data.
Tensor Power Iteration for Multi-Graph Matching
Due to its wide range of applications, matching between two graphs has been extensively studied and remains an active topic.
An End-to-End Spatio-Temporal Attention Model for Human Action Recognition from Skeleton Data
In this work, we propose an end-to-end spatial and temporal attention model for human action recognition from skeleton data.
Ranked #110 on
Skeleton Based Action Recognition
on NTU RGB+D
A Graphical Social Topology Model for Multi-Object Tracking
Inspired with the social affinity property of moving objects, we propose a Graphical Social Topology (GST) model, which estimates the group dynamics by jointly modeling the group structure and the states of objects using a topological representation.
View Adaptive Recurrent Neural Networks for High Performance Human Action Recognition from Skeleton Data
Rather than re-positioning the skeletons based on a human defined prior criterion, we design a view adaptive recurrent neural network (RNN) with LSTM architecture, which enables the network itself to adapt to the most suitable observation viewpoints from end to end.
Ranked #6 on
Skeleton Based Action Recognition
on SYSU 3D
DCFNet: Discriminant Correlation Filters Network for Visual Tracking
In this work, we present an end-to-end lightweight network architecture, namely DCFNet, to learn the convolutional features and perform the correlation tracking process simultaneously.
Generative Partition Networks for Multi-Person Pose Estimation
This paper proposes a new Generative Partition Network (GPN) to address the challenging multi-person pose estimation problem.
Ranked #1 on
Multi-Person Pose Estimation
on WAF
(AP metric)
A Deep Regression Architecture With Two-Stage Re-Initialization for High Performance Facial Landmark Detection
At the global stage, given an image with a rough face detection result, the full face region is firstly re-initialized by a supervised spatial transformer network to a canonical shape state and then trained to regress a coarse landmark estimation.
Human Pose Estimation using Global and Local Normalization
We present a two-stage normalization scheme, human body normalization and limb normalization, to make the distribution of the relative joint locations compact, resulting in easier learning of convolutional spatial models and more accurate pose estimation.
Pose-driven Deep Convolutional Model for Person Re-identification
Our deep architecture explicitly leverages the human part cues to alleviate the pose variations and learn robust feature representations from both the global image and different local parts.
Ranked #105 on
Person Re-Identification
on Market-1501
Robust Object Tracking Based on Temporal and Spatial Deep Networks
Our deep architecture contains three networks, a Feature Net, a Temporal Net, and a Spatial Net.
View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition
In order to alleviate the effects of view variations, this paper introduces a novel view adaptation scheme, which automatically determines the virtual observation viewpoints in a learning based data driven manner.
Ranked #1 on
Skeleton Based Action Recognition
on UWA3D
Towards Pose Invariant Face Recognition in the Wild
To this end, we propose a Pose Invariant Model (PIM) for face recognition in the wild, with three distinct novelties.
Deep Cost-Sensitive and Order-Preserving Feature Learning for Cross-Population Age Estimation
First, a novel cost-sensitive multi-task loss function is designed to learn transferable aging features by training on the source population.
Learning Attentions: Residual Attentional Siamese Network for High Performance Online Visual Tracking
The RASNet model reformulates the correlation filter within a Siamese tracking framework, and introduces different kinds of the attention mechanisms to adapt the model without updating the model online.
Ranked #3 on
Visual Object Tracking
on OTB-2013
Pose Partition Networks for Multi-Person Pose Estimation
This paper proposes a novel Pose Partition Network (PPN) to address the challenging multi-person pose estimation problem.
Visual Tracking via Spatially Aligned Correlation Filters Network
Correlation filters based trackers rely on a periodic assumption of the search sample to efficiently distinguish the target from the background.
Look Across Elapse: Disentangled Representation Learning and Photorealistic Cross-Age Face Synthesis for Age-Invariant Face Recognition
Benchmarking our model on one of the most popular unconstrained face recognition datasets IJB-C additionally verifies the promising generalizability of AIM in recognizing faces in the wild.
Ranked #1 on
Age-Invariant Face Recognition
on MORPH Album2
Selective Refinement Network for High Performance Face Detection
In particular, the SRN consists of two modules: the Selective Two-step Classification (STC) module and the Selective Two-step Regression (STR) module.
Ranked #1 on
Face Detection
on PASCAL Face
Temporal-Spatial Mapping for Action Recognition
We propose a video level 2D feature representation by transforming the convolutional features of all frames to a 2D feature map, referred to as VideoMap.
Ranked #51 on
Action Recognition
on UCF101
Representation based and Attention augmented Meta learning
The method RAML aims to give the Meta learner the ability of leveraging the past learned knowledge to reduce the dimension of the original input data by expressing it into high representations, and help the Meta learner to perform well.
Cooperative Multi-Agent Policy Gradients with Sub-optimal Demonstration
To learn the multi-agent cooperation effectively and tackle the sub-optimality of demonstration, a self-improving learning method is proposed: On the one hand, the centralized state-action values are initialized by the demonstration and updated by the learned decentralized policy to improve the sub-optimality.
SiamRPN++: Evolution of Siamese Visual Tracking with Very Deep Networks
Moreover, we propose a new model architecture to perform depth-wise and layer-wise aggregations, which not only further improves the accuracy but also reduces the model size.
Ranked #5 on
Visual Object Tracking
on VOT2017/18
Multi-Prototype Networks for Unconstrained Set-based Face Recognition
In this paper, we study the challenging unconstrained set-based face recognition problem where each subject face is instantiated by a set of media (images and videos) instead of a single image.
Semantics-Guided Neural Networks for Efficient Skeleton-Based Human Action Recognition
Skeleton-based human action recognition has attracted great interest thanks to the easy accessibility of the human skeleton data.
Ranked #1 on
Skeleton Based Action Recognition
on SYSU 3D
Domain Adaptive Attention Model for Unsupervised Cross-Domain Person Re-Identification
To reduce domain divergence caused by that the source and target datasets are collected from different environments, we force to project the DSH feature maps from different domains to a new nominal domain, and a novel domain similarity loss is proposed based on one-class classification.
Cross-Resolution Face Recognition via Prior-Aided Face Hallucination and Residual Knowledge Distillation
Recent deep learning based face recognition methods have achieved great performance, but it still remains challenging to recognize very low-resolution query face like 28x28 pixels when CCTV camera is far from the captured subject.
PedHunter: Occlusion Robust Pedestrian Detector in Crowded Scenes
Pedestrian detection in crowded scenes is a challenging problem, because occlusion happens frequently among different pedestrians.
Relational Learning for Joint Head and Human Detection
Head and human detection have been rapidly improved with the development of deep convolutional neural networks.
RDSNet: A New Deep Architecture for Reciprocal Object Detection and Instance Segmentation
To reciprocate these two tasks, we design a two-stream structure to learn features on both the object level (i. e., bounding boxes) and the pixel level (i. e., instance masks) jointly.
Ranked #94 on
Instance Segmentation
on COCO test-dev
DSAM: A Distance Shrinking with Angular Marginalizing Loss for High Performance Vehicle Re-identificatio
To obtain a high-performance vehicle ReID model, we present a novel Distance Shrinking with Angular Marginalizing (DSAM) loss function to perform hybrid learning in both the Original Feature Space (OFS) and the Feature Angular Space (FAS) using the local verification and the global identification information.
OpenHoldem: A Benchmark for Large-Scale Imperfect-Information Game Research
Owning to the unremitting efforts by a few institutes, significant progress has recently been made in designing superhuman AIs in No-limit Texas Hold'em (NLTH), the primary testbed for large-scale imperfect-information game research.
Temperature Regret Matching for Imperfect-Information Games
Regret matching (RM) plays a crucial role in CFR and its variants to approach Nash equilibrium.
Anti-UAV: A Large Multi-Modal Benchmark for UAV Tracking
The releasing of such a large-scale dataset could be a useful initial step in research of tracking UAVs.
L2E: Learning to Exploit Your Opponent
In this work, we propose a novel Learning to Exploit (L2E) framework for implicit opponent modeling.
Contrastive Context-Aware Learning for 3D High-Fidelity Mask Face Presentation Attack Detection
To bridge the gap to real-world applications, we introduce a largescale High-Fidelity Mask dataset, namely CASIA-SURF HiFiMask (briefly HiFiMask).
Cooperative Multi-Agent Reinforcement Learning with Sequential Credit Assignment
In this paper, we propose a cooperative MARL method with sequential credit assignment (SeCA) that deduces each agent's contribution to the team's success one by one to learn better cooperation.
RefineCap: Concept-Aware Refinement for Image Captioning
Automatically translating images to texts involves image scene understanding and language modeling.
Ranked #27 on
Image Captioning
on COCO Captions
Actor-Critic Policy Optimization in a Large-Scale Imperfect-Information Game
The deep policy gradient method has demonstrated promising results in many large-scale games, where the agent learns purely from its own experience.
MMTSA: Multimodal Temporal Segment Attention Network for Efficient Human Activity Recognition
The efficiency evaluation on an edge device showed that MMTSA achieved significantly better accuracy, lower computational load, and lower inference latency than SOTA methods.
MMFace4D: A Large-Scale Multi-Modal 4D Face Dataset for Audio-Driven 3D Face Animation
Upon MMFace4D, we construct a non-autoregressive framework for audio-driven 3D face animation.
PMAA: A Progressive Multi-scale Attention Autoencoder Model for High-performance Cloud Removal from Multi-temporal Satellite Imagery
Satellite imagery analysis plays a pivotal role in remote sensing; however, information loss due to cloud cover significantly impedes its application.
Single-stage Multi-human Parsing via Point Sets and Center-based Offsets
We instead present a high-performance Single-stage Multi-human Parsing (SMP) deep architecture that decouples the multi-human parsing problem into two fine-grained sub-problems, i. e., locating the human body and parts.
Ranked #2 on
Multi-Human Parsing
on MHP v2.0
The 3rd Anti-UAV Workshop & Challenge: Methods and Results
Second, we set up two tracks for the first time, i. e., Anti-UAV Tracking and Anti-UAV Detection & Tracking.
Shuffled Autoregression For Motion Interpolation
However, motion interpolation is a more complex problem that takes isolated poses (e. g., only one start pose and one end pose) as input.
Evidential Detection and Tracking Collaboration: New Problem, Benchmark and Algorithm for Robust Anti-UAV System
Still, previous works have simplified such an anti-UAV task as a tracking problem, where the prior information of UAVs is always provided; such a scheme fails in real-world anti-UAV tasks (i. e. complex scenes, indeterminate-appear and -reappear UAVs, and real-time UAV surveillance).
SDDM: Score-Decomposed Diffusion Models on Manifolds for Unpaired Image-to-Image Translation
Recent score-based diffusion models (SBDMs) show promising results in unpaired image-to-image translation (I2I).
LEFormer: A Hybrid CNN-Transformer Architecture for Accurate Lake Extraction from Remote Sensing Imagery
LEFormer contains three main modules: CNN encoder, Transformer encoder, and cross-encoder fusion.
DiffCR: A Fast Conditional Diffusion Framework for Cloud Removal from Optical Satellite Images
Optical satellite images are a critical data source; however, cloud cover often compromises their quality, hindering image applications and analysis.
Speech-Driven 3D Face Animation with Composite and Regional Facial Movements
This paper emphasizes the importance of considering both the composite and regional natures of facial movements in speech-driven 3D face animation.
Semantics2Hands: Transferring Hand Motion Semantics between Avatars
Human hands, the primary means of non-verbal communication, convey intricate semantics in various scenarios.
Versatile Face Animator: Driving Arbitrary 3D Facial Avatar in RGBD Space
Creating realistic 3D facial animation is crucial for various applications in the movie production and gaming industry, especially with the burgeoning demand in the metaverse.
High-Fidelity Lake Extraction via Two-Stage Prompt Enhancement: Establishing a Novel Baseline and Benchmark
Lake extraction from remote sensing imagery is a complex challenge due to the varied lake shapes and data noise.
Unified Single-Stage Transformer Network for Efficient RGB-T Tracking
With this structure, the network can extract fusion features of the template and search region under the mutual interaction of modalities.
Ranked #1 on
Rgb-T Tracking
on GTOT
ARFA: An Asymmetric Receptive Field Autoencoder Model for Spatiotemporal Prediction
Additionally, we construct the RainBench, a large-scale radar echo dataset for precipitation prediction, to address the scarcity of meteorological data in the domain.
UniParser: Multi-Human Parsing with Unified Correlation Representation Learning
Multi-human parsing is an image segmentation task necessitating both instance-level and fine-grained category-level information.
Ranked #1 on
Multi-Human Parsing
on MHP v2.0
PanBench: Towards High-Resolution and High-Performance Pansharpening
Pansharpening, a pivotal task in remote sensing, involves integrating low-resolution multispectral images with high-resolution panchromatic images to synthesize an image that is both high-resolution and retains multispectral information.
Not All Tasks Are Equally Difficult: Multi-Task Deep Reinforcement Learning with Dynamic Depth Routing
Multi-task reinforcement learning endeavors to accomplish a set of different tasks with a single policy.
A Parallel Attention Network for Cattle Face Recognition
Thus, we create the first large-scale cattle face recognition dataset, ICRWE, for wild environments.
