269 citations · 1.8k across the 57 of their papers we have counts for
42 papers · 1 filter
Spatio-Temporal Point Process for Multiple Object Tracking
Tao Wang, Kean Chen, Weiyao Lin +4
Multiple Object Tracking (MOT) focuses on modeling the relationship of detected objects among consecutive frames and merge them into different trajectories. MOT remains a challengi…
The 1st-place Solution for ECCV 2022 Multiple People Tracking in Group Dance Challenge
Yuang Zhang, Tiancai Wang, Weiyao Lin +1
We present our 1st place solution to the Group Dance Multiple People Tracking Challenge. Based on MOTR: End-to-End Multiple-Object Tracking with Transformer, we explore: 1) detect…
FRIH: Fine-grained Region-aware Image Harmonization
Jinlong Peng, Zekun Luo, Liang Liu +6
Image harmonization aims to generate a more realistic appearance of foreground and background for a composite image. Existing methods perform the same harmonization process for the…
Class-aware Sounding Objects Localization via Audiovisual Correspondence
Di Hu, Yake Wei, Rui Qian +3
Audiovisual scenes are pervasive in our daily life. It is commonplace for humans to discriminatively localize different sounding objects but quite challenging for machines to achie…
Exploring the Semi-supervised Video Object Segmentation Problem from a Cyclic Perspective
Yuxi Li, Ning Xu, Wenjie Yang +2
Modern video object segmentation (VOS) algorithms have achieved remarkably high performance in a sequential processing order, while most of currently prevailing pipelines still sho…
LSTC: Boosting Atomic Action Detection with Long-Short-Term Context
Yuxi Li, Boshen Zhang, Jian Li +5
In this paper, we place the atomic action detection problem into a Long-Short Term Context (LSTC) to analyze how the temporal reliance among video signals affect the action detecti…
Enhancing Self-supervised Video Representation Learning via Multi-level Feature Optimization
Rui Qian, Yuxi Li, Huabin Liu +5
The crux of self-supervised video representation learning is to build general features from unlabeled videos. However, most recent works have mainly focused on high-level semantics…
SiamRCR: Reciprocal Classification and Regression for Visual Object Tracking
Jinlong Peng, Zhengkai Jiang, Yueyang Gu +5
Recently, most siamese network based trackers locate targets via object classification and bounding-box regression. Generally, they select the bounding-box with maximum classificat…
Variational Pedestrian Detection
Yuang Zhang, Huanyu He, Jianguo Li +3
Pedestrian detection in a crowd is a challenging task due to a high number of mutually-occluding human instances, which brings ambiguity and optimization difficulties to the curren…
Delving into the Cyclic Mechanism in Semi-supervised Video Object Segmentation
Yuxi Li, Ning Xu, Jinlong Peng +2
In this paper, we address several inadequacies of current video object segmentation pipelines. Firstly, a cyclic mechanism is incorporated to the standard semi-supervised process t…
Discriminative Sounding Objects Localization via Self-supervised Audiovisual Matching
Di Hu, Rui Qian, Minyue Jiang +5
Discriminatively localizing sounding objects in cocktail-party, i.e., mixed sound scenes, is commonplace for humans, but still challenging for machines. In this paper, we propose a…
Finding Action Tubes with a Sparse-to-Dense Framework
Yuxi Li, Weiyao Lin, Tao Wang +5
The task of spatial-temporal action detection has attracted increasing attention among researchers. Existing dominant methods solve this problem by relying on short-term informatio…
CFAD: Coarse-to-Fine Action Detector for Spatiotemporal Action Localization
Yuxi Li, Weiyao Lin, John See +4
Most current pipelines for spatio-temporal action localization connect frame-wise or clip-wise detection results to generate action proposals, where only local information is explo…
AP-Loss for Accurate One-Stage Object Detection
Kean Chen, Weiyao Lin, Jianguo Li +3
One-stage object detectors are trained by optimizing classification-loss and localization-loss simultaneously, with the former suffering much from extreme foreground-background cla…
PIoU Loss: Towards Accurate Oriented Object Detection in Complex Environments
Zhiming Chen, Kean Chen, Weiyao Lin +4
Object detection using an oriented bounding box (OBB) can better target rotated objects by reducing the overlap with background areas. Existing OBB approaches are mostly built on h…
Multiple Sound Sources Localization from Coarse to Fine
Rui Qian, Di Hu, Heinrich Dinkel +3
How to visually localize multiple sound sources in unconstrained videos is a formidable problem, especially when lack of the pairwise sound-object annotations. To solve this proble…
Trained Rank Pruning for Efficient Deep Neural Networks
Yuhui Xu, Yuxi Li, Shuai Zhang +7
To accelerate DNNs inference, low-rank approximation has been widely adopted because of its solid theoretical rationale and efficient implementations. Several previous works attemp…
ATRW: A Benchmark for Amur Tiger Re-identification in the Wild
Shuyuan Li, Jianguo Li, Hanlin Tang +2
Monitoring the population and movements of endangered species is an important task to wildlife conversation. Traditional tagging methods do not scale to large populations, while ap…
Group Re-Identification with Multi-grained Matching and Integration
Weiyao Lin, Yuxi Li, Hao Xiao +5
The task of re-identifying groups of people underdifferent camera views is an important yet less-studied problem.Group re-identification (Re-ID) is a very challenging task sinceit…
Towards Accurate One-Stage Object Detection with AP-Loss
Kean Chen, Jianguo Li, Weiyao Lin +6
One-stage object detectors are trained by optimizing classification-loss and localization-loss simultaneously, with the former suffering much from extreme foreground-background cla…
Trained Rank Pruning for Efficient Deep Neural Networks
Yuhui Xu, Yuxi Li, Shuai Zhang +6
The performance of Deep Neural Networks (DNNs) keeps elevating in recent years with increasing network depth and width. To enable DNNs on edge devices like mobile phones, researche…
Network Decoupling: From Regular to Depthwise Separable Convolutions
Jianbo Guo, Yuxi Li, Weiyao Lin +2
Depthwise separable convolution has shown great efficiency in network design, but requires time-consuming training procedure with full training-set available. This paper first anal…
Tiny-DSOD: Lightweight Object Detection for Resource-Restricted Usages
Yuxi Li, Jiuwei Li, Weiyao Lin +1
Object detection has made great progress in the past few years along with the development of deep learning. However, most current object detection methods are resource hungry, whic…
Enriched Long-term Recurrent Convolutional Network for Facial Micro-Expression Recognition
Huai-Qian Khor, John See, Raphael C. W. Phan +1
Facial micro-expression (ME) recognition has posed a huge challenge to researchers for its subtlety in motion and limited databases. Recently, handcrafted techniques have achieved…