4.8k citations
Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.
- Peking UniversityCN85 papers
- Microsoft (United States)US82 papers
- Tsinghua UniversityCN81 papers
- University of Science and Technology of ChinaCN81 papers
- Hong Kong University of Science and TechnologyHK40 papers
- Beihang UniversityCN36 papers
- Chinese Academy of SciencesCN36 papers
- Shanghai Jiao Tong UniversityCN33 papers
- Microsoft Research (United Kingdom)GB28 papers
- Zhejiang UniversityCN28 papers
- Fudan UniversityCN24 papers
- Carnegie Mellon UniversityUS19 papers
20 papers · 2 filters
Recursive Least-Squares Estimator-Aided Online Learning for Visual Tracking
Jin Gao, Yan Lu, Xiaojuan Qi +5
Tracking visual objects from a single initial exemplar in the testing phase has been broadly cast as a one-/few-shot problem, i.e., one-shot learning for initial adaptation and few…
Temporal Context Mining for Learned Video Compression
Xihua Sheng, Jiahao Li, Bin Li +3
We address end-to-end learned video compression with a special focus on better learning and utilizing temporal contexts. For temporal context mining, we propose to store not only t…
A Picture is Worth a Thousand Words: A Unified System for Diverse Captions and Rich Images Generation
Yupan Huang, Bei Liu, Jianlong Fu +1
A creative image-and-text generative AI system mimics humans' extraordinary abilities to provide users with diverse and comprehensive caption suggestions, as well as rich image cre…
Unifying Multimodal Transformer for Bi-directional Image and Text Generation
Yupan Huang, Hongwei Xue, Bei Liu +1
We study the joint learning of image-to-text and text-to-image generations, which are naturally bi-directional tasks. Typical existing works design two separate task-specific model…
ACP++: Action Co-occurrence Priors for Human-Object Interaction Detection
Dong-Jin Kim, Xiao Sun, Jinsoo Choi +2
A common problem in the task of human-object interaction (HOI) detection is that numerous HOI classes have only a small number of labeled examples, resulting in training sets with…
Pose-Guided Feature Learning with Knowledge Distillation for Occluded Person Re-Identification
Kecheng Zheng, Cuiling Lan, Wenjun Zeng +3
Occluded person re-identification (ReID) aims to match person images with occlusion. It is fundamentally challenging because of the serious occlusion which aggravates the misalignm…
Reference-based Defect Detection Network
Zhaoyang Zeng, Bei Liu, Jianlong Fu +1
The defect detection task can be regarded as a realistic scenario of object detection in the computer vision field and it is widely used in the industrial field. Directly applying…
Hybrid Reasoning Network for Video-based Commonsense Captioning
Weijiang Yu, Jian Liang, Lei Ji +4
The task of video-based commonsense captioning aims to generate event-wise captions and meanwhile provide multiple commonsense descriptions (e.g., attribute, effect and intention)…
StyleCariGAN: Caricature Generation via StyleGAN Feature Map Modulation
Wonjong Jang, Gwangjin Ju, Yucheol Jung +3
We present a caricature generation framework based on shape and style manipulation using StyleGAN. Our framework, dubbed StyleCariGAN, automatically creates a realistic and detaile…
DISP6D: Disentangled Implicit Shape and Pose Learning for Scalable 6D Pose Estimation
Yilin Wen, Xiangyu Li, Hao Pan +4
Scalable 6D pose estimation for rigid objects from RGB images aims at handling multiple objects and generalizing to novel objects. Building on a well-known auto-encoding framework…
AutoFormer: Searching Transformers for Visual Recognition
Minghao Chen, Houwen Peng, Jianlong Fu +1
Recently, pure transformer-based models have shown great potentials for vision tasks such as image classification and detection. However, the design of transformer networks is chal…
Deep Subdomain Adaptation Network for Image Classification
Yongchun Zhu, Fuzhen Zhuang, Jindong Wang +5
For a target task where labeled data is unavailable, domain adaptation can transfer a learner from a different source domain. Previous deep domain adaptation methods mainly learn a…
Transferable Adversarial Examples for Anchor Free Object Detection
Quanyu Liao, Xin Wang, Bin Kong +5
Deep neural networks have been demonstrated to be vulnerable to adversarial attacks: subtle perturbation can completely change prediction result. The vulnerability has led to a sur…
A Reinforcement-Learning-Based Energy-Efficient Framework for Multi-Task Video Analytics Pipeline
Yingying Zhao, Mingzhi Dong, Yujiang Wang +7
Deep-learning-based video processing has yielded transformative results in recent years. However, the video analytics pipeline is energy-intensive due to high data rates and relian…
Unsupervised Shape Completion via Deep Prior in the Neural Tangent Kernel Perspective
Lei Chu, Hao Pan, Wenping Wang
We present a novel approach for completing and reconstructing 3D shapes from incomplete scanned data by using deep neural networks. Rather than being trained on supervised completi…
MonoGRNet: A General Framework for Monocular 3D Object Detection
Zengyi Qin, Jinglu Wang, Yan Lu
Detecting and localizing objects in the real 3D space, which plays a crucial role in scene understanding, is particularly challenging given only a monocular image due to the geomet…
3D Human Body Reshaping with Anthropometric Modeling
Yanhong Zeng, Jianlong Fu, Hongyang Chao
Reshaping accurate and realistic 3D human bodies from anthropometric parameters (e.g., height, chest size, etc.) poses a fundamental challenge for person identification, online sho…
Re-energizing Domain Discriminator with Sample Relabeling for Adversarial Domain Adaptation
Xin Jin, Cuiling Lan, Wenjun Zeng +1
Many unsupervised domain adaptation (UDA) methods exploit domain adversarial training to align the features to reduce domain gap, where a feature extractor is trained to fool a dom…
Prototypical Pseudo Label Denoising and Target Structure Learning for Domain Adaptive Semantic Segmentation
Pan Zhang, Bo Zhang, Ting Zhang +3
Self-training is a competitive approach in domain adaptive segmentation, which trains the network with the pseudo labels on the target domain. However inevitably, the pseudo labels…
Style Normalization and Restitution for Domain Generalization and Adaptation
Xin Jin, Cuiling Lan, Wenjun Zeng +1
For many practical computer vision applications, the learned models usually have high performance on the datasets used for training but suffer from significant performance degradat…