47 citations · 133 across the 15 of their papers we have counts for
14 papers · 1 filter
Beyond Appearance: a Semantic Controllable Self-Supervised Learning Framework for Human-Centric Visual Tasks
Weihua Chen, Xianzhe Xu, Jian Jia +5
Human-centric visual tasks have attracted increasing research attention due to their widespread applications. In this paper, we aim to learn a general human representation from mas…
Making Vision Transformers Efficient from A Token Sparsification View
Shuning Chang, Pichao Wang, Ming Lin +4
The quadratic computational complexity to the number of tokens limits the practical applications of Vision Transformers (ViTs). Several works propose to prune redundant tokens to a…
HyRSM++: Hybrid Relation Guided Temporal Set Matching for Few-shot Action Recognition
Xiang Wang, Shiwei Zhang, Zhiwu Qing +4
Recent attempts mainly focus on learning deep representations for each video individually under the episodic meta-learning regime and then performing temporal alignment to match qu…
An Empirical Study on Distribution Shift Robustness From the Perspective of Pre-Training and Data Augmentation
Ziquan Liu, Yi Xu, Yuanhong Xu +5
The performance of machine learning models under distribution shift has been the focus of the community in recent years. Most of current methods have been proposed to improve the r…
Hybrid Relation Guided Set Matching for Few-shot Action Recognition
Xiang Wang, Shiwei Zhang, Zhiwu Qing +5
Current few-shot action recognition methods reach impressive performance by learning discriminative features for each video via episodic training and designing various temporal ali…
Learning from Untrimmed Videos: Self-Supervised Video Representation Learning with Hierarchical Consistency
Zhiwu Qing, Shiwei Zhang, Ziyuan Huang +6
Natural videos provide rich visual contents for self-supervised learning. Yet most existing approaches for learning spatio-temporal representations rely on manually trimmed videos,…
ELSA: Enhanced Local Self-Attention for Vision Transformer
Jingkai Zhou, Pichao Wang, Fan Wang +3
Self-attention is powerful in modeling long-range dependencies, but it is weak in local finer-level feature learning. The performance of local self-attention (LSA) is just on par w…
Decoupling and Recoupling Spatiotemporal Representation for RGB-D-based Motion Recognition
Benjia Zhou, Pichao Wang, Jun Wan +6
Decoupling spatiotemporal representation refers to decomposing the spatial and temporal features into dimension-independent factors. Although previous RGB-D-based motion recognitio…
TransFGU: A Top-down Approach to Fine-Grained Unsupervised Semantic Segmentation
Zhaoyuan Yin, Pichao Wang, Fan Wang +4
Unsupervised semantic segmentation aims to obtain high-level semantic representation on low-level visual features without manual annotations. Most existing methods are bottom-up ap…
Self-Supervised Pre-Training for Transformer-Based Person Re-Identification
Hao Luo, Pichao Wang, Yi Xu +5
Transformer-based supervised pre-training achieves great performance in person re-identification (ReID). However, due to the domain gap between ImageNet and ReID datasets, it usual…
MAE-DET: Revisiting Maximum Entropy Principle in Zero-Shot NAS for Efficient Object Detection
Zhenhong Sun, Ming Lin, Xiuyu Sun +3
In object detection, the detection backbone consumes more than half of the overall inference cost. Recent researches attempt to reduce this cost by optimizing the backbone architec…
Improved Fine-Tuning by Better Leveraging Pre-Training Data
Ziquan Liu, Yi Xu, Yuanhong Xu +5
As a dominant paradigm, fine-tuning a pre-trained model on the target data is widely used in many deep learning applications, especially for small data sets. However, recent studie…
ParamCrop: Parametric Cubic Cropping for Video Contrastive Learning
Zhiwu Qing, Ziyuan Huang, Shiwei Zhang +5
The central idea of contrastive learning is to discriminate between different instances and force different views from the same instance to share the same representation. To avoid…
Zen-NAS: A Zero-Shot NAS for High-Performance Deep Image Recognition
Ming Lin, Pichao Wang, Zhenhong Sun +5
Accuracy predictor is a key component in Neural Architecture Search (NAS) for ranking architectures. Building a high-quality accuracy predictor usually costs enormous computation.…