activity
20182022
most citedBiSeNet V2: Bilateral Network with Guided Aggregation for Real-time Semantic Segmentation

115 citations · 238 across the 24 of their papers we have counts for

collaborators

29 papers

cs.CV20227 cited

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…

cs.CV2022

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,…

cs.CV20222 cited

Style Transformer for Image Inversion and Editing

Xueqi Hu, Qiusheng Huang, Zhengyi Shi +4

Existing GAN inversion methods fail to provide latent codes for reliable reconstruction and flexible editing simultaneously. This paper presents a transformer-based image inversion…

cs.CV202120 cited

CondNet: Conditional Classifier for Scene Segmentation

Changqian Yu, Yuanjie Shao, Changxin Gao +1

The fully convolutional network (FCN) has achieved tremendous success in dense visual recognition tasks, such as scene segmentation. The last layer of FCN is typically a global cla…

cs.CV2021

Weakly Supervised Person Search with Region Siamese Networks

Chuchu Han, Kai Su, Dongdong Yu +5

Supervised learning is dominant in person search, but it requires elaborate labeling of bounding boxes and identities. Large-scale labeled training data is often difficult to colle…

cs.CV20213 cited

Exploring Stronger Feature for Temporal Action Localization

Zhiwu Qing, Xiang Wang, Ziyuan Huang +6

Temporal action localization aims to localize starting and ending time with action category. Limited by GPU memory, mainstream methods pre-extract features for each video. Therefor…