activity
20202024
most citedPareto Self-Supervised Training for Few-Shot Learning

2 citations · 3 across the 4 of their papers we have counts for

collaborators

5 papers

cs.CV2024

ProFD: Prompt-Guided Feature Disentangling for Occluded Person Re-Identification

Can Cui, Siteng Huang, Wenxuan Song +3

To address the occlusion issues in person Re-Identification (ReID) tasks, many methods have been proposed to extract part features by introducing external spatial information. Howe…

cs.CV20241 cited

PiTe: Pixel-Temporal Alignment for Large Video-Language Model

Yang Liu, Pengxiang Ding, Siteng Huang +3

Fueled by the Large Language Models (LLMs) wave, Large Visual-Language Models (LVLMs) have emerged as a pivotal advancement, bridging the gap between image and text. However, video…

cs.CV2024

Focus-Consistent Multi-Level Aggregation for Compositional Zero-Shot Learning

Fengyuan Dai, Siteng Huang, Min Zhang +2

To transfer knowledge from seen attribute-object compositions to recognize unseen ones, recent compositional zero-shot learning (CZSL) methods mainly discuss the optimal classifica…

cs.CV20212 cited

Pareto Self-Supervised Training for Few-Shot Learning

Zhengyu Chen, Jixie Ge, Heshen Zhan +2

While few-shot learning (FSL) aims for rapid generalization to new concepts with little supervision, self-supervised learning (SSL) constructs supervisory signals directly computed…

cs.CV2020

Attributes-Guided and Pure-Visual Attention Alignment for Few-Shot Recognition

Siteng Huang, Min Zhang, Yachen Kang +1

The purpose of few-shot recognition is to recognize novel categories with a limited number of labeled examples in each class. To encourage learning from a supplementary view, recen…