activity
20142023
most citedFrequency Domain Model Augmentation for Adversarial Attack

7 citations · 27 across the 10 of their papers we have counts for

collaborators

8 papers

cs.CV20221 cited

RepParser: End-to-End Multiple Human Parsing with Representative Parts

Xiaojia Chen, Xuanhan Wang, Lianli Gao +1

Existing methods of multiple human parsing usually adopt a two-stage strategy (typically top-down and bottom-up), which suffers from either strong dependence on prior detection or…

cs.CV20222 cited

Towards Open-vocabulary Scene Graph Generation with Prompt-based Finetuning

Tao He, Lianli Gao, Jingkuan Song +1

Scene graph generation (SGG) is a fundamental task aimed at detecting visual relations between objects in an image. The prevailing SGG methods require all object classes to be give…

cs.CV2022

Prompting for Multi-Modal Tracking

Jinyu Yang, Zhe Li, Feng Zheng +2

Multi-modal tracking gains attention due to its ability to be more accurate and robust in complex scenarios compared to traditional RGB-based tracking. Its key lies in how to fuse…

cs.CV20227 cited

Frequency Domain Model Augmentation for Adversarial Attack

Yuyang Long, Qilong Zhang, Boheng Zeng +4

For black-box attacks, the gap between the substitute model and the victim model is usually large, which manifests as a weak attack performance. Motivated by the observation that t…

cs.CV20222 cited

Adaptive Fine-Grained Predicates Learning for Scene Graph Generation

Xinyu Lyu, Lianli Gao, Pengpeng Zeng +2

The performance of current Scene Graph Generation (SGG) models is severely hampered by hard-to-distinguish predicates, e.g., woman-on/standing on/walking on-beach. As general SGG m…

cs.CV2022

Skeleton-based Action Recognition via Adaptive Cross-Form Learning

Xuanhan Wang, Yan Dai, Lianli Gao +1

Skeleton-based action recognition aims to project skeleton sequences to action categories, where skeleton sequences are derived from multiple forms of pre-detected points. Compared…