29 citations · 107 across the 14 of their papers we have counts for
14 papers
RLIPv2: Fast Scaling of Relational Language-Image Pre-training
Hangjie Yuan, Shiwei Zhang, Xiang Wang +7
Relational Language-Image Pre-training (RLIP) aims to align vision representations with relational texts, thereby advancing the capability of relational reasoning in computer visio…
ViM: Vision Middleware for Unified Downstream Transferring
Yutong Feng, Biao Gong, Jianwen Jiang +4
Foundation models are pre-trained on massive data and transferred to downstream tasks via fine-tuning. This work presents Vision Middleware (ViM), a new learning paradigm that targ…
VoP: Text-Video Co-operative Prompt Tuning for Cross-Modal Retrieval
Siteng Huang, Biao Gong, Yulin Pan +4
Many recent studies leverage the pre-trained CLIP for text-video cross-modal retrieval by tuning the backbone with additional heavy modules, which not only brings huge computationa…
Grow and Merge: A Unified Framework for Continuous Categories Discovery
Xinwei Zhang, Jianwen Jiang, Yutong Feng +6
Although a number of studies are devoted to novel category discovery, most of them assume a static setting where both labeled and unlabeled data are given at once for finding new c…
RLIP: Relational Language-Image Pre-training for Human-Object Interaction Detection
Hangjie Yuan, Jianwen Jiang, Samuel Albanie +4
The task of Human-Object Interaction (HOI) detection targets fine-grained visual parsing of humans interacting with their environment, enabling a broad range of applications. Prior…
Rethinking Supervised Pre-training for Better Downstream Transferring
Yutong Feng, Jianwen Jiang, Mingqian Tang +2
The pretrain-finetune paradigm has shown outstanding performance on many applications of deep learning, where a model is pre-trained on a upstream large dataset (e.g. ImageNet), an…
NGC: A Unified Framework for Learning with Open-World Noisy Data
Zhi-Fan Wu, Tong Wei, Jianwen Jiang +3
The existence of noisy data is prevalent in both the training and testing phases of machine learning systems, which inevitably leads to the degradation of model performance. There…
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…
Proposal Relation Network for Temporal Action Detection
Xiang Wang, Zhiwu Qing, Ziyuan Huang +6
This technical report presents our solution for temporal action detection task in AcitivityNet Challenge 2021. The purpose of this task is to locate and identify actions of interes…
Weakly-Supervised Temporal Action Localization Through Local-Global Background Modeling
Xiang Wang, Zhiwu Qing, Ziyuan Huang +6
Weakly-Supervised Temporal Action Localization (WS-TAL) task aims to recognize and localize temporal starts and ends of action instances in an untrimmed video with only video-level…
Relation Modeling in Spatio-Temporal Action Localization
Yutong Feng, Jianwen Jiang, Ziyuan Huang +5
This paper presents our solution to the AVA-Kinetics Crossover Challenge of ActivityNet workshop at CVPR 2021. Our solution utilizes multiple types of relation modeling methods for…
A Stronger Baseline for Ego-Centric Action Detection
Zhiwu Qing, Ziyuan Huang, Xiang Wang +7
This technical report analyzes an egocentric video action detection method we used in the 2021 EPIC-KITCHENS-100 competition hosted in CVPR2021 Workshop. The goal of our task is to…
Towards Training Stronger Video Vision Transformers for EPIC-KITCHENS-100 Action Recognition
Ziyuan Huang, Zhiwu Qing, Xiang Wang +7
With the recent surge in the research of vision transformers, they have demonstrated remarkable potential for various challenging computer vision applications, such as image recogn…
Self-supervised Motion Learning from Static Images
Ziyuan Huang, Shiwei Zhang, Jianwen Jiang +3
Motions are reflected in videos as the movement of pixels, and actions are essentially patterns of inconsistent motions between the foreground and the background. To well distingui…