10 citations · 22 across the 9 of their papers we have counts for
9 papers
Mug-STAN: Adapting Image-Language Pretrained Models for General Video Understanding
Ruyang Liu, Jingjia Huang, Wei Gao +2
Large-scale image-language pretrained models, e.g., CLIP, have demonstrated remarkable proficiency in acquiring general multi-modal knowledge through web-scale image-text data. Des…
Associating Spatially-Consistent Grouping with Text-supervised Semantic Segmentation
Yabo Zhang, Zihao Wang, Jun Hao Liew +4
In this work, we investigate performing semantic segmentation solely through the training on image-sentence pairs. Due to the lack of dense annotations, existing text-supervised me…
Revisiting Temporal Modeling for CLIP-based Image-to-Video Knowledge Transferring
Ruyang Liu, Jingjia Huang, Ge Li +3
Image-text pretrained models, e.g., CLIP, have shown impressive general multi-modal knowledge learned from large-scale image-text data pairs, thus attracting increasing attention f…
Temporal Perceiving Video-Language Pre-training
Fan Ma, Xiaojie Jin, Heng Wang +4
Video-Language Pre-training models have recently significantly improved various multi-modal downstream tasks. Previous dominant works mainly adopt contrastive learning to achieve g…
Class Prototype-based Cleaner for Label Noise Learning
Jingjia Huang, Yuanqi Chen, Jiashi Feng +1
Semi-supervised learning based methods are current SOTA solutions to the noisy-label learning problem, which rely on learning an unsupervised label cleaner first to divide the trai…
Knowledge Guided Bidirectional Attention Network for Human-Object Interaction Detection
Jingjia Huang, Baixiang Yang
Human Object Interaction (HOI) detection is a challenging task that requires to distinguish the interaction between a human-object pair. Attention based relation parsing is a popul…
Clover: Towards A Unified Video-Language Alignment and Fusion Model
Jingjia Huang, Yinan Li, Jiashi Feng +3
Building a universal Video-Language model for solving various video understanding tasks (\emph{e.g.}, text-video retrieval, video question answering) is an open challenge to the ma…
ARMIN: Towards a More Efficient and Light-weight Recurrent Memory Network
Zhangheng Li, Jia-Xing Zhong, Jingjia Huang +3
In recent years, memory-augmented neural networks(MANNs) have shown promising power to enhance the memory ability of neural networks for sequential processing tasks. However, previ…
A Self-Adaptive Proposal Model for Temporal Action Detection based on Reinforcement Learning
Jingjia Huang, Nannan Li, Tao Zhang +1
Existing action detection algorithms usually generate action proposals through an extensive search over the video at multiple temporal scales, which brings about huge computational…