189 citations · 201 across the 10 of their papers we have counts for
11 papers
Knowledge is Power: Advancing Few-shot Action Recognition with Multimodal Semantics from MLLMs
Jiazheng Xing, Chao Xu, Hangjie Yuan +4
Multimodal Large Language Models (MLLMs) have propelled the field of few-shot action recognition (FSAR). However, preliminary explorations in this area primarily focus on generatin…
CFSum: A Transformer-Based Multi-Modal Video Summarization Framework With Coarse-Fine Fusion
Yaowei Guo, Jiazheng Xing, Xiaojun Hou +5
Video summarization, by selecting the most informative and/or user-relevant parts of original videos to create concise summary videos, has high research value and consumer demand i…
Visual Object Tracking across Diverse Data Modalities: A Review
Mengmeng Wang, Teli Ma, Shuo Xin +5
Visual Object Tracking (VOT) is an attractive and significant research area in computer vision, which aims to recognize and track specific targets in video sequences where the targ…
TryOn-Adapter: Efficient Fine-Grained Clothing Identity Adaptation for High-Fidelity Virtual Try-On
Jiazheng Xing, Chao Xu, Yijie Qian +5
Virtual try-on focuses on adjusting the given clothes to fit a specific person seamlessly while avoiding any distortion of the patterns and textures of the garment. However, the cl…
FaceChain-ImagineID: Freely Crafting High-Fidelity Diverse Talking Faces from Disentangled Audio
Chao Xu, Yang Liu, Jiazheng Xing +8
In this paper, we abstract the process of people hearing speech, extracting meaningful cues, and creating various dynamically audio-consistent talking faces, termed Listening and I…
SDSTrack: Self-Distillation Symmetric Adapter Learning for Multi-Modal Visual Object Tracking
Xiaojun Hou, Jiazheng Xing, Yijie Qian +8
Multimodal Visual Object Tracking (VOT) has recently gained significant attention due to its robustness. Early research focused on fully fine-tuning RGB-based trackers, which was i…
M2-CLIP: A Multimodal, Multi-task Adapting Framework for Video Action Recognition
Mengmeng Wang, Jiazheng Xing, Boyuan Jiang +6
Recently, the rise of large-scale vision-language pretrained models like CLIP, coupled with the technology of Parameter-Efficient FineTuning (PEFT), has captured substantial attrac…
Boosting Few-shot Action Recognition with Graph-guided Hybrid Matching
Jiazheng Xing, Mengmeng Wang, Yudi Ruan +6
Class prototype construction and matching are core aspects of few-shot action recognition. Previous methods mainly focus on designing spatiotemporal relation modeling modules or co…
MA-FSAR: Multimodal Adaptation of CLIP for Few-Shot Action Recognition
Jiazheng Xing, Chao Xu, Mengmeng Wang +5
Applying large-scale vision-language pre-trained models like CLIP to few-shot action recognition (FSAR) can significantly enhance both performance and efficiency. While several stu…
Revisiting the Spatial and Temporal Modeling for Few-shot Action Recognition
Jiazheng Xing, Mengmeng Wang, Yong Liu +1
Spatial and temporal modeling is one of the most core aspects of few-shot action recognition. Most previous works mainly focus on long-term temporal relation modeling based on high…
ActionCLIP: A New Paradigm for Video Action Recognition
Mengmeng Wang, Jiazheng Xing, Yong Liu
The canonical approach to video action recognition dictates a neural model to do a classic and standard 1-of-N majority vote task. They are trained to predict a fixed set of predef…