37 citations · 103 across the 18 of their papers we have counts for
18 papers
Improving Human Image Animation via Semantic Representation Alignment
Chang Liu, Mengting Chen, Yixuan Huang +5
The field of image-to-video generation has made remarkable progress. However, challenges such as human limb twisting and facial distortion persist, especially when generating long…
Unsupervised Domain Adaptation via Similarity-based Prototypes for Cross-Modality Segmentation
Ziyu Ye, Chen Ju, Chaofan Ma +1
Deep learning models have achieved great success on various vision challenges, but a well-trained model would face drastic performance degradation when applied to unseen data. Sinc…
Contrast-Unity for Partially-Supervised Temporal Sentence Grounding
Haicheng Wang, Chen Ju, Weixiong Lin +4
Temporal sentence grounding aims to detect event timestamps described by the natural language query from given untrimmed videos. The existing fully-supervised setting achieves grea…
DENOISER: Rethinking the Robustness for Open-Vocabulary Action Recognition
Haozhe Cheng, Cheng Ju, Haicheng Wang +5
As one of the fundamental video tasks in computer vision, Open-Vocabulary Action Recognition (OVAR) recently gains increasing attention, with the development of vision-language pre…
Audio-Visual Segmentation via Unlabeled Frame Exploitation
Jinxiang Liu, Yikun Liu, Fei Zhang +3
Audio-visual segmentation (AVS) aims to segment the sounding objects in video frames. Although great progress has been witnessed, we experimentally reveal that current methods reac…
AttrSeg: Open-Vocabulary Semantic Segmentation via Attribute Decomposition-Aggregation
Chaofan Ma, Yuhuan Yang, Chen Ju +3
Open-vocabulary semantic segmentation is a challenging task that requires segmenting novel object categories at inference time. Recent studies have explored vision-language pre-tra…
Audio-aware Query-enhanced Transformer for Audio-Visual Segmentation
Jinxiang Liu, Chen Ju, Chaofan Ma +3
The goal of the audio-visual segmentation (AVS) task is to segment the sounding objects in the video frames using audio cues. However, current fusion-based methods have the perform…
Multi-Modal Prototypes for Open-World Semantic Segmentation
Yuhuan Yang, Chaofan Ma, Chen Ju +4
In semantic segmentation, generalizing a visual system to both seen categories and novel categories at inference time has always been practically valuable yet challenging. To enabl…
Annotation-free Audio-Visual Segmentation
Jinxiang Liu, Yu Wang, Chen Ju +3
The objective of Audio-Visual Segmentation (AVS) is to localise the sounding objects within visual scenes by accurately predicting pixel-wise segmentation masks. To tackle the task…
Multi-modal Prompting for Low-Shot Temporal Action Localization
Chen Ju, Zeqian Li, Peisen Zhao +5
In this paper, we consider the problem of temporal action localization under low-shot (zero-shot & few-shot) scenario, with the goal of detecting and classifying the action instanc…
DiffusionSeg: Adapting Diffusion Towards Unsupervised Object Discovery
Chaofan Ma, Yuhuan Yang, Chen Ju +5
Learning from a large corpus of data, pre-trained models have achieved impressive progress nowadays. As popular generative pre-training, diffusion models capture both low-level vis…
Constraint and Union for Partially-Supervised Temporal Sentence Grounding
Chen Ju, Haicheng Wang, Jinxiang Liu +5
Temporal sentence grounding aims to detect the event timestamps described by the natural language query from given untrimmed videos. The existing fully-supervised setting achieves…
Distilling Vision-Language Pre-training to Collaborate with Weakly-Supervised Temporal Action Localization
Chen Ju, Kunhao Zheng, Jinxiang Liu +5
Weakly-supervised temporal action localization (WTAL) learns to detect and classify action instances with only category labels. Most methods widely adopt the off-the-shelf Classifi…
Exploiting Transformation Invariance and Equivariance for Self-supervised Sound Localisation
Jinxiang Liu, Chen Ju, Weidi Xie +1
We present a simple yet effective self-supervised framework for audio-visual representation learning, to localize the sound source in videos. To understand what enables to learn us…
Prompting Visual-Language Models for Efficient Video Understanding
Chen Ju, Tengda Han, Kunhao Zheng +2
Image-based visual-language (I-VL) pre-training has shown great success for learning joint visual-textual representations from large-scale web data, revealing remarkable ability fo…
Adaptive Mutual Supervision for Weakly-Supervised Temporal Action Localization
Chen Ju, Peisen Zhao, Siheng Chen +3
Weakly-supervised temporal action localization aims to localize actions in untrimmed videos with only video-level action category labels. Most of previous methods ignore the incomp…
Point-Level Temporal Action Localization: Bridging Fully-supervised Proposals to Weakly-supervised Losses
Chen Ju, Peisen Zhao, Ya Zhang +2
Point-Level temporal action localization (PTAL) aims to localize actions in untrimmed videos with only one timestamp annotation for each action instance. Existing methods adopt the…
Bottom-Up Temporal Action Localization with Mutual Regularization
Peisen Zhao, Lingxi Xie, Chen Ju +3
Recently, temporal action localization (TAL), i.e., finding specific action segments in untrimmed videos, has attracted increasing attentions of the computer vision community. Stat…