70 citations · 135 across the 11 of their papers we have counts for
12 papers
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…
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…
Inferring Fluid Dynamics via Inverse Rendering
Jinxian Liu, Ye Chen, Bingbing Ni +2
Humans have a strong intuitive understanding of physical processes such as fluid falling by just a glimpse of such a scene picture, i.e., quickly derived from our immersive visual…
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…
AudioEar: Single-View Ear Reconstruction for Personalized Spatial Audio
Xiaoyang Huang, Yanjun Wang, Yang Liu +4
Spatial audio, which focuses on immersive 3D sound rendering, is widely applied in the acoustic industry. One of the key problems of current spatial audio rendering methods is the…
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…
A 3D Mesh-based Lifting-and-Projection Network for Human Pose Transfer
Jinxiang Liu, Yangheng Zhao, Siheng Chen +1
Human pose transfer has typically been modeled as a 2D image-to-image translation problem. This formulation ignores the human body shape prior in 3D space and inevitably causes imp…
Self-Prediction for Joint Instance and Semantic Segmentation of Point Clouds
Jinxian Liu, Minghui Yu, Bingbing Ni +1
We develop a novel learning scheme named Self-Prediction for 3D instance and semantic segmentation of point clouds. Distinct from most existing methods that focus on designing conv…
Modeling Point Clouds with Self-Attention and Gumbel Subset Sampling
Jiancheng Yang, Qiang Zhang, Bingbing Ni +4
Geometric deep learning is increasingly important thanks to the popularity of 3D sensors. Inspired by the recent advances in NLP domain, the self-attention transformer is introduce…
Adversarial Attack and Defense on Point Sets
Jiancheng Yang, Qiang Zhang, Rongyao Fang +3
Emergence of the utility of 3D point cloud data in safety-critical vision tasks (e.g., ADAS) urges researchers to pay more attention to the robustness of 3D representations and dee…