most citedDisentangled Counterfactual Learning for Physical Audiovisual Commonsense Reasoning

2 citations · 3 across the 5 of their papers we have counts for

collaborators

5 papers

cs.CV2024

Decomposed Vector-Quantized Variational Autoencoder for Human Grasp Generation

Zhe Zhao, Mengshi Qi, Huadong Ma

Generating realistic human grasps is a crucial yet challenging task for applications involving object manipulation in computer graphics and robotics. Existing methods often struggl…

cs.CV20241 cited

Mutual Distillation Learning For Person Re-Identification

Huiyuan Fu, Kuilong Cui, Chuanming Wang +2

With the rapid advancements in deep learning technologies, person re-identification (ReID) has witnessed remarkable performance improvements. However, the majority of prior works h…

cs.CV2024

Uncovering the human motion pattern: Pattern Memory-based Diffusion Model for Trajectory Prediction

Yuxin Yang, Pengfei Zhu, Mengshi Qi +1

Human trajectory forecasting is a critical challenge in fields such as robotics and autonomous driving. Due to the inherent uncertainty of human actions and intentions in real-worl…

cs.CV2024

Multi-Stage Contrastive Regression for Action Quality Assessment

Qi An, Mengshi Qi, Huadong Ma

In recent years, there has been growing interest in the video-based action quality assessment (AQA). Most existing methods typically solve AQA problem by considering the entire vid…

cs.CV20232 cited

Disentangled Counterfactual Learning for Physical Audiovisual Commonsense Reasoning

Changsheng Lv, Shuai Zhang, Yapeng Tian +2

In this paper, we propose a Disentangled Counterfactual Learning~(DCL) approach for physical audiovisual commonsense reasoning. The task aims to infer objects' physics commonsense…