activity
20142024
most citedBag of Visual Words and Fusion Methods for Action Recognition: Comprehensive Study and Good Practice

128 citations · 137 across the 10 of their papers we have counts for

collaborators

10 papers

cs.CV2024

Two in One Go: Single-stage Emotion Recognition with Decoupled Subject-context Transformer

Xinpeng Li, Teng Wang, Jian Zhao +5

Emotion recognition aims to discern the emotional state of subjects within an image, relying on subject-centric and contextual visual cues. Current approaches typically follow a tw…

cs.CL2024

MIPS at SemEval-2024 Task 3: Multimodal Emotion-Cause Pair Extraction in Conversations with Multimodal Language Models

Zebang Cheng, Fuqiang Niu, Yuxiang Lin +3

This paper presents our winning submission to Subtask 2 of SemEval 2024 Task 3 on multimodal emotion cause analysis in conversations. We propose a novel Multimodal Emotion Recognit…

cs.CL20242 cited

A Challenge Dataset and Effective Models for Conversational Stance Detection

Fuqiang Niu, Min Yang, Ang Li +3

Previous stance detection studies typically concentrate on evaluating stances within individual instances, thereby exhibiting limitations in effectively modeling multi-party discus…

cs.CV2024

3D Landmark Detection on Human Point Clouds: A Benchmark and A Dual Cascade Point Transformer Framework

Fan Zhang, Shuyi Mao, Qing Li +1

3D landmark detection plays a pivotal role in various applications such as 3D registration, pose estimation, and virtual try-on. While considerable success has been achieved in 2D…

cs.CV20242 cited

MIMIC: Mask Image Pre-training with Mix Contrastive Fine-tuning for Facial Expression Recognition

Fan Zhang, Xiaobao Guo, Xiaojiang Peng +1

Cutting-edge research in facial expression recognition (FER) currently favors the utilization of convolutional neural networks (CNNs) backbone which is supervisedly pre-trained on…

cs.LG20233 cited

The Snowflake Hypothesis: Training Deep GNN with One Node One Receptive field

Kun Wang, Guohao Li, Shilong Wang +6

Despite Graph Neural Networks demonstrating considerable promise in graph representation learning tasks, GNNs predominantly face significant issues with over-fitting and over-smoot…