Visual engagement in social media platforms comprises interactions with photo posts including comments, shares, and likes. In this paper, we leverage such visual engagement clues as supervisory signals for representation learning. However, learning from engagement signals is non-trivial as it is not clear how to bridge the gap between low-level visual information and high-level social interactions. We present VisE, a weakly supervised learning approach, which maps social images to pseudo labels derived by clustered engagement signals. We then study how models trained in this way benefit subjective downstream computer vision tasks such as emotion recognition or political bias detection. Through extensive studies, we empirically demonstrate the effectiveness of VisE across a diverse set of classification tasks beyond the scope of conventional recognition.
Department of Computer Science（计算机科学系）；Center for Machine Vision（机器视觉中心）；Signal Analysis, University of Oulu, Finland（信号分析中心，奥卢大学，芬兰）；State Key Lab of Blockchain（区块链国家重点实验室）；Data Security, Zhejiang University, China（数据安全，浙江大学，中国）
Engagement Prediction of Short Videos with Large Multimodal Models
East China Normal University（华东师范大学）；Shanghai Jiao Tong University（上海交通大学）；City University of Hong Kong（香港城市大学）；Shanghai University of Electric Power（上海电力大学）
Perception Without Engagement: Dissecting the Causal Discovery Deficit in LMMs
Harbin Institute of Technology（哈尔滨工业大学）；Pengcheng Laboratory（鹏城实验室）；National University of Singapore（新加坡国立大学）；Peking University（北京大学）；Harvard University（哈佛大学）
Exploring Cluster Analysis in Nelore Cattle Visual Score Attribution
Universidade Católica Dom Bosco（多姆博斯科天主教大学）；Kerow Soluções de Precisao（凯罗精准解决方案公司）；Universidade Estadual de Mato Grosso do Sul（南马托格罗索州立大学）；Embrapa Beef Cattle（巴西农牧业研究院肉牛研究中心）；Universidade Federal de Mato Grosso do Sul（南马托格罗索联邦大学）
VisualRWKV: Exploring Recurrent Neural Networks for Visual Language Models
College of Computer Science and Software Engineering, Shenzhen University（深圳大学计算机与软件学院）；Guangdong Laboratory of Artificial Intelligence and Digital Economy (SZ)（广东省人工智能与数字经济实验室（深圳））
Exploring Image Representation with Decoupled Classical Visual Descriptors
The MIx Group University of Birmingham, UK（米克斯小组英国伯明翰大学）；University of Cambridge Cambridge, UK（剑桥大学）；Allsee Technologies Ltd Birmingham, UK（Allsee技术有限公司）
Exploring adaptation of VideoMAE for Audio-Visual Diarization & Social @ Ego4d Looking at me Challenge
Beijing University of Posts and Telecommunications（北京邮电大学）；Nanjing University（南京大学）
在本报告中，我们介绍了将预训练的视频掩码自动编码器（VideoMAE）转移到Ego 4d Looking at me Challenge的自我中心任务。VideoMAE是用于自我监督视频预训练的数据高效预训练模型，可以轻松地转移到下游任务。我们表明，从VideoMAE传输的表示具有良好的时空建模和捕捉小动作的能力。我们只需要使用以自我为中心的数据来训练基于VideoMAE的10个epoch，其中VideoMAE是由从第三人的视角获取的普通视频进行预训练的，并且我们可以得到比Ego 4d Looking at me Challenge的基线更好的结果。
Exploring Interpretability for Visual Prompt Tuning with Cross-layer Concepts
School of Computer Science and Technology, Tongji University（同济大学计算机科学与技术学院）；Microsoft Research Asia（微软亚洲研究院）；School of Telecommunication and Engineering, Xidian University（西安电子科技大学电信与工程学院）
Mixture-of-Visual-Thoughts: Exploring Context-Adaptive Reasoning Mode Selection for General Visual Reasoning
Fudan University（复旦大学）；Alibaba Group Holding Limited（阿里巴巴集团控股有限公司）；Future Living Lab of Alibaba（阿里巴巴未来生活实验室）；University of Southern California（南加州大学）；Shanghai Innovation Institute（上海创新研究院）
