We introduce SuperClass, a super simple classification method for vision-language pre-training on image-text data. Unlike its contrastive counterpart CLIP who contrast with a text encoder, SuperClass directly utilizes tokenized raw text as supervised classification labels, without the need for additional text filtering or selection. Due to the absence of the text encoding as contrastive target, SuperClass does not require a text encoder and does not need to maintain a large batch size as CLIP does. SuperClass demonstrated superior performance on various downstream tasks, including classic computer vision benchmarks and vision language downstream tasks. We further explored the scaling behavior of SuperClass on model size, training length, or data size, and reported encouraging results and comparisons to CLIP. https://github.com/x-cls/superclass
Using Left and Right Brains Together: Towards Vision and Language Planning
Southern University of Science and Technology（南方科技大学）；Microsoft Research Asia（微软亚洲研究院）；The Hong Kong University of Science and Technology（香港科技大学）；Xi’an Jiaotong University（西安交通大学）；City University of Hong Kong（香港城市大学）
What's left can't be right -- The remaining positional incompetence of contrastive vision-language models
SFI Centre for Research Training in Machine Learning at University College Dublin（都柏林大学学院SFI机器学习研究培训中心）；School of Computer Science, University College Dublin（都柏林大学学院计算机科学学院）；School of Computer Science and Statistics, Trinity College Dublin（都柏林圣三一学院计算机科学与统计学院）
ProEdit: Inversion-based Editing From Prompts Done Right
Sun Yat-sen University（中山大学）；CUHK MMLab（香港中文大学MMLab）；College of Computing and Data Science, Nanyang Technological University（南洋理工大学计算机与数据科学学院）；The University of Hong Kong（香港大学）；Key Laboratory of Machine Intelligence and Advanced Computing, Ministry of Education, China（中国教育部机器智能与高级计算重点实验室）
$\left|\,\circlearrowright\,\boxed{\text{BUS}}\,\right|$: A Large and Diverse Multimodal Benchmark for evaluating the ability of Vision-Language Models to understand Rebus Puzzles
Tredence Inc.（特伦德公司）；Indian Institute of Technology Kharagpur（印度理工学院克拉格浦尔分校）；Inria Paris-Rocquencourt（巴黎-罗克琴库特研究所）；Rajiv Gandhi University（拉吉夫·甘地大学）；Tsinghua University（清华大学）；Palmer Research Laboratories（帕勒姆研究实验室）
Elastic Weight Consolidation Done Right for Continual Learning
School of Artificial Intelligence, Sun Yat-sen University, China（中山大学人工智能学院，中国）；Key Laboratory of Intelligent Assessment Technology for Sustainable Tourism, Ministry of Culture and Tourism, Sun Yat-sen University（文化旅游可持续发展智能评估技术重点实验室，文化部，中山大学）；Key Laboratory of Machine Intelligence and Advanced Computing, Ministry of Education, China（机器智能与高级计算重点实验室，教育部，中国）
Beyond Masking: Demystifying Token-Based Pre-Training for Vision Transformers
University of Chinese Academy of Sciences（中国科学院大学）；Huawei Inc.（华为公司）；Huazhong University of Science and Technology（华中科技大学）；Chinese Academy of Sciences（中国科学院）
The Right Spin: Learning Object Motion from Rotation-Compensated Flow Fields
TU Berlin（柏林工业大学）；University of Massachusetts（马萨诸塞大学）；Inria（法国国家信息与自动化研究所）；École Normale Supérieure（巴黎高等师范学院）；CNRS（法国国家科学研究中心）；PSL Research University（巴黎文理研究大学）；Univ. Grenoble Alpes（格勒诺布尔阿尔卑斯大学）；Grenoble INP（格勒诺布尔理工学院）
Prescribing the Right Remedy: Mitigating Hallucinations in Large Vision-Language Models via Targeted Instruction Tuning
Beijing Key Lab of Traffic Data Analysis and Mining（北京交通大数据分析与挖掘重点实验室）；Beijing Jiaotong University（北京交通大学）；School of Information Technology and Management（信息科学技术学院）
Cross from Left to Right Brain: Adaptive Text Dreamer for Vision-and-Language Navigation
Fudan University（复旦大学）；Shanghai AI Laboratory（上海人工智能实验室）；School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院）；MAIS, Institute of Automation of Chinese Academy of Sciences（中国科学院自动化研究所MAIS）；Mohamed bin Zayed University of Artificial Intelligence（穆罕默德·本·扎耶德人工智能大学）；University of Science and Technology of China（中国科学技术大学）；TeleAI, China Telecom Corp Ltd（中国电信TeleAI）
