Video description entails automatically generating coherent natural language sentences that narrate the content of a given video. We introduce CLearViD, a transformer-based model for video description generation that leverages curriculum learning to accomplish this task. In particular, we investigate two curriculum strategies: (1) progressively exposing the model to more challenging samples by gradually applying a Gaussian noise to the video data, and (2) gradually reducing the capacity of the network through dropout during the training process. These methods enable the model to learn more robust and generalizable features. Moreover, CLearViD leverages the Mish activation function, which provides non-linearity and non-monotonicity and helps alleviate the issue of vanishing gradients. Our extensive experiments and ablation studies demonstrate the effectiveness of the proposed model. The results on two datasets, namely ActivityNet Captions and YouCook2, show that CLearViD significantly outperforms existing state-of-the-art models in terms of both accuracy and diversity metrics.
CurEvo: Curriculum-Guided Self-Evolution for Video Understanding
Huazhong University of Science and Technology（华中科技大学）；La Trobe University（拉特罗布大学）；Beijing Institute of Computer Technology and Applications（北京计算机技术与应用研究院）
Answering from Sure to Uncertain: Uncertainty-Aware Curriculum Learning for Video Question Answering
School of Computing and Information Systems, The University of Melbourne（墨尔本大学计算与信息系统学院）；Computer Science & Software Engineering, University of Western Australia（西澳大学计算机科学与软件工程系）；School of Computing and Communications, Lancaster University（兰卡斯特大学计算与通讯学院）；Department of Data Science & AI, Monash University（莫纳什大学数据科学与人工智能系）
VideoTG-R1: Boosting Video Temporal Grounding via Curriculum Reinforcement Learning on Reflected Boundary Annotations
University of Science and Technology of China（中国科学技术大学）；Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）；Beihang University（北京航空航天大学）；Shanghai Jiao Tong University（上海交通大学）；Zhejiang University（浙江大学）；State Key Laboratory for Novel Software Technology, Nanjing University（南京大学新型软件技术国家重点实验室）；Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究所）
Hierarchical Curriculum Learning for Surgical Video Generation: Focusing on Bilateral Axillo-Breast Approach Robotic Thyroidectomy Datasets
Seoul National University College of Medicine（首尔国立大学医学院）；Seoul National University Hospital（首尔国立大学医院）；YONSEI WITH-YOU Surgery Clinic（延世WITH-YOU外科诊所）；Institute of Convergence Medicine with Innovative Technology, Seoul National University Hospital（首尔国立大学医院创新技术融合医学研究所）
