Visual Perspective
Visual perspective, encompassing the understanding and representation of viewpoints, is a burgeoning research area focusing on how humans and machines perceive and interpret visual information from different angles. Current research emphasizes improving the generalization capabilities of models like Vision-Language Models (VLMs) and developing methods to mitigate biases, particularly cultural biases, in image understanding and analysis. This work is crucial for advancing artificial intelligence, particularly in applications requiring nuanced understanding of context and diverse perspectives, such as autonomous driving, human-robot interaction, and healthcare.
Papers
A Perspective for Adapting Generalist AI to Specialized Medical AI Applications and Their Challenges
Zifeng Wang, Hanyin Wang, Benjamin Danek, Ying Li, Christina Mack, Hoifung Poon, Yajuan Wang, Pranav Rajpurkar, Jimeng Sun
ByteNet: Rethinking Multimedia File Fragment Classification through Visual Perspectives
Wenyang Liu, Kejun Wu, Tianyi Liu, Yi Wang, Kim-Hui Yap, Lap-Pui Chau
From Distributional Robustness to Robust Statistics: A Confidence Sets Perspective
Gabriel Chan, Bart Van Parys, Amine Bennouna
SemSim: Revisiting Weak-to-Strong Consistency from a Semantic Similarity Perspective for Semi-supervised Medical Image Segmentation
Shiao Xie, Hongyi Wang, Ziwei Niu, Hao Sun, Shuyi Ouyang, Yen-Wei Chen, Lanfen Lin
Inverse Problems and Data Assimilation: A Machine Learning Approach
Eviatar Bach, Ricardo Baptista, Daniel Sanz-Alonso, Andrew Stuart
Evaluating Semantic Variation in Text-to-Image Synthesis: A Causal Perspective
Xiangru Zhu, Penglei Sun, Yaoxian Song, Yanghua Xiao, Zhixu Li, Chengyu Wang, Jun Huang, Bei Yang, Xiaoxiao Xu
Revisiting and Benchmarking Graph Autoencoders: A Contrastive Learning Perspective
Jintang Li, Ruofan Wu, Yuchang Zhu, Huizhe Zhang, Xinzhou Jin, Guibin Zhang, Zulun Zhu, Zibin Zheng, Liang Chen