arXiv Computer Vision
Aug 31

From Perspective to Fisheye Depth Estimation and Open-Vocabulary Segmentation

The paper introduces Distortion Extenders (DEX), learnable parameters that adapt vision foundation models to fisheye cameras by modeling distortion coefficients and correcting distributional shifts between fisheye and perspective images. DEX is applied to monocular depth estimation and open‑vocabulary segmentation across convolutional and Transformer architectures, consistently outperforming baselines on indoor and outdoor fisheye datasets. Additionally, DEX activations can be decoded to obtain distortion coefficients, aiding camera calibration.

By Rit Gangopadhyay, Alex Wong
arXiv AI
Aug 7

DistMedVL: Distributional Vision-Language Alignment for Uncertainty-Aware Medical Image Segmentation

arXiv:2608. 05683v1 Announce Type: cross Abstract: Cross-modal alignment of visual and textual representations is fundamental to multimodal medical image understanding, yet remains hindered by uncertainty in both modalities under real-world clinical conditions.

By Jiaxuan Li, Qing Xu, Xiangjian He, Yue Li, Daokun Zhang, Fiseha B. Tesema, Rong Qu
arXiv AI
Aug 20

From Inference to Adaptation: A Unified Optimal Transport View of Vision Language Model

arXiv:2608. 18339v1 Announce Type: cross Abstract: Vision-language models (VLMs) have demonstrated remarkable zero-shot capabilities yet remain sensitive to real-world distribution shifts during inference.

By Qi Yu, Zhichen Zeng, Katherine Tieu, Xiyuan Yang, Ruizhong Qiu, Yuchen Yan, Lihui Liu, Yanjun Zhao, Lingjie Chen, Jingrui He, Hanghang Tong