5 papers
Do Instance Priors Help Weakly Supervised Semantic Segmentation?
Anurag Das, Anna Kukleva, Xinting Hu +2
Semantic segmentation requires dense pixel-level annotations, which are costly and time-consuming to acquire. To address this, we present SeSAM, a framework that uses a foundationa…
ClipTTT: CLIP-Guided Test-Time Training Helps LVLMs See Better
Mriganka Nath, Anurag Das, Jiahao Xie +1
Large vision-language models (LVLMs) tend to hallucinate, especially when visual inputs are corrupted at test time. We show that such corruptions act as additional distribution shi…
More Images, More Problems? A Controlled Analysis of VLM Failure Modes
Anurag Das, Adrian Bulat, Alberto Baldrati +4
Large Vision Language Models (LVLMs) have demonstrated remarkable capabilities, yet their proficiency in understanding and reasoning over multiple images remains largely unexplored…
MTA-CLIP: Language-Guided Semantic Segmentation with Mask-Text Alignment
Anurag Das, Xinting Hu, Li Jiang +1
Recent approaches have shown that large-scale vision-language models such as CLIP can improve semantic segmentation performance. These methods typically aim for pixel-level vision-…
Improving 2D Feature Representations by 3D-Aware Fine-Tuning
Yuanwen Yue, Anurag Das, Francis Engelmann +2
Current visual foundation models are trained purely on unstructured 2D data, limiting their understanding of 3D structure of objects and scenes. In this work, we show that fine-tun…