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20242026
most citedPrompt-CAM: Making Vision Transformers Interpretable for Fine-Grained Analysis

2 citations · 3 across the 9 of their papers we have counts for

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5 papers · 1 filter

cs.CV2026

Mind the Tail: Data Selection Through Sparse Concepts

Ziheng Zhang, Neha Hulkund, Colin Lee +7

Data selection reallocates training support across visual concepts, including objects, attributes, color, texture, and backgrounds. Different allocation strategies may produce vari…

cs.CL2026

Lost at the End: Primacy Bias in Multimodal Retrieval-Augmented Question Answering

Jieyuan Liu, Jianyang Gu, Shijie Chen +2

Knowledge-based visual question answering (KB-VQA) lets vision-language systems answer questions that exceed their parametric knowledge by conditioning a reader on passages retriev…

cs.CV2026

Leveraging Latent Visual Reasoning in Silence

Dongyao Zhu, Zhen Wang, Xi Xiao +7

Latent visual reasoning involves visual evidence more directly in multimodal reasoning by inserting continuous latent tokens before textual generation. However, the necessity of th…

cs.CV2026

TaxaAdapter: Vision Taxonomy Models are Key to Fine-grained Image Generation over the Tree of Life

Mridul Khurana, Amin Karimi Monsefi, Justin Lee +9

Accurately generating images across the Tree of Life is difficult: there are over 10M distinct species on Earth, many of which differ only by subtle visual traits. Despite the rema…

cs.CV2026

Lessons and Open Questions from a Unified Study of Camera-Trap Species Recognition Over Time

Sooyoung Jeon, Hongjie Tian, Lemeng Wang +7

Camera traps are vital for large-scale biodiversity monitoring, yet accurate automated analysis remains challenging due to diverse deployment environments. While the computer visio…