arXiv Computer Vision September 1, 2026 By Jinghao Liu, Xingrun Liu, Gengchen Sun, Han Xiao, Xingyu Chen, Yuhui Deng

Learning to Ground Before Reading: Unified PCB Engineering Drawing Parsing with Compact Vision-Language Models

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arXiv AI

Jun 11

LASA: A Weak Supervision Method for Open-Vocabulary Scene Sketch Semantic Segmentation

arXiv:2606. 11837v1 Announce Type: cross Abstract: Open-vocabulary scene sketch semantic segmentation aims to assign dense semantic labels to sparse line drawings based on flexible category vocabularies specified at inference time, without relying on pixel-level annotations during training.

By Liwen Yi, Xianlin Zhang, Yue Zhang, Yue Ming, Xueming Li

llms computer-vision multimodal safety

arXiv Computation and Language

Sep 1

ReVA: A Region-Aware Visual Assistant for Visually Grounded Question Answering

arXiv:2608.28707v1 Announce Type: new Abstract: Multimodal Large Language Models (MLLMs) have achieved remarkable progress in Visual Question Answering (VQA), yet they continue to struggle with quest...

By Anoop Senthil

llms rag computer-vision nlp multimodal safety

arXiv Computation and Language

Aug 25

Learning to Look Again: Loss-Gap Supervision for Free-form Crop Routing in Vision-Language Models

arXiv:2608.21762v1 Announce Type: cross Abstract: Vision-language models (VLMs) fail many detail-centric questions for a concrete reason: the answer is visible in the image, yet lost after the image...

By Jinchang Zhu, Rong Fu, Yi Ding, Chenghao Wu, Ying Liu, Menglin Yang

llms computer-vision multimodal benchmarks

arXiv Machine Learning

Jun 30

DataComp-VLM: Improved Open Datasets for Vision-Language Models

arXiv:2606. 28551v1 Announce Type: cross Abstract: Building performant Vision-Language Models (VLMs) requires carefully curating large-scale training datasets, yet the community lacks systematic benchmarks for evaluating such curation strategies.

By Matteo Farina, Vishaal Udandarao, Thao Nguyen, Selim Kuzucu, Maximilian B\"other, Andreas Hochlehnert, Adhiraj Ghosh, Marianna Nezhurina, Karsten Roth, Joschka Struber, Yuhui Zhang, Sebastian Dziadzio, Elaine Sui, Soumya Jahagirdar, Dhruba Ghosh, Hasan Hammoud, Thomas De Min, Simone Caldarella, Jehanzeb Mirza, Sedrick Keh, Mehdi Cherti, Hilde Kuehne, Bernt Schiele, Serena Yeung-Levy, Muhammad Ferjad Naeem, Federico Tombari, Ana Klimovic, Elisa Ricci, Matthias Bethge, Sewoong Oh, Ameya Prabhu, Alessio Tonioni, Jenia Jitsev, Massimiliano Mancini, Ludwig Schmidt, Nikhil Parthasarathy

llms multimodal benchmarks

arXiv AI

Jun 2

CLIP Tricks You: Training-free Token Pruning for Efficient Pixel Grounding in Large VIsion-Language Models

arXiv:2605. 13178v2 Announce Type: replace-cross Abstract: In large vision-language models, visual tokens typically constitute the majority of input tokens, leading to substantial computational overhead.

By Sangin Lee, Yukyung Choi

llms fine-tuning efficiency multimodal

arXiv Computer Vision

Sep 1

Instruction Distillation: Text Instructions as Visual Examples

arXiv:2608.28696v1 Announce Type: new Abstract: Visual in-context learning (ICL) with multimodal large language models (MLLMs) is effective for fine-grained visual classification, but each retrieved...

By Hardik Jindal, Soumyabrata Pal, Sayak Ray Chowdhury

llms efficiency multimodal benchmarks