Computer vision

Detection, segmentation, depth and recognition research, plus the vision backbones that keep displacing the last generation.

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arXiv AI

Jun 8

Which Anatomy Matters Under Limited Labels? A Data-Efficient Anatomy-Aware Benchmark for Cardiac Pathology Prediction

arXiv:2606. 06509v1 Announce Type: cross Abstract: Numerous medical imaging problems must be solved under limited labels and constrained compute, yet it remains unclear whether performance gains are driven mainly by more expressive models or by better representation of clinically meaningful anatomy.

By Himanshu Singh

computer-vision benchmarks

arXiv AI

Jun 8

Attention-Guided Autoencoder Fusion for Insulator Defect Detection Using UAV Transmission-Line Imaging

arXiv:2606. 06536v1 Announce Type: cross Abstract: Automated defect detection in high-voltage transmission-line insulators remains challenging due to severe class imbalance, large scale variation, and the small spatial extent of defect instances in Unmanned Aerial Vehicle (UAV) imagery.

By Malak Allam, Khaled Shaban, Ali Hamdi

computer-vision

arXiv AI

Jun 8

MMBU: A Massive Multi-modal Biomedical Understanding Benchmark to Probe the Perception Capabilities of Vision-Language Models

arXiv:2606. 06696v1 Announce Type: cross Abstract: Vision and language models (VLMs) hold immense promise to transform biomedical imaging workflows, from detecting lesions in chest X-rays to profiling cellular features in microscopy.

By Ryan D'Cunha, Alejandro Lozano, Xiaoxiao Sun, Daniel Vela Jarquin, Min Woo Sun, Josiah Aklilu, James Burgess, Yuhui Zhang, Ryan Nayebi, Paola Avila, Robayo, Jin Ye, Ming Hu, Zhongying Deng, Junjun He, Xin Chen, Yue Yao, Robert Tibshirani, Jeffrey J. Nirschl, Serena Yeung-Levy

llms computer-vision multimodal benchmarks

arXiv Machine Learning

Jun 8

LRMIL: Efficient Low-Resolution Multiple Instance Learning via High-Resolution Knowledge Distillation for Whole Slide Image Classification

arXiv:2606. 06864v1 Announce Type: cross Abstract: Multiple instance learning (MIL) has become a standard paradigm for whole slide image (WSI) analysis in digital pathology, as it enables slide-level prediction without dense annotations.

By Yonghan Shin, Won-Ki Jeong

rag computer-vision efficiency benchmarks

arXiv AI

Jun 8

ReclAIm: A Multi-Agent Framework for Monitoring and Correcting Performance Decline in Medical Imaging AI

arXiv:2510. 17004v2 Announce Type: replace-cross Abstract: Purpose: To develop and evaluate a multi-agent framework (ReclAIm) for automated monitoring, detection, and correction of performance decline in medical image classification models.

By Eleftherios Tzanis, Michail E. Klontzas

llms agents computer-vision fine-tuning

arXiv AI

Jun 8

MatterDoor: Sampling Zero-shot Spatio-semantic Priors using Generative Models

arXiv:2510. 11014v2 Announce Type: replace-cross Abstract: Autonomous robots often view rooms only partially, through a doorway, where the walls and scene structure hide the geometry and task-relevant semantics needed for safe navigation and goal-directed action.

By Subhransu S. Bhattacharjee, Hao Lu, Dylan Campbell, Rahul Shome

agents computer-vision robotics fine-tuning multimodal benchmarks

arXiv AI

Jun 8

ChemQuests: A Curated Chemistry Question-Answer Database Extracted from ChemRxiv papers

arXiv:2505. 05232v3 Announce Type: replace Abstract: The rapid expansion of chemistry literature poses significant challenges for researchers seeking to efficiently access domain-specific knowledge.

By Mahmoud Amiri, Thomas Bocklitz

llms computer-vision nlp fine-tuning

arXiv AI

Jun 6

Brick-Composer: Using MLLMs for Assembly with Diverse Bricks

arXiv:2606. 05445v1 Announce Type: new Abstract: We dream of AI agents that can read arbitrary designs and construct real-world objects from reusable building blocks.

By Jiateng Liu, Bingxuan Li, Zhenhailong Wang, Rushi Wang, Kaiwen Hong, Cheng Qian, Jiayu Liu, Denghui Zhang, Katherine Driggs-Campbell, Manling Li, Heng Ji

llms agents computer-vision multimodal benchmarks

arXiv AI

Jun 6

Adversarial Agents: Black-Box Evasion Attacks with Reinforcement Learning

arXiv:2503. 01734v3 Announce Type: replace-cross Abstract: Attacks on machine learning models have been extensively studied through stateless optimization.

By Kyle Domico, Jean-Charles Noirot Ferrand, Ryan Sheatsley, Eric Pauley, Josiah Hanna, Patrick McDaniel

agents computer-vision reinforcement-learning benchmarks safety

arXiv AI

Jun 6

Image Generators are Generalist Vision Learners

arXiv:2604. 20329v3 Announce Type: replace-cross Abstract: Recent works show that image and video generators exhibit zero-shot visual understanding behaviors, in a way reminiscent of how LLMs develop emergent capabilities of language understanding and reasoning from generative pretraining.

By Valentin Gabeur, Shangbang Long, Songyou Peng, Paul Voigtlaender, Shuyang Sun, Yanan Bao, Karen Truong, Zhicheng Wang, Wenlei Zhou, Jonathan T. Barron, Kyle Genova, Nithish Kannen, Sherry Ben, Yandong Li, Mandy Guo, Suhas Yogin, Yiming Gu, Huizhong Chen, Oliver Wang, Saining Xie, Howard Zhou, Kaiming He, Thomas Funkhouser, Jean-Baptiste Alayrac, Radu Soricut

llms diffusion computer-vision benchmarks

arXiv AI

Jun 6

Towards Generalization of Block Attention via Automatic Segmentation and Block Distillation

arXiv:2605. 15913v4 Announce Type: replace-cross Abstract: Block attention, which processes the input as separate blocks that cannot attend to one another, offers significant potential to improve KV cache reuse in long-context scenarios such as Retrieval-Augmented Generation (RAG).

By Shuaiyi Li, Zhisong Zhang, Yan Wang, Lei Zhu, Dongyang Ma, Chenlong Deng, Yang Deng, Wai Lam

rag computer-vision fine-tuning efficiency benchmarks

arXiv Machine Learning

Jun 5

Is Diversity All You Need for Scalable Robotic Manipulation?

arXiv:2507. 06219v2 Announce Type: replace-cross Abstract: Data scaling has driven remarkable success in foundation models for Natural Language Processing (NLP) and Computer Vision (CV), yet the principles of effective data scaling in robotic manipulation remain insufficiently understood.

By Modi Shi, Li Chen, Jin Chen, Yuxiang Lu, Chiming Liu, Guanghui Ren, Ping Luo, Di Huang, Maoqing Yao, Hongyang Li

computer-vision nlp robotics fine-tuning

arXiv Machine Learning

Jun 5

Noise-Adaptive Regularization for Robust Multi-Label Remote Sensing Image Classification

arXiv:2601. 08446v2 Announce Type: replace-cross Abstract: The development of reliable methods for multi-label classification (MLC) has become a prominent research direction in remote sensing (RS).

By Tom Burgert, Julia Henkel, Beg\"um Demir

computer-vision

arXiv Machine Learning

Jun 5

On the training of physics-informed neural operators for solving parametric partial differential equations

arXiv:2606. 06164v1 Announce Type: new Abstract: Physics-informed neural operators (PINOs) aim to learn solution operators for partial differential equations by using the governing physics as supervision, rather than relying solely on paired input-output simulation data.

By Nanxi Chen, Chuanjie Cui, Airong Chen, Sifan Wang, Rujin Ma

llms computer-vision benchmarks

arXiv Machine Learning

Jun 5

RREDCoT: Segment-Level Reward Redistribution for Reasoning Models

arXiv:2606. 06475v1 Announce Type: new Abstract: Recent advancements in reasoning language models have been driven by Reinforcement Learning (RL) fine-tuning.

By Mykyta Ielanskyi, Kajetan Schweighofer, Lukas Aichberger, Sepp Hochreiter

llms computer-vision reinforcement-learning fine-tuning

arXiv Machine Learning

Jun 5

End-to-End Subgraph Detection with GraphDETR

arXiv:2606. 06364v1 Announce Type: new Abstract: Subgraph detection seeks to identify whether and where instances of query patterns occur within a larger graph.

By Dexiong Chen, Till Hendrik Schulz, Karsten Borgwardt

llms computer-vision

arXiv Machine Learning

Jun 5

Vision Hopfield Memory Networks

arXiv:2603. 25157v2 Announce Type: replace Abstract: Recent vision and multimodal foundation backbones, such as Transformer families and state-space models like Mamba, have achieved remarkable progress, enabling unified modeling across images, text, and beyond.

By Jianfeng Wang, Amine M'Charrak, Luk Koska, Xiangtao Wang, Daniel Petriceanu, Ruizhi Wang, Michael Bumbar, Luca Pinchetti, Thomas Lukasiewicz

llms computer-vision multimodal benchmarks safety

arXiv Machine Learning

Jun 5

GridPE: A Grid Cell-Inspired Unified Position Embedding for Arbitrary-Dimensional Spaces

arXiv:2406. 07049v3 Announce Type: replace-cross Abstract: Understanding spatial relationships across all dimensions is fundamental for intelligent systems.

By Boyang Li, Yulin Wu, Nuoxian Huang, Wenjia Zhang

rag computer-vision robotics

arXiv Machine Learning

Jun 5

Exact Linear Attention

arXiv:2605. 18848v3 Announce Type: replace Abstract: This paper introduces Exact Linear Attention (ELA), a mechanism that achieves linear computational complexity for Transformer attention by exploiting the exact decomposition property of kernel functions, thereby eliminating approximation error.

By Weinuo Ou

llms computer-vision reinforcement-learning efficiency safety

arXiv Machine Learning

Jun 5

Learned Response-Field Inertia Operator for HEC-RAS 2D Water-Surface Elevation Prediction

arXiv:2606. 06385v1 Announce Type: new Abstract: This article presents a cross-dataset evaluation of learned native-cell surrogate models for solver-consistent water-surface elevation (WSE) prediction in HEC-RAS 2D.

By Edward Holmberg, Elias Ioup, Md Meftahul Ferdaus, Mahdi Abdelguerfi, Julian Simeonov

computer-vision benchmarks

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