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 2

Parameter-Efficient Fine-Tuning of Large Pretrained Models for Instance Segmentation Tasks

arXiv:2606. 01947v1 Announce Type: cross Abstract: Research and applications in artificial intelligence have recently shifted with the rise of large pretrained models, which deliver state-of-the-art results across numerous tasks.

By Nermeen Abou Baker, David Rohrschneider, Uwe Handmann

llms computer-vision fine-tuning benchmarks

arXiv Machine Learning

Jun 2

Normalized Relevance Measure as a Unifying Framework to Explain Neural Network Latent Structures

arXiv:2606. 00557v1 Announce Type: new Abstract: To understand how a neural network (NN) functions and makes predictions, it has become increasingly clear that analyzing only the input domain is insufficient -- one must also examine its internal inference mechanisms to capture the complete picture.

By Ping Xiong, Thomas Schnake, Gr\'egoire Montavon, Klaus-Robert M\"uller, Shinichi Nakajima

computer-vision

arXiv AI

Jun 2

AnyMo: Scaling Any-Modality Conditional Motion Generation with Masked Modeling

arXiv:2605. 29488v2 Announce Type: replace-cross Abstract: Conditional human motion generation remains a fundamental challenge in computer vision and robotics.

By Yiheng Li, Zhuo Li, Ruibing Hou, Yingjie Chen, Hong Chang, Hao Liu, Shiguang Shan

llms computer-vision nlp robotics multimodal

arXiv AI

Jun 2

FW-NKF: Frequency-Weighted Neural Kalman Filters

arXiv:2606. 02251v1 Announce Type: cross Abstract: Robust state estimation is central to robotic autonomy, yet classical Kalman filters struggle with frequency-dependent disturbances and model mismatch such as sensor vibrations, electromagnetic interference, and periodic noise.

By Adnan Harun Dogan, Berken Utku Demirel, Christian Holz

computer-vision robotics benchmarks

arXiv AI

Jun 2

Ryze: Evidence-Enriched Data Synthesis from Biomedical Papers

arXiv:2606. 00902v1 Announce Type: new Abstract: General-purpose VLMs remain unreliable for biomedical research because valid answers in scientific papers depend on evidence split across figures, tables, charts, captions, and referring text.

By Yeqi Huang, Yue Chen, Yanwei Ye, Guanhao Su, Luo Mai

llms computer-vision reinforcement-learning fine-tuning multimodal

arXiv AI

Jun 2

MoEIoU: Rethinking Bounding-Box Regression as a Mixture of Experts

arXiv:2606. 00844v1 Announce Type: cross Abstract: Bounding-box regression is a fundamental component of object detection, playing a critical role in precise object localization.

By Vinay Edula, Priyanka Bagade

computer-vision benchmarks safety

arXiv AI

Jun 2

CalArena: A Large-Scale Post-Hoc Calibration Benchmark

arXiv:2605. 30188v2 Announce Type: replace-cross Abstract: Reliable probability estimates are critical in many machine learning applications, yet modern classifiers are often poorly calibrated.

By Eug\`ene Berta, David Holzm\"uller, Francis Bach, Michael I. Jordan

computer-vision benchmarks

arXiv AI

Jun 2

Agricultural Landscape Understanding At Country-Scale

arXiv:2411. 05359v2 Announce Type: replace-cross Abstract: Comprehensive agricultural landscape understanding is critical for addressing global challenges in food security, climate change, and resource management.

By Radhika Dua, Aditi Agarwal, Aishwarya Jayagopal, Depanshu Sani, Alex Wilson, Hoang Tran, Ishan Deshpande, Bogdan Floristean, Neelabh Goyal, Ramya Cheruvu, Vishal Batchu, Yan Mayster, Gaurav Aggarwal, Alok Talekar, Vaibhav Rajan

computer-vision

arXiv AI

Jun 2

A Lightweight Context-Driven Training-Free Network for Scene Text Segmentation and Recognition

arXiv:2503. 15639v2 Announce Type: replace-cross Abstract: Modern scene text recognition systems often depend on large end-to-end architectures that require extensive training and are prohibitively expensive for real-time scenarios.

By Ritabrata Chakraborty, Shivakumara Palaiahnakote, Umapada Pal, Cheng-Lin Liu

computer-vision benchmarks

arXiv AI

Jun 2

Enhancing Layer Attention Efficiency through Pruning Redundant Retrievals

arXiv:2503. 06473v5 Announce Type: replace-cross Abstract: Growing evidence suggests that layer attention mechanisms, which enhance interaction among layers in deep neural networks, have significantly advanced network architectures.

By Hanze Li, Yaosong Du, Zhibo Yao, Mengyao Zeng, Xiuqi Ge, Xiande Huang

computer-vision efficiency

arXiv AI

Jun 2

Zamba2-VL Technical Report

arXiv:2606. 00390v1 Announce Type: cross Abstract: We present Zamba2-VL, a suite of vision-language models built on Zamba2, a hybrid language-model architecture combining Mamba2 state-space layers with a small number of shared transformer blocks.

By Hassan Shapourian, Kasra Hejazi, Olabode M. Sule, Beren Millidge

llms computer-vision efficiency multimodal benchmarks

arXiv Machine Learning

Jun 2

Domain Adaptation with a Single Vision-Language Embedding

arXiv:2410. 21361v2 Announce Type: replace-cross Abstract: Domain adaptation has been extensively investigated in computer vision but still requires access to target data at the training time, which might be difficult to obtain in real-world autonomous driving scenarios, especially under rare or adverse conditions.

By Mohammad Fahes, Tuan-Hung Vu, Andrei Bursuc, Patrick P\'erez, Raoul de Charette

rag agents computer-vision robotics fine-tuning multimodal

arXiv Machine Learning

Jun 2

Scaling Parallel Sequence Models to Foundation-Scale Vision Encoders

arXiv:2606. 00746v1 Announce Type: cross Abstract: Vision foundation models are bottlenecked by the quadratic cost of self-attention, which limits usable resolution and increases the cost of large-scale pretraining.

By Yitong Jiang, Hongjun Wang, Collin McCarthy, Hanrong Ye, David Wehr, Xinhao Li, Qi Dou, Tianfan Xue, Ka Chun Cheung, Simon See, Wonmin Byeon, Ke Chen, Kai Han, Jinwei Gu, Hongxu Yin, Pavlo Molchanov, Jan Kautz, Sifei Liu

rag computer-vision efficiency multimodal

arXiv AI

Jun 2

VLM4VLA: Revisiting Vision-Language-Models in Vision-Language-Action Models

arXiv:2601. 03309v2 Announce Type: replace-cross Abstract: Vision-Language-Action (VLA) models, which integrate pretrained large Vision-Language Models (VLM) into their policy backbone, are gaining significant attention for their promising generalization capabilities.

By Jianke Zhang, Xiaoyu Chen, Qiuyue Wang, Mingsheng Li, Yanjiang Guo, Yucheng Hu, Jiajun Zhang, Shuai Bai, Junyang Lin, Jianyu Chen

llms computer-vision robotics fine-tuning multimodal benchmarks

arXiv AI

Jun 2

An Open-Source Benchmark and Baseline for Multi-temporal Referring Segmentation

arXiv:2606. 00987v1 Announce Type: cross Abstract: Large Vision-Language Models (LVLMs) have shown strong visual understanding and language-guided grounding abilities, yet their capacity for multi-temporal visual reasoning remains underexplored.

By Bingyu Li, Da Zhang, Tao Huo, Zhiyuan Zhao, Junyu Gao, Xuelong Li

llms agents computer-vision fine-tuning multimodal benchmarks

arXiv Machine Learning

Jun 2

WildCat: Near-Linear Attention in Theory and Practice

arXiv:2602. 10056v2 Announce Type: replace Abstract: We introduce WildCat, a high-accuracy, low-cost approach to compressing the attention mechanism in neural networks.

By Tobias Schr\"oder, Lester Mackey

llms diffusion computer-vision efficiency benchmarks

arXiv AI

Jun 2

Pre-Deployment Robustness Stress Testing for CT Segmentation Systems Using Clinically Motivated Multi-Corruption Augmentation

arXiv:2606. 00491v1 Announce Type: cross Abstract: Deep learning-based CT segmentation systems often achieve high accuracy on clean benchmark images, but their performance may degrade under heterogeneous clinical imaging conditions such as noise, resolution loss, contrast variation, intensity shift, and artifacts.

By CholMin Kang, Jonghyun Chung, Amanpreet Kaurb, Nagesh Gulkotwarb, Arthi Sivasankaranb

computer-vision benchmarks

arXiv AI

Jun 2

GiPL: Generative augmented iterative Pseudo-Labeling for Cross-Domain Few-Shot Object Detection

arXiv:2605. 29539v2 Announce Type: replace-cross Abstract: Vision-language foundation models have shown promising zero-shot generalization for Cross-Domain Few-Shot Object Detection (CD-FSOD).

By Jiacong Liu, Shu Luo, Yikai Qin, Yaze Zhao, Yongwei Jiang, Yixiong Zou

llms computer-vision fine-tuning multimodal benchmarks

arXiv AI

Jun 2

Beyond Visual Fidelity: Benchmarking Super-Resolution Models for Large-Scale Remote Sensing Imagery via Downstream Task Integration

arXiv:2605. 00310v2 Announce Type: replace-cross Abstract: Super-resolution (SR) techniques have made major advances in reconstructing high-resolution images from low-resolution inputs.

By Zhili Li, Kangyang Chai, Zhihao Wang, Xiaowei Jia, Yanhua Li, Gengchen Mai, Sergii Skakun, Dinesh Manocha, Yiqun Xie

llms diffusion computer-vision benchmarks

arXiv Machine Learning

Jun 2

PaCX-MAE: Physiology-Augmented Chest X-Ray Masked Autoencoder

arXiv:2606. 01537v1 Announce Type: cross Abstract: Clinical diagnosis often requires combining imaging with physiological measurements, yet deployed models typically operate on unimodal data.

By Yancheng Liu, Kenichi Maeda, Manan Pancholy

rag computer-vision efficiency multimodal benchmarks

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