Computer vision

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

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arXiv Machine Learning

Jun 16

Structured Nonparametric Variational Inference for Dependent Latent Modeling

arXiv:2606. 15458v1 Announce Type: cross Abstract: Variational inference (VI) is a core engine of modern AI, enabling scalable approximate Bayesian learning and uncertainty-aware training of large probabilistic and generative models.

By Yuda Shao, Zhiling Gu, Shan Yu

computer-vision

arXiv AI

Jun 16

Multi-Sensor Fusion for UAV Classification Based on Feature Maps of Image and Radar Data

arXiv:2410. 16089v2 Announce Type: replace Abstract: The unique cost, flexibility, speed, and efficiency of modern UAVs make them an attractive choice in many applications in contemporary society.

By Nikos Sakellariou (Centre for Research and Technology Hellas, Information Technologies Institute), Antonios Lalas (Centre for Research and Technology Hellas, Information Technologies Institute), Konstantinos Votis (Centre for Research and Technology Hellas, Information Technologies Institute), Dimitrios Tzovaras (Centre for Research and Technology Hellas, Information Technologies Institute)

computer-vision

arXiv AI

Jun 16

Domain-Guided Prompting of the Segment Anything Model for Seismic Interpretation: The Role of Attributes, Visualization, and Hybrid Prompts

arXiv:2606. 15786v1 Announce Type: cross Abstract: The advent of large pretrained foundation models for computer vision has significantly improved the efficiency of visual data interpretation.

By Aniq Ahmad, Heather Bedle, Ahmad Mustafa

llms computer-vision fine-tuning

arXiv AI

Jun 16

K-Prism: A Knowledge-Guided and Prompt Integrated Universal Medical Image Segmentation Model

arXiv:2509. 25594v2 Announce Type: replace-cross Abstract: Medical image segmentation is fundamental to clinical decision-making, yet existing models remain fragmented.

By Bangwei Guo, Yunhe Gao, Meng Ye, Difei Gu, Yang Zhou, Leon Axel, Dimitris Metaxas

computer-vision benchmarks

arXiv AI

Jun 16

An affordable hardware-aware neural architecture search for deploying convolutional neural networks on ultra-low-power computing platforms

arXiv:2606. 16290v1 Announce Type: cross Abstract: Hardware-aware neural architecture search (HW-NAS) allows the integration of Convolutional Neural Networks (CNNs) in microcontrollers devices by automatically designing neural architectures that can fit prearranged hardware constraints.

By Andrea Mattia Garavagno, Edoardo Ragusa, Antonio Frisoli, Paolo Gastaldo

computer-vision benchmarks

arXiv AI

Jun 16

Gen-VCoT: Generative Visual Chain-of-Thought Reasoning via Diffusion-Based RGB Intermediate Representations

arXiv:2606. 16783v1 Announce Type: cross Abstract: Multimodal large language models (MLLMs) excel at visual reasoning but rely on text-based chain-of-thought (CoT), lacking interpretable visual intermediates.

By Zhiqiang Zhou, Junliang Dai, Xu ling

llms diffusion computer-vision multimodal

arXiv AI

Jun 16

RAMS: Resource-Adaptive and Detection-Conditioned Model Switching for Embedded Edge Perception

arXiv:2606. 14716v1 Announce Type: cross Abstract: Edge object detection on embedded hardware requires balancing inference latency and detection quality under changing resource pressure.

By Kushal Khemani, Evan Leri, George Xu, Amit Hod

computer-vision efficiency

arXiv AI

Jun 16

RSRCC: A Remote Sensing Regional Change Comprehension Benchmark Constructed via Retrieval-Augmented Best-of-N Ranking

arXiv:2604. 20623v2 Announce Type: replace-cross Abstract: Traditional change detection identifies where changes occur, but does not explain what changed in natural language.

By Roie Kazoom, Yotam Gigi, George Leifman, Tomer Shekel, Genady Beryozkin

rag computer-vision multimodal benchmarks

arXiv Machine Learning

Jun 16

KeepLoRA++: Continual Learning with Layer-Scaled Residual Gradient Adaptation

arXiv:2606. 16256v1 Announce Type: cross Abstract: Continual learning for pre-trained vision-language models requires balancing three competing objectives: retaining pre-trained knowledge, preserving knowledge from a sequence of learned tasks, and maintaining the plasticity to acquire new knowledge.

By Mao-Lin Luo, Yi-Lin Zhang, Zi-Hao Zhou, Yankun Hong, Xialiang Tong, Mingxuan Yuan, Tong Wei, Min-Ling Zhang

llms computer-vision nlp fine-tuning multimodal

arXiv Machine Learning

Jun 16

MNet++: Extended 2D/3D Networks for Anisotropic Medical Image Segmentation

arXiv:2606. 15370v1 Announce Type: cross Abstract: This work demonstrates a full reproduction and extension of MNet, a hybrid 2D/3D convolutional network designed for anisotropic medical image segmentation.

By Kirsten Odendaal, Rade Bajic

computer-vision

arXiv Machine Learning

Jun 16

Learning a Sampling-Free Variational DNN Plugin from Tiny Training Sets to Refine OOD Segmentation With Uncertainty Estimation

arXiv:2606. 15837v1 Announce Type: cross Abstract: Deep neural networks (DNNs) frequently fail to generalize to out-of-distribution (OOD) medical images because of variations in scanners and acquisition protocols.

By Jimut B. Pal, Suyash P. Awate

computer-vision

arXiv AI

Jun 16

SceneConductor: 3D Scene Generation from a Single Image with Multi-Agent Orchestration

arXiv:2606. 08402v2 Announce Type: replace-cross Abstract: Generating complete 3D scenes from a single image requires inferring globally consistent geometry, object relationships, and environmental context from inherently ambiguous visual evidence.

By Jeonghwan Kim, Yushi Lan, Yongwei Chen, Hieu Trung Nguyen, Chuanyu Pan, Xingang Pan

agents computer-vision benchmarks

arXiv Machine Learning

Jun 16

BRICKS-WM: Building Reusability via Interface Composition Kinetics for Structured World Models

arXiv:2606. 16489v1 Announce Type: new Abstract: Model-based Reinforcement Learning (MBRL) has achieved remarkable success in continuous control by leveraging latent world models.

By Shaowei Zhang, Jiahan Cao, Xunlan Zhou, Shenghua Wan, De-Chuan Zhan

agents computer-vision reinforcement-learning

arXiv AI

Jun 16

Federated Medical Image Segmentation under Real-World Label Noise: A Benchmark Suite for Noisy Label Learning Method Selection

arXiv:2606. 16868v1 Announce Type: cross Abstract: While federated learning (FL) enables collaborative medical image segmentation without centralizing sensitive data, real-world deployment is frequently complicated by cross-site label imperfections such as contour disagreement, missing or additional structures, and confused labels.

By Markus Bujotzek, Dimitrios Bounias, Stefan Denner, Ralf Floca, Maximilian Fischer, Peter Neher, Klaus Maier-Hein

computer-vision benchmarks

arXiv AI

Jun 16

Mask Proposal Voting Based on Geodesic Framework for Robust Image Segmentation

arXiv:2606. 14912v1 Announce Type: cross Abstract: Despite great advances, finding accurate segmentation remains a challenging task, especially in scenarios with cluttered backgrounds, complex intensity variations and topology appearance.

By Li Liu, Mingzhu Wang, Zhenjiang Li, Da Chen, Laurent D. Cohen

computer-vision benchmarks

arXiv AI

Jun 16

Lightweight Distillation of SAM 3 and DINOv3 for Edge-Deployable Individual-Level Livestock Monitoring and Longitudinal Visual Analytics

arXiv:2604. 27128v2 Announce Type: replace-cross Abstract: Foundation-model pipelines for individual-level livestock monitoring -- combining open-vocabulary detection, promptable video segmentation, and self-supervised visual embeddings -- have raised the accuracy ceiling of precision livestock farming (PLF), but their GPU memory budgets exceed the envelope of commodity edge accelerators.

By Haiyu Yang, Miel Hostens

rag computer-vision efficiency

arXiv Machine Learning

Jun 16

Emergent retokenization symmetry in large language models: phenomenology and applications

arXiv:2606. 15521v1 Announce Type: cross Abstract: Tokenization introduces representational redundancy: under a fixed token vocabulary, every byte string admits many valid token encodings, or segmentations, that decode to the same surface string.

By Kanishk Jain, Matthew Day, Tankut Can

llms computer-vision nlp benchmarks

arXiv AI

Jun 16

SLUM-i: Semi-supervised Learning for Urban Mapping of Informal Settlements and Data Quality Benchmarking

arXiv:2602. 04525v2 Announce Type: replace-cross Abstract: Rapid urban expansion has fueled the growth of informal settlements in major cities of low- and middle-income countries, with Lahore and Karachi in Pakistan and Mumbai in India serving as prominent examples.

By Muhammad Taha Mukhtar, Syed Musa Ali Kazmi, Khola Naseem, Muhammad Ali Chattha, Andreas Dengel, Sheraz Ahmed, Muhammad Naseer Bajwa, Muhammad Imran Malik

computer-vision benchmarks data-engineering

arXiv AI

Jun 15

How do Self-Supervised Remote Sensing Vision Models Transfer to Downstream Tasks?

arXiv:2606. 13896v1 Announce Type: cross Abstract: Self-supervised geospatial foundation models (GeoFMs) learn transferable representations from remote sensing data, but their downstream behavior is difficult to characterize.

By Julia Romero, Qin Lv, Morteza Karimzadeh

llms rag computer-vision fine-tuning multimodal benchmarks

arXiv AI

Jun 15

Fusion of Pervasive RF Data with Spatial Images via Vision Transformers for Enhanced Mapping in Smart Cities

arXiv:2508. 03736v2 Announce Type: replace-cross Abstract: In this paper, we present a deep learning-based approach that integrates the DINOv2 architecture to improve building mapping by combining (possibly erroneous) maps from open-source platforms with pervasive radio frequency (RF) data collected from multiple wireless user equipments and base stations.

By Rafayel Mkrtchyan, Armen Manukyan, Hrant Khachatrian, Theofanis P. Raptis

llms computer-vision

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