AI & Machine Learning · Computer Science & AI
Computer Vision
The newest papers in Computer Vision that ReadSearch has read, newest first. Each links to its own page, with its authors, date, where it was published and the original.
The newest 5,000 papers, from 29 September 2026 to 5 November 2026: 4,999 arXiv preprints and 1 OpenAlex record.
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Newest papers, page 163
Selective Spatio-Temporal Fruit Inspector (S$^2$FI): A Mamba-based Framework for Rotational Fruit Quality Assessment
The global fruit and vegetable industry confronts severe post-harvest sorting challenges for small and medium-scale growers.ROSETTA: Rotation Subspace Estimation for Training-free Transmodal Alignment
When a query must be matched against a database built with a different sensor or processed by a different encoder, the independently trained encoders impose distinct coordinate systems on the…Win-Win Fusion: Multi-Modal Image Fusion with Visual-Semantic Collaboration
Existing multi-modal image fusion (MMIF) methods generally face an optimization gap between the visual and semantic domains, limiting the enhancement of perception capabilities.MeshBind: Point-to-Mesh Binding for Non-Rigid Scene Flow Automatic Annotation
Scene flow estimation is essential for many autonomous driving (AD) and robotic applications, but obtaining ground-truth scene flow for non-rigid objects such as pedestrians is highly difficult.AD-SRF: Attack-Defense Semantic Reinforcement for Multi-Modal Image Fusion
Existing image fusion methods struggle to balance visual quality and semantic attributes due to the inherent conflict between their optimization objectives.A Lightweight Velocity Correction Framework for Improving Instruction Following in Few-Step Text-to-Image Generation
Few-step text-to-image generators produce high-quality images with few network forward passes, yet struggle with complex prompts involving multiple subjects, interactions, spatial relations…JMT-YOLO: Multi-task Domain Adaptation via Segmentation-Guided Consistency Learning
Domain adaptive object detection (DAOD) aims to reduce performance degradation due to distribution shifts between training (source) and testing (target) datasets.BLEND: Balancing Personalization vs. Generalization in Federated Vision-Language Models
Federated vision-language models (FedVLMs) represent one of the emerging frontiers of machine learning, aiming to integrate federated learning within the fine-tuning pipelines of vision-language…What Egocentric Video Embeddings Encode and How to Combine Them with Inertial Sensors for Human Activity Recognition
Egocentric video and body-worn inertial sensors are increasingly recorded together, and the video is usually encoded by pretrained models.UniDeblur: Unified Motion Deblurring via Fourier Convolution Learning
Image deblurring is a fundamental task in computer vision, yet existing methods mainly specialize in isolated scenarios.In-Context Learning Can Help Vision-Language Models Overcome Training Priors
Vision-language models (VLMs) learn strong statistical regularities during training, which can make them fail to properly respond to visual evidence when input images violate those regularities.WaveMark: Robust Wavelet-Domain Image Watermarking for Diffusion Models
Image watermarking enables provenance tracing for photorealistic images generated by diffusion models.Video Codec-Guided Training-Free Token Compression for Video Large Language Models
Video large language models (VLLMs) rely on long visual token sequences to understand complex temporal content, which makes visual token compression important for efficient inference.MVGraph: Benchmarking LVLMs on Evidence Composition across Multi-View Visual Graph
We introduce MVGraph, a benchmark for evaluating large vision-language models (LVLMs) on multi-graph reasoning where the evidence required by a query is distributed across multiple interrelated…MotionCoach: Constructing Multimodal Guidance for Video Professional Motion Editing
Video editing models achieve strong performance in modifying appearance, backgrounds, styles, and motions, empowered by large-scale training data.Learning Representations to Guide Pixel Diffusion Transformers
Visual representations are largely developed for understanding, yet what makes them useful for generation remains unclear.The Evidence Anchoring Boundary: Where Visual Interventions Matter and When They Help in Multimodal LLMs
Visual interventions provide a training-free way to reduce hallucinations in multimodal large language models.Learning Features, Not Layouts: Feature-Axis Attention as a Transfer Prior for Medical Imaging
Natural-image-pretrained foundation models are increasingly adapted to medical imaging, yet transfer remains unreliable under domain shift, modality heterogeneity, and label scarcity.FlyHack: Stress-Testing Mechanistic Claims in a Connectome-Constrained Visual Model
Connectome-constrained neural networks make biological components directly accessible to intervention, but their mechanistic conclusions can still depend on the comparison model, intervention…EmbodiedCLBench: Evaluating Continual Learning for Self-Evolving Embodied Agents
Continual learning requires models to accumulate past experience and transfer it to new tasks.