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: 4,996 arXiv preprints and 4 OpenAlex records.
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Newest papers, page 245
When to Retrieve, When to Stay: Uncertainty-Aware Temporal Evidence Allocation for Streaming Video-LLMs
Streaming video understanding requires Video Large Language Models (Video-LLMs) to reason over continuous visual streams under causal constraints.TAEC: Trajectory-Aware Evidence Coordination for Multi-Step Visual RAG
Multi-step visual retrieval-augmented generation (RAG) answers complex questions by repeatedly retrieving visual evidence, updating an intermediate state, and deciding whether to continue searching…PCaPaint: Prostate Cancer Inpainting by Mitigating Shortcut Learning
The development of AI systems for tumor-specific applications is limited by the scarcity of labeled data.Encore: Few-Shot Agentic Discovery of Manipulation Strategies
Coding agents can now write, run, and debug programs with little human help.Visual Anomaly Synthesis for Model Selection in Data Scarcity
Defect detection systems for industrial condition monitoring can only be relied upon if they are validated, yet defective samples are rare and, for a specific asset, often nonexistent.Think Before You Score: Thinking Reward Model for Visual Generation
Visual reward models are essential for evaluating and improving visual generation models, yet existing approaches typically map task conditions and candidate outputs directly to scalar rewards…MG-Thinker: Bi-Axial Self-Reflection for Multi-Image Reasoning Grounding
Reinforcement learning (RL) has recently delivered substantial gains in multimodal reasoning, opening a promising route for fine-grained visual perception.Do-JEPA: From Masking to Intervention in Latent World Models
Latent world models are trained to predict what happens next, so nothing in their objective separates what an action caused from what merely co-occurred with it.Anatomy-Aware Prediction of Bronchoscopic Accessibility from 3D CT
Pre-operative planning for bronchoscopy is critical for the diagnosis of lung lesions.Multi-task learning for the automatic grading of enlarged perivascular space burden using MRI
Enlarged perivascular spaces (PVS) visible in brain magnetic resonance imaging (MRI) are increasingly thought to be linked to poor brain health.BeatDance: Generating Beat-Consistent 3D Dance with Hierarchical Spatial-Temporal Modeling
Generating realistic 3D dance from music is a challenging task that requires accurate synchronization with musical rhythms while capturing the spatial complexity of human motion.Complementary Retrieval-Augmented Prompting for Consistent Long-Form Video Generation
While recent video foundation models excel at generating high-quality short videos, long-form video generation remains a critical challenge, where a major bottleneck lies in conditioning…LazySloth: Bounded LLM-based Lazy Tree Search for Fast Long Video Comprehension
Modern vision-language models (VLMs) have shown promising results in long-video understanding due to the rich semantic information they can capture.Hierarchical Compression of Vision-Language Model Benchmarks
Thorough evaluation of vision-language models (VLMs) has become prohibitively expensive, as benchmarks span an ever-broader spectrum of capabilities and new models arrive at a relentless pace.Principled MAP estimation for inverse problems: bridging the gap between convergence and performance
Pretrained denoisers provide a powerful way to incorporate image priors into restoration algorithms.RawVLA: Embodied Neural Image Signal Processor For Robotic Manipulation
Vision-language-action (VLA) models typically operate on RGB images produced by a fixed camera image signal processor (ISP), leaving the imaging pipeline outside the learning and evaluation loop.Weeding Out Bad Seeds: Initial-Noise-Robust Unlearning for Text-to-Image Diffusion Models
Machine unlearning has emerged as a critical post-hoc safety measure to erase sensitive concepts from Text-to-Image (T2I) models without prohibitive retraining.APM-Bench: Benchmarking Cross-session Persistent Memory for Egocentric Streaming Video Assistants
To serve as real-world personal assistants, streaming video models need persistent memory that retains past experiences for later use.Decompose Radicals, Then Reward: Fine-Grained Inspection for Accurate Chinese Text Rendering
Rendering accurate Chinese text remains challenging for text-to-image models.Evaluating the Evaluators: Diagnosing Large Multimodal Models for AI-Generated Image Assessment
With the rapid advancement of text-to-image (T2I) generation, robust evaluation becomes critical yet challenging, as traditional metrics fail to capture fine-grained alignment and generative…