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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 110
Channel-Level Mask Guidance for Slimmable Image Compression
Learned image compression (LIC) has achieved promising rate-distortion performance, but most methods lack scalability for diverse platforms with varying computational and storage constraints.Robustness of Internal Signals for Hallucination Detection in Vision-Language Models
When given an image and a text prompt, vision-language models (VLMs) can produce seemingly sophisticated text responses.VitronEdit: Universal Visual Editing across Images, Videos, and 3D Scenes
Recent advances in unified visual editing have begun to consolidate image and video editing within a single model, while 3D scene editing remains largely separate and constrained by scarce paired…BUMP: Body-driven Unified Multi-object Physics for HOI Synthesis
Generating human-object interactions (HOIs) with multiple objects is challenging because the resulting motions arise from different underlying causes.SciFigBench: A Benchmark for Scientific Method Figure Understanding
Scientific method figures often provide researchers with a clear visual entry point into a paper's methodology and core contributions, yet their dense layouts and complex information flows are…Omni-Woodpecker: Hallucination Correction via Evidence-Prior Rebalancing for Omni LLMs
Recent Omni LLMs (OLLMs) have demonstrated remarkable capabilities in joint audio-visual reasoning, yet cross-modal hallucinations remain a major challenge.LVMem: Image-Augmented Memory for Long-Horizon Question Answering
Reliable language-guided perception is essential for embodied agents, where questions about objects, events, and their locations must be answered from long observation histories.Counting Contributions, Not Views: Occlusion-Aware Densification for 3D Gaussian Splatting
3D Gaussian Splatting grows a dense scene from a sparse initialization through adaptive density control (ADC), driven by per-Gaussian statistics gathered during rendering.MatchCityLoc: Aerial Localization in LoD City Models via Surface Normal Matching
Autonomous Unmanned Aerial Vehicle (UAV) navigation in urban environments requires precise pose estimation.PRIMFLOW: Learning Trajectory Primitives for Few-Step Distillation
Few-step trajectory distillation aims to learn accurate global updates from dense local supervision along a teacher's sampling trajectory.CARLAR Influence: A Structured Hallucination Risk in Multimodal Cheque Extraction and an Exploratory Compressed-Image Countermeasure
Multimodal large language models (LLMs) are increasingly used for structured extraction from financial documents, but their behavior under visual ambiguity is poorly understood.Which Depth Estimator for Jetson AGX Orin? A 2026 Benchmark of Monocular and Stereo Methods
The Jetson AGX Orin is a common computer for robots and vehicles, yet measured accuracy, speed and energy of current depth estimators on it are hard to find.Language Guidance for Supervised Vision Training: An Empirical Study of Generalization
Deep neural networks have achieved remarkable success on vision benchmarks, yet they continue to struggle with many generalization challenges.On Maximum Clique and Maximum Consensus Formulations in Point Cloud Registration
Maximum clique and maximum consensus are widely used formulations for robust point cloud registration in robotics.Generalizing Multimodal Foundational Models in Context-Driven Class Incremental Learning
Class-Incremental Learning (CIL) aims to learn new classes continuously without forgetting the previously learned ones.What Not to Erase: Saliency-Constrained Augmentation for Low-Resource Handwritten Character Recognition
Optical Character Recognition (OCR) and handwritten character recognition (HCR) in low-resource historical material collections often rely on data augmentation, yet augmentation strategies are…Steering Vision-Language Models for Visual-Contextual Privacy in Location Disclosure
Vision-language models (VLMs) can infer sensitive information such as geolocation from subtle visual cues, creating privacy risks when responses disclose more detail than the visual context warrants.Look Before You Reason: Implicit Visual Thinking for Efficient Multimodal Reasoning
Vision-language models (VLMs) have advanced rapidly in multimodal reasoning, yet they remain limited on tasks requiring fine-grained understanding of small or easily overlooked image regions.On the Limits of Scale: Inverse Scaling in Vision-Language Models
Scaling laws suggest that increasing model size, training data, and compute generally yields monotonic performance improvements.PerQ: Inverse Generative Modeling for Neural Image Compression via Quantization Error Compensation
Neural image codecs face a fundamental trade-off between pixel fidelity and perceptual quality.