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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 166
QCLR: A Plug-in Robustification Module via Quantization for Contrastive Learning in Self-Supervised Skeleton-Based Human Action Recognition
Skeleton-based human action recognition has gained increasing attention due to the stability of skeleton data across diverse environments and advances in motion capture technologies.Multi-Source and Multi-Dimensional Information Fusion in Resource-Constrained Edge Networks
Multi-source semantic information fusion enables edge networks to support global inference using observations collected by distributed terminals.Decoding Register Tokens Preserves Global Scene Details
Pretrained vision transformers learn structured image representations spanning spatial patch tokens and non-spatial tokens.Sonic-AMG: Streaming Human Motion Generation from Spatial Audio
Modeling human reactions to spatial audio requires understanding both what is heard and where it comes from.PAGE: Parts-Aware Guidance for Co-Speech Gesture Portrait Video Generation
Co-speech gesture video generation aims to synthesize realistic, audio-driven portrait videos from a single reference image and a speech signal.NovaSplat: Model-Free 6D Object Pose Estimation via 3D Gaussian Splatting Onboarding and Refinement
6D pose estimation of novel objects without CAD models remains an open challenge: model-free methods must build the object representation from scratch during onboarding and have long trailed their…ThinkDrag: Semantic Drag-Based Image Editing with Visual Reasoning
Drag-based image editing provides an intuitive interface for spatially controlled image manipulation by allowing users to specify handle and target points.CoVA: Composing VAE Representations for Generative Learning
Representation alignment with pretrained visual encoders improves diffusion transformer training but requires auxiliary encoders and extra training overhead.LETHE: Defending against Unauthorized Image-to-Video Generation via Value Diversity Collapse
Image-to-video (I2V) diffusion models can animate a single reference image into high-quality, subject-consistent videos, posing threats of misuse as anyone can turn personal photos into fabricated…Exploring Attention Sinks in Text-to-Image Transformers from Diffusion and Autoregressive Models
Attention sinks are tokens or positions that consistently receive disproportionate attention mass, often beyond what their semantic content would suggest.VideoAWR: Reinforcing Video Flow Models with Decoupled-Timestep Advantage-Weighted Rectification
Regression-based RL for flow models mandates sampling \emph{without} classifier-free guidance (CFG) to align the rectification target with the reference marginal velocity.Why DiT Models Underperform as Representation Learners without Long Skip Connections
Diffusion feature, i.e., intermediate activations extracted from diffusion model backbones, has emerged as a promising approach for unifying generation and vision understanding (discrimination).ECGWmark: Deep Watermarking for Electrocardiogram Signals
Secure transmission of electrocardiogram (ECG) signals remains a major challenge in telecardiology.Radiomics-Based Thyroid Nodule Classification with Label-Conditional Conformal Prediction
Thyroid nodule malignancy assessment relies on ultrasound imaging, yet current risk stratification systems suffer from substantial interobserver variability, contributing to a high rate of…TextVBench: Evaluating Text Rendering and Preservation in Video Generation
Modern video generators can synthesize convincing scenes while rendering a few characters incorrectly, omitting requested text, or changing it across frames.SADL: A Size-Adaptive Decision Layer for Chronic Stroke Lesion Segmentation in ISLES 2026
Chronic ischemic stroke lesions are difficult to segment on T1-weighted MRI.When MLLMs miss what humans read: diagnosing and learning to read squint text
Multimodal LLMs can fail to read text embedded in an image's coarse structure (text a human recovers by squinting) and report that no text is present.A Real-World CAD Segmentation Benchmark: Comparing Rule-Based and Learning-Based Approaches through Hole Detection
CAD segmentation datasets are predominantly synthetic or automatically labelled, leaving it unclear how well existing methods transfer to real-world engineering tasks.GraftOOD: Grafting Out-of-Distribution Exemplars into Advantage-Weighted Rectification for Video Reward Optimization
Video reward optimization can increase proxy rewards while reducing video dynamics, a mismatch we identify as \emph{motion collapse}, including under a video-level motion-quality reward.Stride: Scale-Aware Multi-Agent Evolutionary Refinement for Infographic Generation
Infographic generation challenges text-to-image models because a single image must satisfy numerous visual, textual, and semantic constraints.