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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 46
DEQ-Stereo: Deep Euilibrium Video Stereo Matching via Adaptive Convergence
Iterative refinement models have significantly improved stereo matching accuracy, but their rigid, fixed update schedules incur heavy computational costs and a strict trade-off between speed and…Confidence Filtering in Video-Based Gait Assessment: Measurement Error versus Selection Shift
Video-based gait assessment often keeps only high-confidence pose observations, which can lower measurement error but change the movement assessed.NIF: Normal-Intersection Fields for Geometrically Consistent Oriented Box Refinement
Accurate oriented detection requires boxes to follow object boundaries, yet small errors in the coupled center, size, and angle estimates can displace long boundaries even when an object is…WorldProbe: When Video World Models Fall Apart under Long-Horizon Spatial Exploration
Video world models generate future visual observations from camera or action inputs, with the goal of synthesizing persistent 3D worlds that can be explored across time and space.Cross-Encoder Anchor Refinement for Zero-Shot Anomaly Detection
Zero-shot anomaly detection aims to transfer anomaly discrimination from auxiliary training domains to unseen categories while localizing subtle defects.Quantum-Inspired Convolutional Networks for High-Resolution Pseudo-Label Generation from Multimodal Earth Observation Data
Accurate land-cover mapping from multimodal Earth observation (EO) data remains a challenge in regions where high-resolution annotations are scarce or costly to obtain.SCOPE: Semantic COdec Pre-Editing for Object-Preserving Compression
Image compression is increasingly used before machine vision systems, yet standard codecs are mainly optimized for human perception rather than downstream task accuracy.GROME: GROunding Factorized MEmory into Video World Models
Long-horizon consistency is essential for video world models, yet it becomes difficult to maintain once observed content falls outside the context window.Aligning Medical Vision Language Models at the Capability Boundary with Entropy Adaptive Rubric Grounding
Medical diagnostic reasoning requires verifiable links between visual evidence and clinical knowledge, yet current medical vision language models are aligned with objectives that reward the wrong…LiDAR-SHIELD: A Fully Dockerized Adversarial Robustness Assessment Framework for LiDAR-Based 3D Perception Models
LiDAR-based 3D perception underpins autonomous driving and robotics, yet deploying these models safely requires stress-testing them against adversarial inputs before real-world use.Open-set multi-view seahorse re-identification from underwater photograph sequences
Automated animal re-identification from photograph sequences can facilitate minimally invasive Capture-Mark-Recapture studies at scale.M-PFT: Angular Margin for Calibrated Prompt-Tuning of Fine-Tuned Vision-Language Models
Prompt-tuning has emerged as a parameter-efficient technique for adapting large-scale vision-language models (VLMs) to downstream tasks while keeping the pre-trained weights frozen.Parametric Gaussian Noise Mixer (PGNM): Unified Synthesis of Camera Noise and Film Grain
Noise synthesis is commonly approached through physical models of image formation or neural networks trained to generate noisy images.What Survives Label Downsampling? Exact Region Survival for Coarse Segmentation Targets
Segmentation networks that predict at an output stride are trained against label maps downsampled by strided sampling, nearest-neighbor resampling or majority pooling.sDMD: Sinkhorn Distribution Matching Distillation
Distribution Matching Distillation (DMD) distills multi-step diffusion models into few-step generators by minimizing reverse KL divergence.TokenRepel: Generating Diverse Image Sets Without Compromising Quality
We present an inference-time approach that enhances the visual diversity of image sets generated by text-to-image models, without compromising the average image quality.PhysAg3D: Bridging 3D Generative and Agentic Models for High-Fidelity and Physically-Interactive Assets
Generating interactive 3D assets from a single image requires realistic appearance, fidelity to the depicted object, and plausible physical behavior.Spectral Invariant Fusion and Refinement for Domain Generalized Semantic Segmentation
Domain generalized semantic segmentation is challenging because it requires accurate dense prediction on unseen domains without any access to target-domain data during training.MLLM-GS: Multimodal Large Language Model Prior-Guided Fast Novel View Synthesis in Extreme Environments
Novel view synthesis (NVS) in degraded scenes aims to reconstruct 3D scenes from multi-view observations affected by various degradations, and to generate clear and photorealistic images at unseen…BiHashFormer: Hash-Driven Dual-Branch Transformer for Efficient Object Detection in HRW Shots
Recent advances in gigapixel-level imaging have brought High-Resolution Wide shots to the forefront of research.