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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 92
Spatially Gated Demographic Steering for Debiasing Multi-Person Text-to-Image Generation
Text-to-Image models reproduce societal stereotypes, and existing mitigation applies one demographic correction to the whole scene.Learning to Reason Like a Cytopathologist:Clinically Grounded Multiscale Evidential Learning for Cervical Cytology
Cervical cytology presents a fundamentally ambiguous diagnostic landscape, where equivocal categories such as Atypical Squamous Cells of Undetermined Significance (ASC-US) occupy a morphological…Rethinking Post-hoc Debiasing of Large Pretrained Models
Large pretrained models such as CLIP are widely used as frozen feature extractors.Efficient Generative Image Compression with Quantized Latents Refinement
Generative image compression (GIC) delivers strong perceptual quality at low bitrates.Forward-Only Learning for Shared-Kernel Wavelet Neural Networks without Backpropagation
Backpropagation remains the dominant training paradigm, using backward gradients and local updates that can suffer from vanishing or exploding gradients in deep networks.Learning to Measure and Diagnose: A Clinical-Dictionary Radiomic Bottleneck Model for Thyroid Nodule Classification
Thyroid ultrasound supports the assessment of nodule malignancy, but classification predictions often lack evidence linked to familiar clinical descriptions.APED-CXR: Adaptive Privileged Evidence Distillation for Chest X-Ray Grounding with Image-Only Inference
Compact chest X-ray (CXR) models that localize findings suit deployment, but box annotations are limited and report-aware multimodal models are expensive at inference.LEAF-Mamba: Feature Adaptation and Fusion for Whole Slide Image Analysis
Whole slide image (WSI) analysis enables computational diagnosis and prognosis, but gigapixel images and limited annotations make slide-level learning challenging.UniRAP: Towards Unified Part-level Physical Affordance Reasoning and Actionable Perception
Vision-language perception has achieved impressive progress in aligning natural language with visual observations, yet grounding high-level semantics into part-level physical interaction remains…Key Findings for Small UAS for Post-Disaster Damage Assessment from the CURSE'26 Exercise
The CLARKE building and road damage assessment AI classifier was incorporated into the Catastrophic Unmanned Remote Sensing Exercise (CURSE) 2026, which trained 23 small uncrewed aerial systems…JointLight: Human-Centric Video Relighting with Asynchronous RGB-Irradiance Joint Modeling
Understanding and modeling light transport in human-centric scenes is a longstanding challenge.Open-Vocabulary Models and Label Propagation for Uncertainty-aware Pseudo-Labeling of Object Detection Datasets
Object detection models rely on large-scale annotated datasets that require substantial manual annotation effort.Compositionality with Primitives: A Unified View from Euclidean to Hyperbolic Vision-Language Models
Compositional zero-shot learning (CZSL) aims to recognize novel combinations of previously observed objects and attributes.GuidedSAM-Plume: An Expert-Guided Segmentation Framework for Volcanic Plumes
Quantitative analysis of environmental phenomena increasingly relies on image-based measurements.Early versus Late RGB-Infrared Fusion for Counter-UAV Detection: A Comparative Study
This paper presents a novel strategy for UAV detection through visible (RGB) and infrared (IR) fusion, in order to improve accuracy and precision in harsh environments and weather conditions.Revealing Hidden Failure Modes in Chest X-ray Classification via Spectral Domain Analysis
Deep learning models for chest X-ray anomaly detection remain vulnerable to subtle distributional shifts (e.g., acquisition technique, patient-related factors, and preprocessing).How Do DiTs Denoise? An Aggregator-Predictor View of Attention and MLPs
How do diffusion transformers (DiTs) represent image content and learn rules for reusing it to generalize?GeoMem-VLA: Internalizing 3D Scene Understanding into Temporal Memory for Vision-Language-Action Models
Geometric structure is essential for robotic manipulation and understanding the 3D world.Stochastic Nonlinearities Improve Uncertainty Estimation
Reliable uncertainty estimation is important across visual recognition tasks, providing information about the confidence and reliability of model predictions.Expandable Modality Experts for Medical Vision-Language Learning
Learning across medical imaging modalities requires making use of images without paired text and accommodating new domains while retaining existing representations.