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: 4,999 arXiv preprints and 1 OpenAlex record.
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Newest papers, page 47
ElasticFormer: Detecting Objects in HRW Shots via Elastic Computing Vision Transformer
Recent advances in gigapixel-level imaging have brought High-Resolution Wide shots to the forefront of research.UniRA: Unified Representation Alignment and Auto-Encoding for Efficient Diffusion Generation
Diffusion transformers (DiTs) have become the dominant architecture for high-fidelity image synthesis, yet they suffer from slow training convergence and high computational cost.Mind The Gap (MTG-AD): Dataset and Fine-grained Evaluation for End-to-End Inline Audio Descriptions
Audio descriptions (AD) are essential for making visual media accessible to blind and low-vision (BLV) users.Blind Video Denoising for Vocal Tract rtMRI using a Self-Supervised Ensemble
Real-time magnetic resonance imaging of the vocal tract makes speech articulation directly observable, but the trade-off between temporal resolution and signal quality introduces substantial noise…How Much Should a VLA Trust Its Reasoning? Step-Level Guidance as a Measured Action-Value Function
Reasoning vision-language-action (VLA) policies write a reasoning trace and then decode the action from it.Greenscapes: Optimized Vegetation Configurations in Cities
Vegetation plays a central role in cities, serving functional roles such as balancing shade and sky exposure, shaping the microclimate, carbon sequestration, and providing acoustic screening.DINOv2 for PET: A Pre-Training Domain Benchmark Across Classification, Segmentation, and Prognosis
DINO (self-DIstillation with NO labels) has become a stan- dard for self-supervised pre-training, producing features that generalise across datasets and downstream tasks.Segment Anything Model Based Context Guided Mamba Unet for Medical Image Segmentation
Medical image segmentation is crucial for clinical diagnosis and treatment planning, requiring both global contextual understanding and fine-grained structural precision.EPC-RawDC: Equivariant Physics Constrained 3D Diffusion with Raw Data Consistency for CBCT to CT Image Synthesis
Cone beam computed tomography (CBCT) is widely used for image guided and adaptive radiotherapy, but scatter, noise, reconstruction artifacts, and inaccurate Hounsfield units limit its quantitative…Localizing Concepts in Visual Autoregressive Models
Understanding the internal routing of visual concepts is crucial for the safe and controllable adaptation of generative models.BRACHIO: Broad Representations Across Centres for Head-and-neck Imaging Oncology (HECKTOR 2026)
The HECKTOR 2026 challenge benchmarks machine learning for head and neck cancer, where teams delineate primary and nodal tumour volumes, perform TN staging, and predict recurrence-free survival (RFS).Uncertainty-aware Coarse-to-Fine Main Pancreatic Duct Segmentation for Width Measurement in 3D MRCP
Assessment of the main pancreatic duct (MPD) from volumetric magnetic resonance cholangiopancreatography (MRCP) scans is an essential task for ductal dilatation diagnosis and pancreatic cancer…Morphology-Aware Channel-Decoupled Mamba Network for Medical Image Segmentation
Accurate multi-organ segmentation in computed tomography (CT) is essential for computer-aided diagnosis, treatment planning, and disease monitoring.Neural Blind Deconvolution for Image Scanning Microscopy
Image scanning microscopy (ISM) acquires one scanned image per detector element, which are then fused into a single image by multi-image deconvolution to obtain a super-resolved image.RepConTUR: A Report-Conditioned Volumetric Multimodal Model for Abdominal Tumor Segmentation
Tumor delineation is required for radiotherapy planning, yet automatic segmentation is limited by errors, predominantly false positives, that distort dose and increase editing time.SelfThinker: Self-Evolving Reasoning via Reliability-Verified Intrinsic Visual Thoughts
Unified multimodal models can both generate and understand images within a single set of weights, offering an opportunity to use image synthesis as training supervision for visual comprehension.SynthFaces: Balanced Large Scale Human Dataset
The pursuit of fair and equitable AI in human-centric tasks is critically hindered by the inherent biases and privacy risks of real-world datasets, which offer imbalanced representations of identity…Learning a Motion Manifold to Constrain Ambiguity in Unsupervised 4D Medical Image Interpolation
Unsupervised 4D medical image interpolation reconstructs intermediate anatomical states from temporally sparse observations without intermediate supervision.It Takes Two to Probe, Thousands to Retrieve: Putting Vision--Language Probes to the Retrieval Test
Vision--language models (VLMs) are tested for fine-grained understanding with image--text matching probes, a model must prefer the correct image or caption over handcrafted negative.VideoMemBench: How Well Do MLLMs Remember in Video Conversations?
Multimodal large language models (MLLMs) can now process hour-long videos, making extended multi-turn dialogue over a single video a natural mode of use.