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 155
ChemExplain: A Multimodal Benchmark for Explanatory Chemical Reasoning
Current chemistry benchmarks primarily evaluate outcome prediction or reaction pathway generation, but rarely test whether a model can explain why an observed chemical effect occurs.Multics: Generative Multi-Player World Model with Hierarchical Communication
Multi-player interactive world models aim to simulate shared environments from multiple players' perspectives, but achieving robust generation while scaling to more players remains challenging.DexNinja: Learning Robust Dexterous Cutting Policy with a Real-to-Sim-to-Real Data Engine
Cutting a piece of food looks effortless in human hands.AReA: Adaptive Receptive-Field Attention for Efficient High-Resolution Diffusion Transformers
Generating images beyond the training resolution of Diffusion Transformers (DiTs) remains challenging, as the number of tokens grows rapidly with resolution, full attention becomes expensive, and…Beyond Sparse Captions: Aligning Slide-Level Text and Patch-Level Vision in Pathology
Contrastive vision-language (VL) pretraining in computational pathology has generally relied on curated image-caption datasets sourced from social media, educational videos, and research publications.IsoVRBench: Video Reward Modeling Without Human Feedback
Video reward models are trained and evaluated on human preferences between videos that often differ simultaneously on properties such as prompt alignment, visual fidelity, motion, and generator…When Left Is Right? Camera-Motion Understanding beyond Screen
Can a vision-language model tell left from right?HybridDualPathNet: Cross-Fusion Attention of EfficientNetV2 and Swin Transformer for Brain MRI Classification of Alzheimer's and Parkinson's Disease
Separating Alzheimer's Disease (AD), Parkinson's Disease (PD), and Healthy Controls (HC) using structural MRI remains challenging because disease signatures overlap and model performance can be…SSM-Attack: Learning Where to Perturb for Black-Box Face Privacy
Face recognition systems are deployed widely enough that a photograph shared online can be matched without its subject's consent.HairFlow: Modeling 3D Hair Strands with Multi-Modal Conditioned Diffusion
Modeling 3D hair strands is challenging due to complex hairstyle geometry, scarce high-quality training data, and incomplete or noisy input observations.MID: Mask-Image Distributional Divergence for Evaluating Medical Image Segmentation
Current evaluation protocols for medical image segmentation remain largely anchored in sample-wise comparisons, e.g., Dice and Hausdorff distance, which require each prediction to be paired with a…WIRE: Weight Space Implicit Representation Encoding of Frozen Video LLMs
Video language models represent a video as a long sequence of visual embeddings in a decoder's input.Brain2Face: A Study on the Relation of EEG Signals and Spatio-temporal Head Avatars
Driving digital head avatars is classically done by hand-crafted artist design or by image-based facial performance capture.What Makes a Latent Space Shared? Direct and Recoverable Compatibility in Motion VAEs
A shared latent space is often assumed to make representations from different schemas directly interchangeable, yet sharing parameters, dimensionality, or a prior does not determine how…Bottlenecked semantic compression by representation reconstruction
Vision Transformers incur high computational costs when processing long visual token sequences.World-T3: Test-Time Training as Persistent Memory for Real-Time Video World Models
Recent advances in video world models have enabled plausible interactive environments, yet long-horizon generation places a fundamental demand on visual memory: the previously observed world must…Know-Thy-Not: Training-Free Targeted Negation in Dual-Encoder Vision-Language Models
Dual-encoder vision-language models systematically fail at negation, scoring ''a cat without a dog'' nearly identically to ''a cat with a dog.'' We show that this failure is inherent to the…TableVision: Recovering Verified Structure from Historical Tables
Gaps, skips, and mistakes are the normal state of a historical register: rows go missing, tables cross page breaks, columns slip under wrong names, and column sums disagree with the corresponding…Efficient Fréchet Distributional Decoder Alignment for Latent Generative Models
Fréchet Distributional (FD) objectives provide an effective distribution-level signal for post-training generative models.Towards automatic digital garment assembly
Creating digital garments from existing two-dimensional patterns remains a time-consuming process.