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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 145
MFVSR: A Multi-Style and Fine-Grained Benchmark for Video Subtitle Removal
Video subtitle removal (VSR) is an important video restoration task, yet existing evaluation remains limited by low-resolution datasets, fragmented test sets, and generic video-inpainting protocols…Simplifying and Improving On-Policy Distillation for Few-Step Diffusion
Few-step distillation reduces the sampling cost of diffusion and flow models, but existing approaches often require auxiliary score models or specialized architectures.Physics-Aware Deepfake Detection via Distance-Speech Consistency
Recent progress in generative models has made talking-head deepfakes highly realistic and easy to create, jeopardizing public trust.EgoOmniWorld: Unifying Egocentric Understanding via Ego-Action-World Interactions
Egocentric video understanding spans diverse capabilities, modalities, and evaluation formats, yet these are often studied in isolation despite their tight coupling in real-world interaction.Dual-Domain Sparse-View 4D CBCT Reconstruction with Spatial-Temporal Consistency Learning
Four-dimensional cone-beam computed tomography (4D CBCT) provides phase-resolved volumetric images for image-guided radiotherapy.Meta-Video: Efficient Standardized Evaluation for Video Understanding
Video understanding benchmarks are the primary yardstick for measuring progress in video-language modeling, and a growing body of work relies on them to claim improvements.Beyond the Encoder: Object-Centric Representations Emerging from Segmentation Foundation Models
Foundation models are increasingly used as feature extractors for downstream tasks with limited data, typically through representations obtained from their encoders.Unbalanced W-Flow: Unbalanced Wasserstein Drifting under Finite-Batch Marginal Fluctuations
Teacher-free one-step generative models such as W-Flow learn directly from distributional objectives, with the Sinkhorn divergence providing the strongest empirical performance among the energies…Denoise X: Self-Supervised U-Net for Quantum Mottle Suppression in Chest Radiography
Deep learning for medical image restoration is often constrained by the need for paired training data.Observation Quality in DSM-Assisted Aerial Localization: A Benchmark and Base-Anchored Uncertainty Calibration
Aerial map matching provides position observations for navigation, but a position error alone does not determine how an observation should be weighted.Parameter-Efficient Multimodal Brain Tumor Segmentation With Stage-Wise Shared Adapters
Different MRI modalities reveal different parts of a brain tumor.Detecting Unreliable Classifications from Quantized Models
Post-training INT8 quantization is typically validated by checking accuracy retention, which requires ground-truth labels often unavailable in low-resource clinical settings.Rethinking Projector Training in Multimodal LLMs
Multimodal LLMs are typically trained by first aligning a vision encoder with a language model via projector training on large image-caption datasets, followed by joint training of the full…LLM2R: From Large Language Models to 3D Reassemblers
3D part assembly aims to reconstruct a complete shape from a set of unassembled parts by predicting a rigid SE(3) transformation for each part.Recasting DiT for Extreme Image Compression with Hierarchical Visual Priors
Pretrained diffusion models provide powerful generative priors for extreme image compression, but their stochastic, iterative generation processes are not designed to decode compressed…DPOison-VL: Clean Preference Poisoning in Vision-Language Direct Preference Optimization
Direct Preference Optimization (DPO) has become a widely adopted approach for aligning vision-language models (VLMs), yet the security of multimodal preference data remains largely unexplored.VideoJev: System One Model for Video Understanding and Interaction
Visual agents must turn video observations into timely decisions to track progress and choose what to do next.MajorTOM:Elliot Pretrain, A Multi-Sensor, Multi-Temporal Pretraining Dataset for Earth Observation
Pretraining generalist Earth observation foundation models requires data that combine several sensors, varied temporal sampling and, increasingly, text, yet open datasets rarely provide all three in…Grid or Nodes? Probing Object Memory in World Models Under Partial Observability
A robot's world model must remember objects that leave the camera's view.ST-GRU PredRNN-V2: A GRU-Based Reformulation of PredRNN-V2 for Video Prediction
Video prediction requires models to capture complex spatiotemporal dependencies and to retain relevant information over long sequences.