Every paper checked — nothing hidden

All papers

Every paper checked for brief #4 of 12–19 Oct 2026 — nothing is hidden. Brief papers first.

All17,313 In brief24 More (on-topic)3,333 Not relevant13,754 Not assessed87 Older352 Could not confirm93 Blogs with no paper266 All blogs284

Paper	Topic	Relevance
A Gradient-Regularized Deep Learning Method for Restoring Image in Myopic Vision A gradient-regularized U-Net framework transforms images to counteract spherical eye defocus aberrations and improve visual clarity for myopic individuals. Preprint	Multimodal models via arxiv · 15 Oct, 15:06	Not relevant Presents image-to-image defocus correction, not multimodal vision-language models.
Riding the Fixed-Point Curve: Image Classification in the Continuous Thought Machine The paper investigates the emergent dynamics of the Continuous Thought Machine during image classification using non-normal maps and fixed-point curves. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 15:06	Not relevant Analyzes theoretical recurrent neural dynamics, not industrial vision inspection.
Riding the Fixed-Point Curve: Image Classification in the Continuous Thought Machine The paper investigates the emergent dynamics of the Continuous Thought Machine during image classification using non-normal maps and fixed-point curves. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Investigates neural dynamical properties, not LLM benchmark evaluation.
Riding the Fixed-Point Curve: Image Classification in the Continuous Thought Machine The paper investigates the emergent dynamics of the Continuous Thought Machine during image classification using non-normal maps and fixed-point curves. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Theoretical neural analysis without direct focus on safety or guardrails.
Riding the Fixed-Point Curve: Image Classification in the Continuous Thought Machine The paper investigates the emergent dynamics of the Continuous Thought Machine during image classification using non-normal maps and fixed-point curves. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Studies recurrent neural mechanisms, not model quantization or efficient inference.
Riding the Fixed-Point Curve: Image Classification in the Continuous Thought Machine The paper investigates the emergent dynamics of the Continuous Thought Machine during image classification using non-normal maps and fixed-point curves. Preprint	Multimodal models via arxiv · 15 Oct, 15:06	Not relevant Explores unimodal image classification dynamics, not multimodal models.
Long-horizon Video Generation with Slot Memory Addressed By Region Tokens SMART introduces a slot-memory mechanism addressed by region tokens to achieve bounded memory and spatially consistent recall in long-horizon video generation. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 15:06	Not relevant Focuses on video generation architectures, not industrial defect detection.
Long-horizon Video Generation with Slot Memory Addressed By Region Tokens SMART introduces a slot-memory mechanism addressed by region tokens to achieve bounded memory and spatially consistent recall in long-horizon video generation. Preprint	Multimodal models via arxiv · 15 Oct, 15:06	Not relevant Covers long-horizon video generation mechanisms, not multimodal cross-modality understanding.
LAMP: 3D Source-Aware Lighting Estimation at Multiple Points The paper proposes a framework using vision-language reasoning to estimate point-conditioned 3D directions and physical light source contributions. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 15:06	Not relevant Studies 3D lighting estimation in scenes, not industrial defect inspection.
Deep Learning for Atmospheric Turbulence Mitigation: A Comparison Between GANs and Transformers The paper compares GAN and Transformer architectures for mitigating atmospheric turbulence distortions in multi-frame imaging. Peer-reviewed	Computer vision for industrial applications via arxiv · 15 Oct, 15:06	Not relevant Focuses on atmospheric image turbulence reduction, not factory defect detection.
Deep Learning for Atmospheric Turbulence Mitigation: A Comparison Between GANs and Transformers The paper compares GAN and Transformer architectures for mitigating atmospheric turbulence distortions in multi-frame imaging. Peer-reviewed	Multimodal models via arxiv · 15 Oct, 15:06	Not relevant Compares image restoration methods on single imagery, not multimodal models.
Kandinsky S2V 1.0: Audio-Driven Avatar Video Generation The paper presents open speech-to-video models that generate long-form avatar video with expressive motion driven by an audio waveform. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 15:06	Not relevant Presents speech-to-video avatar generation, not industrial manufacturing vision.
Data-free Multistep Diffusion Distillation The paper proposes a data-free multistep distillation method for text-to-image diffusion models to improve generation fidelity across sampling budgets. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 15:06	Not relevant Proposes image diffusion distillation, not industrial visual quality control.
Data-free Multistep Diffusion Distillation The paper proposes a data-free multistep distillation method for text-to-image diffusion models to improve generation fidelity across sampling budgets. Preprint	Multimodal models via arxiv · 15 Oct, 15:06	Not relevant Focuses on diffusion model distillation, not multimodal language-vision architectures.
SonoWorld: Towards Interactive Pose-Controllable Ultrasound World Models The paper proposes an interactive ultrasound world model that learns video dynamics and maps them to six-degree-of-freedom probe control. Preprint	Multimodal models via arxiv · 15 Oct, 15:06	Not relevant Develops a diffusion-based ultrasound world model, not multimodal vision-language reasoning.
SonoWorld: Towards Interactive Pose-Controllable Ultrasound World Models The paper proposes an interactive ultrasound world model that learns video dynamics and maps them to six-degree-of-freedom probe control. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 15:06	Not relevant Focuses on robotic ultrasound medical imaging, not industrial manufacturing inspection.
NavAble: A Large-Scale Dataset and Synthetic Data Generation Pipeline for Blind Navigation The paper introduces a dataset and synthetic generation pipeline focused on recognizing accessibility-critical objects to assist blind navigation. Preprint	Multimodal models via arxiv · 15 Oct, 15:06	Not relevant Introduces a navigation dataset and synthetic rendering pipeline, not multimodal models.
NavAble: A Large-Scale Dataset and Synthetic Data Generation Pipeline for Blind Navigation The paper introduces a dataset and synthetic generation pipeline focused on recognizing accessibility-critical objects to assist blind navigation. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 15:06	Not relevant Focuses on assistive navigation for visually impaired users, not manufacturing.
MMKG-R1: Constructing Multimodal Knowledge Graphs via Reinforcement Learning The paper proposes a reinforcement learning framework using quality-aware rewards to guide multimodal knowledge graph construction for retrieval-augmented generation. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 15:06	Not relevant Covers multimodal knowledge graphs and RAG, not industrial defect inspection.
Integrated Novelty Detection Systems: From Sensor Networks to Multi-Novelty Computer Vision The paper investigates novelty and anomaly detection techniques across rain gauge sensor networks and computer vision scenarios. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Not related to model compression, quantization, or small LLMs.
Integrated Novelty Detection Systems: From Sensor Networks to Multi-Novelty Computer Vision The paper investigates novelty and anomaly detection techniques across rain gauge sensor networks and computer vision scenarios. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Examines sensor novelty detection, not language model benchmark methodologies.
Integrated Novelty Detection Systems: From Sensor Networks to Multi-Novelty Computer Vision The paper investigates novelty and anomaly detection techniques across rain gauge sensor networks and computer vision scenarios. Preprint	Multimodal models via arxiv · 15 Oct, 15:06	Not relevant Addresses sensor anomaly detection, not multimodal models.
Integrated Novelty Detection Systems: From Sensor Networks to Multi-Novelty Computer Vision The paper investigates novelty and anomaly detection techniques across rain gauge sensor networks and computer vision scenarios. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 15:06	Not relevant Focuses on weather sensor networks and general vision, not manufacturing.
The Impact of Detector Architecture, Model Scale, and Scene Density on Multi-Pedestrian Tracking The paper benchmarks how detector architectures, model scale, and crowd density influence multi-pedestrian tracking performance in video sequences. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Benchmarking pedestrian trackers, not AI safety, hallucination, or guardrails.
The Impact of Detector Architecture, Model Scale, and Scene Density on Multi-Pedestrian Tracking The paper benchmarks how detector architectures, model scale, and crowd density influence multi-pedestrian tracking performance in video sequences. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 15:06	Not relevant Focuses on pedestrian tracking benchmarks, not manufacturing quality control.
The Impact of Detector Architecture, Model Scale, and Scene Density on Multi-Pedestrian Tracking The paper benchmarks how detector architectures, model scale, and crowd density influence multi-pedestrian tracking performance in video sequences. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Surveys existing detector architectures rather than proposing efficient serving techniques.
The Impact of Detector Architecture, Model Scale, and Scene Density on Multi-Pedestrian Tracking The paper benchmarks how detector architectures, model scale, and crowd density influence multi-pedestrian tracking performance in video sequences. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Evaluates vision object detectors, not LLM benchmarks or evaluation.
The Impact of Detector Architecture, Model Scale, and Scene Density on Multi-Pedestrian Tracking The paper benchmarks how detector architectures, model scale, and crowd density influence multi-pedestrian tracking performance in video sequences. Preprint	Multimodal models via arxiv · 15 Oct, 15:06	Not relevant Addresses unimodal video object detection without multimodal integration.
Robust Explanatory Analysis for Structured Optimization Networks (REASON) in Learning The paper proposes a hierarchical two-loop optimization framework that uses residual errors to trigger active evidence acquisition during inference. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 15:06	Not relevant General learning framework, not industrial visual inspection.
Robust Explanatory Analysis for Structured Optimization Networks (REASON) in Learning The paper proposes a hierarchical two-loop optimization framework that uses residual errors to trigger active evidence acquisition during inference. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Focuses on two-loop inference optimization, not LLM benchmark evaluation.
Robust Explanatory Analysis for Structured Optimization Networks (REASON) in Learning The paper proposes a hierarchical two-loop optimization framework that uses residual errors to trigger active evidence acquisition during inference. Preprint	Multimodal models via arxiv · 15 Oct, 15:06	Not relevant Not focused on multimodal vision-language architectures or cross-modal tasks.
Robust Explanatory Analysis for Structured Optimization Networks (REASON) in Learning The paper proposes a hierarchical two-loop optimization framework that uses residual errors to trigger active evidence acquisition during inference. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Investigates evidence acquisition loops, not model compression or small models.
Train to Stitch: Topology-Preserving Regularization for Interoperable Representations The paper introduces topological regularization to align latent spaces across independently trained autoencoders without inter-model communication. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Proposes representation regularization, not model distillation, quantization, or pruning.
Train to Stitch: Topology-Preserving Regularization for Interoperable Representations The paper introduces topological regularization to align latent spaces across independently trained autoencoders without inter-model communication. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Focuses on representation stitching, not LLM evaluation or benchmarks.
Train to Stitch: Topology-Preserving Regularization for Interoperable Representations The paper introduces topological regularization to align latent spaces across independently trained autoencoders without inter-model communication. Preprint	Multimodal models via arxiv · 15 Oct, 15:06	Not relevant Covers general neural representations, not multimodal vision-language models.
Train to Stitch: Topology-Preserving Regularization for Interoperable Representations The paper introduces topological regularization to align latent spaces across independently trained autoencoders without inter-model communication. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 15:06	Not relevant Addresses general autoencoder latent alignment, not industrial defect detection.
Train to Stitch: Topology-Preserving Regularization for Interoperable Representations The paper introduces topological regularization to align latent spaces across independently trained autoencoders without inter-model communication. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Theoretical latent space alignment, not AI safety or deployment guardrails.
WinARDet: Compressed Window Auto-Regression for Long-Horizon Vision-Language Grounding The paper proposes compressed window autoregression to prevent repetitive bounding box errors in vision-language object grounding tasks. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 15:06	Not relevant Focuses on vision-language grounding in dense scenes, not manufacturing inspection.
Learning to Select Features with Manifold Deep Unfolding PCA The paper proposes a deep unfolding network integrating manifold optimization for unsupervised feature selection in image processing. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 15:06	Not relevant Theoretical unsupervised feature selection algorithm, not industrial vision defect detection.
Learning to Select Features with Manifold Deep Unfolding PCA The paper proposes a deep unfolding network integrating manifold optimization for unsupervised feature selection in image processing. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Focuses on PCA optimization, not AI safety or reliability.
Learning to Select Features with Manifold Deep Unfolding PCA The paper proposes a deep unfolding network integrating manifold optimization for unsupervised feature selection in image processing. Preprint	Multimodal models via arxiv · 15 Oct, 15:06	Not relevant Not related to multimodal models or vision-language processing.
Learning to Select Features with Manifold Deep Unfolding PCA The paper proposes a deep unfolding network integrating manifold optimization for unsupervised feature selection in image processing. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Focuses on dimensionality reduction, not model compression or efficient serving.
Learning to Select Features with Manifold Deep Unfolding PCA The paper proposes a deep unfolding network integrating manifold optimization for unsupervised feature selection in image processing. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Addresses classical feature selection, not LLM evaluation or benchmarks.
DeBoneDiT: Depth-Driven Conditional Bridge Diffusion Transformers for Bone Suppression in Chest X-Ray Images The paper proposes a depth-conditioned diffusion transformer architecture to suppress overlapping bone structures in chest X-ray images. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 15:06	Not relevant Focuses on medical chest X-ray imaging, not industrial manufacturing inspection.
DeBoneDiT: Depth-Driven Conditional Bridge Diffusion Transformers for Bone Suppression in Chest X-Ray Images The paper proposes a depth-conditioned diffusion transformer architecture to suppress overlapping bone structures in chest X-ray images. Preprint	Multimodal models via arxiv · 15 Oct, 15:06	Not relevant Covers image-to-image bone suppression diffusion, not multimodal vision-language models.
Robota-Embed: Unified VLA Embeddings for Robot In-Context Learning and Evolution The paper introduces an embedding model that jointly encodes vision, language, and action to retrieve relevant historical trajectories for robot learning. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 15:06	Not relevant Addresses robotics policy retrieval, not visual inspection on production lines.
Holi-Spatial 360: Generative Reconstruction from Sparse Panoramas for Spatial Rendering and Intelligence The paper introduces a generative pipeline to reconstruct multi-room 3D environments from sparse panorama images using geometric optimization. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 15:06	Not relevant Focuses on real-estate indoor 3D reconstruction, not manufacturing quality inspection.
Spectral World Renderer: Unified 3D Hyperspectral Unmixing The paper introduces a synthetic world generator and benchmark for evaluating 3D hyperspectral material unmixing within novel view rendering. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 15:06	Not relevant Develops a 3D hyperspectral renderer rather than an industrial inspection system.
HyrCap: Hybrid Rank-Calibration of Action Proposals for Temporal Event Understanding The paper introduces a rank-calibration framework to improve proposal localization quality for temporal action detection in long untrimmed videos. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 15:06	Not relevant Focuses on temporal video action detection, not industrial defect detection.
Reducing the Gap Between Spatial and Space-Filling Curve Neighbors in 3D Point Cloud Segmentation The paper proposes a loss function that reduces spatial neighborhood discrepancies in space-filling curve representations for 3D point cloud segmentation. Preprint	Multimodal models via arxiv · 15 Oct, 15:06	Not relevant Exclusively studies 3D point cloud geometric loss functions without multimodal components.
Reducing the Gap Between Spatial and Space-Filling Curve Neighbors in 3D Point Cloud Segmentation The paper proposes a loss function that reduces spatial neighborhood discrepancies in space-filling curve representations for 3D point cloud segmentation. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 15:06	Not relevant Addresses 3D point cloud segmentation geometry, not factory quality control.
RDCompGS: Rate-Distortion Optimization for Compression of 3D Gaussian Splatting with Image Codecs The paper proposes an optimization method for 3D Gaussian Splatting that improves rate-distortion performance when compressing scenes with image codecs. Preprint	Multimodal models via arxiv · 15 Oct, 15:06	Not relevant Investigates rate-distortion compression of 3D representations, not cross-modal understanding.
RDCompGS: Rate-Distortion Optimization for Compression of 3D Gaussian Splatting with Image Codecs The paper proposes an optimization method for 3D Gaussian Splatting that improves rate-distortion performance when compressing scenes with image codecs. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 15:06	Not relevant Focuses on 3DGS compression algorithms, not industrial manufacturing inspection.
Cross-Scenario Industrial Defect Detection via Domain-Robust Training and Metric-Aligned Selection The paper presents a domain-generalization method to detect industrial board defects reliably across varied production environments and lighting conditions. Preprint	Multimodal models via arxiv · 15 Oct, 15:06	Not relevant Addresses standard 2D computer vision defect classification, not multimodal models.
TextVerifier: Seeing Beyond Recognition to Verify Chinese Text Rendering The paper introduces TEXTVERIFIER, a verification framework using character structural sequences to detect fine rendering defects in generated Chinese text. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 15:06	Not relevant Detects font rendering errors in generated images, not industrial defects.
Learning Visual Speech Representations via Cross-Modal Distillation and Joint Face-Lip Modeling The paper introduces FLARE, a visual speech framework combining cross-modal distillation from audio features with dual-stream face and lip modeling. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 15:06	Not relevant Concerns speech and lipreading, not manufacturing defect inspection.
SaTPO: Text-to-Image Safety Alignment via Transition Preference Optimization The paper proposes a step-wise alignment method to suppress harmful content in text-to-image models while maintaining generation semantics. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 15:06	Not relevant Focuses on generative image safety alignment, not industrial anomaly detection.
What Breaks in Multi-Image Schema Application? Factorial Oracle Interventions Reveal an Instance-Information Bottleneck Uses factorial interventions to locate failure points in multi-image vision-language reasoning pipelines. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 15:06	Not relevant Investigates reasoning bottlenecks in VLMs, not industrial inspection.
MagicGarment: Benchmarking Single-Image Standalone 3D Garment Reconstruction Introduces a benchmark dataset and baseline architecture for reconstructing standalone 3D garments from single images. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 15:06	Not relevant Deals with 3D garment reconstruction, not industrial visual quality control.
Adaptive Observation Control for Active Visual Recognition Adapts both field of view and fixation counts per image to optimize computation in active visual recognition. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 15:06	Not relevant General active visual recognition rather than manufacturing defect inspection.

2,881–2,940 of 13,754← Previous Next →