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
AReA: Adaptive Receptive-Field Attention for Efficient High-Resolution Diffusion Transformers AReA uses an adaptive, coarse-to-fine receptive field attention mechanism to enable efficient high-resolution generation in diffusion transformers. Preprint	Multimodal models via arxiv · 15 Oct, 06:18	Not relevant Focuses on attention mechanisms in image diffusion models rather than multimodal reasoning.
AReA: Adaptive Receptive-Field Attention for Efficient High-Resolution Diffusion Transformers AReA uses an adaptive, coarse-to-fine receptive field attention mechanism to enable efficient high-resolution generation in diffusion transformers. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 06:18	Not relevant Focuses on diffusion transformers for image generation, not industrial inspection.
Beyond Sparse Captions: Aligning Slide-Level Text and Patch-Level Vision in Pathology LIMIT aligns slide-level pathology reports with patch-level vision features by freezing the vision encoder and training only the text encoder. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 06:18	Not relevant Deals with clinical pathology slides, not industrial defect detection.
When Left Is Right? Camera-Motion Understanding beyond Screen CameraVLM couples motion states and on-screen flow dynamics to improve camera-motion understanding in vision-language models. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 06:18	Not relevant Investigates camera motion understanding in VLMs, not industrial inspection.
HybridDualPathNet: Cross-Fusion Attention of EfficientNetV2 and Swin Transformer for Brain MRI Classification of Alzheimer's and Parkinson's Disease The authors introduce a hybrid architecture combining EfficientNetV2 and Swin Transformer to classify Alzheimer's and Parkinson's disease from brain MRI. Preprint	LLMs for code via arxiv · 15 Oct, 06:18	Not relevant Medical image classification; cs.SE category appears misapplied.
HybridDualPathNet: Cross-Fusion Attention of EfficientNetV2 and Swin Transformer for Brain MRI Classification of Alzheimer's and Parkinson's Disease The authors introduce a hybrid architecture combining EfficientNetV2 and Swin Transformer to classify Alzheimer's and Parkinson's disease from brain MRI. Preprint	Multimodal models via arxiv · 15 Oct, 06:18	Not relevant Addresses single-modality medical imaging, not multimodal language models.
HybridDualPathNet: Cross-Fusion Attention of EfficientNetV2 and Swin Transformer for Brain MRI Classification of Alzheimer's and Parkinson's Disease The authors introduce a hybrid architecture combining EfficientNetV2 and Swin Transformer to classify Alzheimer's and Parkinson's disease from brain MRI. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Studies medical diagnostic prediction rather than generative AI deployment safety.
HybridDualPathNet: Cross-Fusion Attention of EfficientNetV2 and Swin Transformer for Brain MRI Classification of Alzheimer's and Parkinson's Disease The authors introduce a hybrid architecture combining EfficientNetV2 and Swin Transformer to classify Alzheimer's and Parkinson's disease from brain MRI. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Evaluates medical classification models, not language model benchmarks.
HybridDualPathNet: Cross-Fusion Attention of EfficientNetV2 and Swin Transformer for Brain MRI Classification of Alzheimer's and Parkinson's Disease The authors introduce a hybrid architecture combining EfficientNetV2 and Swin Transformer to classify Alzheimer's and Parkinson's disease from brain MRI. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 06:18	Not relevant Focuses on medical brain MRI scans, not manufacturing inspection.
HybridDualPathNet: Cross-Fusion Attention of EfficientNetV2 and Swin Transformer for Brain MRI Classification of Alzheimer's and Parkinson's Disease The authors introduce a hybrid architecture combining EfficientNetV2 and Swin Transformer to classify Alzheimer's and Parkinson's disease from brain MRI. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Proposes a standard hybrid vision backbone, not model compression or efficiency.
HairFlow: Modeling 3D Hair Strands with Multi-Modal Conditioned Diffusion HairFlow generates 3D hair strands using a multimodal diffusion framework conditioned on coarse meshes and multi-view sketches. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 06:18	Not relevant Focuses on 3D hair modeling, not industrial defect detection.
HairFlow: Modeling 3D Hair Strands with Multi-Modal Conditioned Diffusion HairFlow generates 3D hair strands using a multimodal diffusion framework conditioned on coarse meshes and multi-view sketches. Preprint	Multimodal models via arxiv · 15 Oct, 06:18	Not relevant Covers conditional 3D mesh generation rather than multimodal reasoning or VLMs.
WIRE: Weight Space Implicit Representation Encoding of Frozen Video LLMs WIRE reduces video LLM inference costs by moving video computations from input tokens into temporary low-rank weight updates. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 06:18	Not relevant Focuses on video language model architecture, not industrial inspection.
Brain2Face: A Study on the Relation of EEG Signals and Spatio-temporal Head Avatars The paper presents a dataset and baseline model to track spatio-temporal human head avatars directly from EEG brain signals. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 06:18	Not relevant Addresses EEG-driven digital avatars, not industrial computer vision.
Brain2Face: A Study on the Relation of EEG Signals and Spatio-temporal Head Avatars The paper presents a dataset and baseline model to track spatio-temporal human head avatars directly from EEG brain signals. Preprint	Multimodal models via arxiv · 15 Oct, 06:18	Not relevant Focuses on EEG-to-avatar synthesis rather than multimodal foundation models.
TableVision: Recovering Verified Structure from Historical Tables TableVision recovers verified table structure from historical documents using dynamic programming and column sum checks. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 06:18	Not relevant Targets historical document table extraction rather than industrial manufacturing inspection.
Efficient Fréchet Distributional Decoder Alignment for Latent Generative Models Proposes a post-training decoder alignment method for latent generative models to resolve distribution mismatches between training and generation latents. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 06:18	Not relevant Focuses on generative image decoder optimization, not industrial inspection.
Efficient Fréchet Distributional Decoder Alignment for Latent Generative Models Proposes a post-training decoder alignment method for latent generative models to resolve distribution mismatches between training and generation latents. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Focuses on image generative alignment rather than small or quantized language models.
Efficient Fréchet Distributional Decoder Alignment for Latent Generative Models Proposes a post-training decoder alignment method for latent generative models to resolve distribution mismatches between training and generation latents. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Studies distribution alignment in image generation, not deployment guardrails or safety.
Efficient Fréchet Distributional Decoder Alignment for Latent Generative Models Proposes a post-training decoder alignment method for latent generative models to resolve distribution mismatches between training and generation latents. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Addresses generative model post-training alignment rather than LLM benchmarking.
Efficient Fréchet Distributional Decoder Alignment for Latent Generative Models Proposes a post-training decoder alignment method for latent generative models to resolve distribution mismatches between training and generation latents. Preprint	Multimodal models via arxiv · 15 Oct, 06:18	Not relevant Addresses image generation latent decoders rather than multimodal foundation models.
CÆNL: Collapse-Based Active Neural Learning with Objective-Aligned Representation Control Investigates active learning dynamics by regularizing representation collapse and managing sample acquisition across training iterations. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 06:18	Not relevant General active learning theory, not industrial visual inspection.
CÆNL: Collapse-Based Active Neural Learning with Objective-Aligned Representation Control Investigates active learning dynamics by regularizing representation collapse and managing sample acquisition across training iterations. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Focuses on active learning algorithms rather than LLM evaluation benchmarks.
CÆNL: Collapse-Based Active Neural Learning with Objective-Aligned Representation Control Investigates active learning dynamics by regularizing representation collapse and managing sample acquisition across training iterations. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Studies active learning optimization rather than AI deployment safety or robustness.
CÆNL: Collapse-Based Active Neural Learning with Objective-Aligned Representation Control Investigates active learning dynamics by regularizing representation collapse and managing sample acquisition across training iterations. Preprint	Multimodal models via arxiv · 15 Oct, 06:18	Not relevant Focuses on single-modality vision classification, not multimodal models.
CÆNL: Collapse-Based Active Neural Learning with Objective-Aligned Representation Control Investigates active learning dynamics by regularizing representation collapse and managing sample acquisition across training iterations. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Deals with sample acquisition in active learning, not model compression.
Gaussian Splatting for Real-Time Scene Reconstruction: Methods, Systems, and Trade-offs Surveys real-time 3D Gaussian Splatting scene reconstruction methods, systems, and practical trade-offs across five major technical categories. Peer-reviewed	Computer vision for industrial applications via arxiv · 15 Oct, 06:18	Not relevant Surveys general 3D Gaussian Splatting scene reconstruction, not industrial inspection.
Gaussian Splatting for Real-Time Scene Reconstruction: Methods, Systems, and Trade-offs Surveys real-time 3D Gaussian Splatting scene reconstruction methods, systems, and practical trade-offs across five major technical categories. Peer-reviewed	Multimodal models via arxiv · 15 Oct, 06:18	Not relevant Covers 3D novel view synthesis methods rather than multimodal vision-language models.
TwinPrune: Density-Aware Two-Phase Token Pruning for Vision-Language Models Proposes a two-phase token pruning strategy for vision-language models and identifies evaluation flaws in common decode-phase benchmarks. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 06:18	Not relevant Focuses on vision-language model token pruning, not industrial inspection.
CrossPortrait: Cross-Species Portrait Animation via Adversarial Motion Alignment Introduces a cross-species portrait animation framework that decomposes motion into head poses and expression keypoints to prevent driving-species leakage. Preprint	Multimodal models via arxiv · 15 Oct, 06:18	Not relevant Focuses on video face reenactment rather than multimodal foundation models.
CrossPortrait: Cross-Species Portrait Animation via Adversarial Motion Alignment Introduces a cross-species portrait animation framework that decomposes motion into head poses and expression keypoints to prevent driving-species leakage. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 06:18	Not relevant Deals with facial portrait animation rather than industrial visual inspection.
Physical-Field-Bridged Material Parameter Inference from Multi-View Deformation Videos Presents a single-pass framework inferring simulation-ready material parameters from multi-view deformation videos using projected physical response fields. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 06:18	Not relevant Addresses robotic deformable object manipulation rather than manufacturing quality control.
Physical-Field-Bridged Material Parameter Inference from Multi-View Deformation Videos Presents a single-pass framework inferring simulation-ready material parameters from multi-view deformation videos using projected physical response fields. Preprint	Multimodal models via arxiv · 15 Oct, 06:18	Not relevant Focuses on vision-based physical parameter estimation, not multimodal language models.
PLEDGE: Progressive Lossy Error-Bounded Compression of 3D Gaussian Splatting Presents an error-bounded progressive compression codec for 3D Gaussian Splatting with user-declared tolerances for primitive distortion. Preprint	Multimodal models via arxiv · 15 Oct, 06:18	Not relevant Addresses 3D Gaussian Splatting compression, not multimodal models.
PLEDGE: Progressive Lossy Error-Bounded Compression of 3D Gaussian Splatting Presents an error-bounded progressive compression codec for 3D Gaussian Splatting with user-declared tolerances for primitive distortion. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 06:18	Not relevant Focuses on 3D computer graphics compression, not industrial defect inspection.
Background2Background: a self-supervised background removal framework for high dimensional microscopy Introduces a self-supervised background removal framework for high-dimensional microscopy using local pixel shuffling and noise-reduction techniques. Preprint	Multimodal models via arxiv · 15 Oct, 06:18	Not relevant Addresses specialized optical microscopy imaging rather than multimodal language models.
Background2Background: a self-supervised background removal framework for high dimensional microscopy Introduces a self-supervised background removal framework for high-dimensional microscopy using local pixel shuffling and noise-reduction techniques. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 06:18	Not relevant Focuses on biological/scientific microscopy rather than manufacturing defect inspection.
Smartphone-Based, Field-Deployed AI Inspection System for Power Distribution Infrastructure Presents a field-deployed smartphone inspection system combining deep learning and geometry to assess power distribution infrastructure conditions. Preprint	Multimodal models via arxiv · 15 Oct, 06:18	Not relevant Applies standard vision techniques to inspection rather than advancing multimodal foundation models.
From Pixels to Executable Physics: Behavior-Grounded World Models for Embodied Action Consequence Prediction The paper presents a physically consistent world model incorporating robot-terrain contact dynamics to predict quadruped action outcomes from an RGB image. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 06:18	Not relevant Addresses quadruped robot locomotion, not industrial defect detection.
From Pixels to Executable Physics: Behavior-Grounded World Models for Embodied Action Consequence Prediction The paper presents a physically consistent world model incorporating robot-terrain contact dynamics to predict quadruped action outcomes from an RGB image. Preprint	Multimodal models via arxiv · 15 Oct, 06:18	Not relevant Focuses on video world models for robotics rather than multimodal foundation models.
Calibrated Disambiguation for Partial Multi-label Learning Proposes a calibrated disambiguation model to optimize sample weighting when training classifiers on datasets with noisy or excessive labels. Peer-reviewed	Computer vision for industrial applications via arxiv · 15 Oct, 06:18	Not relevant General machine learning disambiguation method, not industrial computer vision.
Calibrated Disambiguation for Partial Multi-label Learning Proposes a calibrated disambiguation model to optimize sample weighting when training classifiers on datasets with noisy or excessive labels. Peer-reviewed	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Addresses noisy multi-label training, not model compression or efficient serving.
Calibrated Disambiguation for Partial Multi-label Learning Proposes a calibrated disambiguation model to optimize sample weighting when training classifiers on datasets with noisy or excessive labels. Peer-reviewed	Multimodal models via arxiv · 15 Oct, 06:18	Not relevant Standard classification under label noise without multimodal model focus.
Calibrated Disambiguation for Partial Multi-label Learning Proposes a calibrated disambiguation model to optimize sample weighting when training classifiers on datasets with noisy or excessive labels. Peer-reviewed	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Focuses on partial-label classification, not deployment safety or guardrails.
Calibrated Disambiguation for Partial Multi-label Learning Proposes a calibrated disambiguation model to optimize sample weighting when training classifiers on datasets with noisy or excessive labels. Peer-reviewed	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Deals with multi-label learning, not language model evaluation or benchmarks.
FrameLite: Decision-Oriented Aesthetic Cropping with a Compact Vision-Language Model Proposes a lightweight aesthetic cropping model built on a small vision-language model using multi-stage candidate optimization. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 06:18	Not relevant Focuses on aesthetic image cropping, not manufacturing or industrial inspection.
MemAct: Streaming Spatial Understanding with Memory and Active Exploration Presents an embodied agent framework combining streaming spatial memory with active exploration for visual reasoning in physical environments. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 06:18	Not relevant Focuses on embodied spatial navigation and exploration, not industrial visual defect detection.
Code2Worlds-2: Physically Plausible Agent Harness for Embodied World Simulation Extends dynamic world generation to produce physics-executable simulations with collision meshes and physical dynamics for embodied agents. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 06:18	Not relevant Focuses on 4D simulation generation for embodied robotics, not industrial manufacturing inspection.
Code2Worlds-2: Physically Plausible Agent Harness for Embodied World Simulation Extends dynamic world generation to produce physics-executable simulations with collision meshes and physical dynamics for embodied agents. Preprint	Multimodal models via arxiv · 15 Oct, 06:18	Not relevant Focuses on compiling 3D/4D visual scenes into physics simulation formats rather than multimodal model capabilities.
Open-o3-Video-v2: Seek, Ground, and Reason with Verifiable Spatio-Temporal Evidence Develops an agentic video reasoning framework that iteratively identifies missing information, gathers visual evidence, and grounds temporal clues. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 06:18	Not relevant Focuses on spatio-temporal video question answering, not industrial defect or visual quality inspection.
ScenePercept: Scenario-Conditioned Perceptual Assessment of Real-World User-Generated Content Images Introduces an evaluation benchmark to assess user-generated image quality and aesthetics across diverse real-world scenes. Preprint	Multimodal models via arxiv · 15 Oct, 06:18	Not relevant Focuses on image quality and aesthetic assessment, not multimodal vision-language reasoning.
ScenePercept: Scenario-Conditioned Perceptual Assessment of Real-World User-Generated Content Images Introduces an evaluation benchmark to assess user-generated image quality and aesthetics across diverse real-world scenes. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 06:18	Not relevant Assesses perceptual quality of user-generated mobile photos, not industrial manufacturing quality control.
Integrating 3D Posture Neutralization with Statistical Shape Modeling for Head-Neck-Shoulder Ergonomic Design Presents a framework integrating posture neutralization with statistical shape modeling to isolate anatomical variations for ergonomic product design. Preprint	Multimodal models via arxiv · 15 Oct, 06:18	Not relevant Deals with 3D scan shape modeling and posture neutralization, not multimodal language models.
Integrating 3D Posture Neutralization with Statistical Shape Modeling for Head-Neck-Shoulder Ergonomic Design Presents a framework integrating posture neutralization with statistical shape modeling to isolate anatomical variations for ergonomic product design. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 06:18	Not relevant Focuses on 3D anatomical modeling for ergonomic product design, not industrial defect detection.
Deficit-Driven Stratified Acquisition for Building STRATA-S12: An Open SAR-Optical Corpus for Self-Supervised Learning Introduces a deficit-driven stratified acquisition algorithm to create a balanced, co-registered satellite imagery corpus for self-supervised Earth observation. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Presents an acquisition strategy for satellite imagery, not an evaluation framework for LLMs.
Deficit-Driven Stratified Acquisition for Building STRATA-S12: An Open SAR-Optical Corpus for Self-Supervised Learning Introduces a deficit-driven stratified acquisition algorithm to create a balanced, co-registered satellite imagery corpus for self-supervised Earth observation. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Focuses on remote sensing dataset collection, not model compression or efficient inference.
Deficit-Driven Stratified Acquisition for Building STRATA-S12: An Open SAR-Optical Corpus for Self-Supervised Learning Introduces a deficit-driven stratified acquisition algorithm to create a balanced, co-registered satellite imagery corpus for self-supervised Earth observation. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Focuses on satellite data collection rather than deployment safety, guardrails, or hallucination.
Deficit-Driven Stratified Acquisition for Building STRATA-S12: An Open SAR-Optical Corpus for Self-Supervised Learning Introduces a deficit-driven stratified acquisition algorithm to create a balanced, co-registered satellite imagery corpus for self-supervised Earth observation. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 06:18	Not relevant Focuses on Earth observation satellite SAR-optical dataset acquisition, not manufacturing inspection.
Hierarchical Curriculum Learning for Surgical Video Generation: Focusing on Bilateral Axillo-Breast Approach Robotic Thyroidectomy Datasets Adapts text-to-video diffusion models for surgical procedure simulation using hierarchical procedural decomposition and progressive curriculum learning. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 06:18	Not relevant Focuses on robotic surgical video generation, not manufacturing or industrial visual inspection.
SWORD: Scene-Grounded Wasserstein Object-Relation Distance for 3D Scene Graph Prediction Introduces a scene-grounded Wasserstein distance objective to optimize graph-level semantic proximity in 3D scene graph prediction tasks. Peer-reviewed	Computer vision for industrial applications via arxiv · 15 Oct, 06:18	Not relevant Focuses on 3D scene graph prediction losses, not manufacturing or industrial inspection.

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