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
3D-VITAL: Visibility-aware Identity Training from Artificial Liftings of 3D Reconstruction model for Cross-View Object Re-Identification The paper presents a method using 3D mesh reconstruction and visibility-aware training to improve cross-view object re-identification. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 06:18	Not relevant Concerns object re-identification across viewpoints, not manufacturing quality control.
3D-VITAL: Visibility-aware Identity Training from Artificial Liftings of 3D Reconstruction model for Cross-View Object Re-Identification The paper presents a method using 3D mesh reconstruction and visibility-aware training to improve cross-view object re-identification. Preprint	Multimodal models via arxiv · 15 Oct, 06:18	Not relevant Focuses on single-modality 3D reconstruction and ReID, not multimodal models.
PathPilot: An Adaptive Question-Conditioned Multi-Magnification Navigation Agent for Whole-Slide Image Analysis The paper proposes an adaptive whole-slide image navigation agent that models cross-magnification localization for pathology diagnosis. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 06:18	Not relevant Focuses on medical histopathology slide examination, not industrial manufacturing.
FoveaTree: Information-Density Trees for Query-Guided Visual Token Pruning in Video Large Language Models The paper introduces a training-free visual token pruning framework using information-density trees to speed up video large language model inference. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 06:18	Not relevant Addresses video large language model token pruning, not industrial inspection.
The Midas Touch for Metric Depth: Interpretable Metricization for Generalizable Depth Perception The paper introduces a hybrid framework combining explicit depth metricization with data-driven refinement to recover metric depth from relative estimates. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 06:18	Not relevant Covers monocular metric depth perception, not industrial defect detection.
The Midas Touch for Metric Depth: Interpretable Metricization for Generalizable Depth Perception The paper introduces a hybrid framework combining explicit depth metricization with data-driven refinement to recover metric depth from relative estimates. Preprint	Multimodal models via arxiv · 15 Oct, 06:18	Not relevant Standard single-modality 3D vision estimation, not multimodal reasoning.
Image Tokenizers Should Be Trained Between Images The paper presents a method that fine-tunes image tokenizers on interpolated latent codes to reduce decoding artifacts in diffusion generation. Preprint	Multimodal models via arxiv · 15 Oct, 06:18	Not relevant Focuses on vision tokenizers for image generation rather than multimodal reasoning.
Image Tokenizers Should Be Trained Between Images The paper presents a method that fine-tunes image tokenizers on interpolated latent codes to reduce decoding artifacts in diffusion generation. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 06:18	Not relevant Concerns latent diffusion image generation, not industrial visual inspection.
Towards Complementary Keypoint Detection via Mixture of Detectors The paper presents a framework that distills multiple keypoint detectors and dynamically routes them per patch for dense image matching. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 06:18	Not relevant Deals with general geometric keypoint matching, not industrial quality inspection.
Towards Complementary Keypoint Detection via Mixture of Detectors The paper presents a framework that distills multiple keypoint detectors and dynamically routes them per patch for dense image matching. Preprint	Multimodal models via arxiv · 15 Oct, 06:18	Not relevant Focuses purely on geometric vision detectors, not multimodal models.
ViewStruct: View-Grounded Articulation and Connectivity Inference The paper proposes a vision-language framework to predict rigid-body connectivity and articulation parameters of objects from single views. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 06:18	Not relevant Addresses 3D object articulation for robotics, not manufacturing defect detection.
X-Ragent: Tool-Augmented Reinforcement Learning for Evidence-Grounded Chest X-Ray Reasoning The paper introduces a tool-augmented reinforcement learning framework for evidence-grounded chest X-ray diagnostic reasoning. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 06:18	Not relevant Applies to medical chest X-ray interpretation, not industrial manufacturing inspection.
Carve VFM: Carving Specialists out of Generalists, Automatic Task-Specific Compression of Vision Foundation Models The paper proposes a structured pruning method to compress vision foundation models into compact task-specific models under parameter budgets. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 06:18	Not relevant Focuses on vision foundation model compression, not industrial quality control.
Carve VFM: Carving Specialists out of Generalists, Automatic Task-Specific Compression of Vision Foundation Models The paper proposes a structured pruning method to compress vision foundation models into compact task-specific models under parameter budgets. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant About model compression efficiency, not AI safety, hallucination, or alignment.
Carve VFM: Carving Specialists out of Generalists, Automatic Task-Specific Compression of Vision Foundation Models The paper proposes a structured pruning method to compress vision foundation models into compact task-specific models under parameter budgets. Preprint	Multimodal models via arxiv · 15 Oct, 06:18	Not relevant Focuses broadly on vision model pruning rather than multimodal text-image interactions.
Carve VFM: Carving Specialists out of Generalists, Automatic Task-Specific Compression of Vision Foundation Models The paper proposes a structured pruning method to compress vision foundation models into compact task-specific models under parameter budgets. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Introduces a pruning technique, not LLM benchmarks or evaluation methods.
A Multi-Task Deep Learning Framework for Pose-Invariant Face Recognition and Facial Landmark Localization The paper presents a dual-branch neural network designed to simultaneously perform pose-invariant face recognition and facial landmark localization. Preprint	Multimodal models via arxiv · 15 Oct, 06:18	Not relevant Standard computer vision task on facial images, not multimodal models.
A Multi-Task Deep Learning Framework for Pose-Invariant Face Recognition and Facial Landmark Localization The paper presents a dual-branch neural network designed to simultaneously perform pose-invariant face recognition and facial landmark localization. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 06:18	Not relevant Addresses face recognition and landmark localization, not industrial defect detection.
Vision Models Capture Complementary Representations in Children That Converge in Adults The paper uses EEG data to compare how image-text aligned and vision-only models align with developing human visual representations. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 06:18	Not relevant Examines human developmental neuroscience representations, not industrial computer vision.
From Volpiano to Pixels: 30,000 Lines of OMR Ground Truth for Medieval Chant The paper introduces an optical music recognition dataset and pipeline linking medieval chant manuscript images to musical target tokens. Preprint	Multimodal models via arxiv · 15 Oct, 06:18	Not relevant OMR dataset paper, not general multimodal reasoning or VLMs.
From Volpiano to Pixels: 30,000 Lines of OMR Ground Truth for Medieval Chant The paper introduces an optical music recognition dataset and pipeline linking medieval chant manuscript images to musical target tokens. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:18	Not relevant About optical music recognition, not retrieval-augmented generation systems.
From Volpiano to Pixels: 30,000 Lines of OMR Ground Truth for Medieval Chant The paper introduces an optical music recognition dataset and pipeline linking medieval chant manuscript images to musical target tokens. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 06:18	Not relevant Focuses on historical manuscript transcription, not industrial visual inspection.
EviStress: Evidence Stress Learning for Generalizable Face Forgery Detection with Textual Explanations The paper introduces a framework combining targeted patch perturbation with textual explanations to improve face forgery detection generalization. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 06:18	Not relevant Focuses on face forgery detection, not industrial manufacturing inspection.
Barycentric Guidance: Turning Foundation Image Editors into Continuous Affective Controllers The paper proposes a training-free guidance method enabling continuous valence-arousal emotional control in foundation image editors. Preprint	Multimodal models via arxiv · 15 Oct, 06:18	Not relevant Focuses on text-guided affective image editing, not multimodal understanding.
Barycentric Guidance: Turning Foundation Image Editors into Continuous Affective Controllers The paper proposes a training-free guidance method enabling continuous valence-arousal emotional control in foundation image editors. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Studies affective image generation control, not deployment safety or robustness.
Barycentric Guidance: Turning Foundation Image Editors into Continuous Affective Controllers The paper proposes a training-free guidance method enabling continuous valence-arousal emotional control in foundation image editors. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Proposes an image editing controller, not LLM evaluation benchmarks.
Barycentric Guidance: Turning Foundation Image Editors into Continuous Affective Controllers The paper proposes a training-free guidance method enabling continuous valence-arousal emotional control in foundation image editors. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 06:18	Not relevant Focuses on facial emotion image editing, not industrial inspection.
Barycentric Guidance: Turning Foundation Image Editors into Continuous Affective Controllers The paper proposes a training-free guidance method enabling continuous valence-arousal emotional control in foundation image editors. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Does not address model compression, pruning, or efficient inference.
Mining Cross-Modal Perception Gaps in Autonomous Driving: When Camera and LiDAR Disagree The paper analyzes camera and LiDAR detection discrepancies across driving datasets to evaluate assumptions in multimodal fusion design. Preprint	Multimodal models via arxiv · 15 Oct, 06:18	Not relevant Examines camera-LiDAR sensor fusion for autonomous vehicles.
Mining Cross-Modal Perception Gaps in Autonomous Driving: When Camera and LiDAR Disagree The paper analyzes camera and LiDAR detection discrepancies across driving datasets to evaluate assumptions in multimodal fusion design. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 06:18	Not relevant Focuses on autonomous driving perception, which is explicitly excluded.
ConDenseDrive: Knowledge-Dense, Token-Sparse Visual Encoder for End-to-End Autonomous Driving ConDenseDrive combines knowledge distillation and token pruning to create an efficient visual encoder for end-to-end autonomous driving. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 06:18	Not relevant Focuses on autonomous driving perception, explicitly excluded from this topic.
ConDenseDrive: Knowledge-Dense, Token-Sparse Visual Encoder for End-to-End Autonomous Driving ConDenseDrive combines knowledge distillation and token pruning to create an efficient visual encoder for end-to-end autonomous driving. Preprint	Multimodal models via arxiv · 15 Oct, 06:18	Not relevant Focuses on visual encoders for autonomous driving planning.
FOCUS: A Subspecialty Foundation Model for Intraoperative Central Nervous System Pathology FOCUS is a specialized pathology foundation model pretrained on intraoperative frozen-section slides of central nervous system tissue. Preprint	Multimodal models via arxiv · 15 Oct, 06:18	Not relevant Focuses on pathology image classification, not multimodal models.
FOCUS: A Subspecialty Foundation Model for Intraoperative Central Nervous System Pathology FOCUS is a specialized pathology foundation model pretrained on intraoperative frozen-section slides of central nervous system tissue. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 06:18	Not relevant Focuses on intraoperative digital pathology, not manufacturing defect detection.
UniMetric: Universal Metric Scale Recovery for Visual Geometry Foundation Models UniMetric provides a training-free framework that recovers metric scale in visual geometry foundation models using object size priors. Preprint	Multimodal models via arxiv · 15 Oct, 06:18	Not relevant Addresses 3D geometric scale recovery rather than multimodal reasoning.
UniMetric: Universal Metric Scale Recovery for Visual Geometry Foundation Models UniMetric provides a training-free framework that recovers metric scale in visual geometry foundation models using object size priors. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 06:18	Not relevant Studies metric scale recovery for 3D reconstruction, not manufacturing.
DDVR-Net: Degradation-Aware Decoupling and Visibility-Guided Restoration for Historical Document Recognition DDVR-Net decouples glyph structures from physical damage and uses visibility-guided restoration for historical document OCR. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 06:18	Not relevant Focuses on historical document OCR, not manufacturing quality inspection.
UniLAP: A Unified Latent Action Policy for Cross-Embodiment Manipulation UniLAP introduces a continuous latent action policy trained via flow matching to enable robot manipulation without relying on future frames. Preprint	Multimodal models via arxiv · 15 Oct, 06:18	Not relevant Focuses on robotics action representation, not multimodal reasoning models.
UniLAP: A Unified Latent Action Policy for Cross-Embodiment Manipulation UniLAP introduces a continuous latent action policy trained via flow matching to enable robot manipulation without relying on future frames. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 06:18	Not relevant Focuses on robotic arm manipulation policies, not industrial inspection.
Self-Supervised Learning of Semantically Consistent Visual Features via a Simple 3D Prior The paper presents a self-supervised approach using a simple 3D prior to learn semantically consistent visual features for complex objects. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 06:18	Not relevant Addresses 3D semantic correspondence, not industrial quality control.
Self-Supervised Learning of Semantically Consistent Visual Features via a Simple 3D Prior The paper presents a self-supervised approach using a simple 3D prior to learn semantically consistent visual features for complex objects. Preprint	Multimodal models via arxiv · 15 Oct, 06:18	Not relevant Investigates unimodal self-supervised visual feature correspondence using 3D priors.
MaskSense: Confronting the Visual Exploration Trap in Masked Image Generation MaskSense addresses decoding exploration biases in masked generative models when aligning them with reinforcement learning. Preprint	Multimodal models via arxiv · 15 Oct, 06:18	Not relevant Studies masked generative image models, not multimodal text-vision interaction.
MaskSense: Confronting the Visual Exploration Trap in Masked Image Generation MaskSense addresses decoding exploration biases in masked generative models when aligning them with reinforcement learning. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 06:18	Not relevant Focuses on masked image generation, not industrial defect detection.
DIME: Scaling Facial Representation Learning via Differential Masked Autoencoding DIME scales facial representation learning using a differential masked autoencoder that supervises differences between identity-matched image pairs. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 06:18	Not relevant Focuses on facial representation learning, not industrial inspection.
DIME: Scaling Facial Representation Learning via Differential Masked Autoencoding DIME scales facial representation learning using a differential masked autoencoder that supervises differences between identity-matched image pairs. Preprint	Multimodal models via arxiv · 15 Oct, 06:18	Not relevant Focuses on vision-only autoencoders for facial representation learning.
PerturbBridge: Direct Phenotypic Supervision for Cellular Perturbation Generation PerturbBridge directly supervises perturbation-specific phenotypes during generative training to model cellular responses in images. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 06:18	Not relevant Focuses on cellular microscopy phenotypes, not industrial quality control.
PerturbBridge: Direct Phenotypic Supervision for Cellular Perturbation Generation PerturbBridge directly supervises perturbation-specific phenotypes during generative training to model cellular responses in images. Preprint	Multimodal models via arxiv · 15 Oct, 06:18	Not relevant Studies biological cellular image generation, not multimodal vision-language models.
A unified three-branch deep learning framework for automated detection and counting of wildlife in aerial imagery The authors present a three-branch deep learning framework for automated aerial wildlife detection, strip delineation, and image matching. Preprint	Multimodal models via arxiv · 15 Oct, 06:18	Not relevant Focuses on unimodal aerial computer vision, not multimodal interaction.
A unified three-branch deep learning framework for automated detection and counting of wildlife in aerial imagery The authors present a three-branch deep learning framework for automated aerial wildlife detection, strip delineation, and image matching. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 06:18	Not relevant Addresses wildlife detection in aerial imagery, not manufacturing inspection.
Rethinking LLM-Guided Visual Question Answering: Does Deeper Knowledge Integration Really Help? The study compares LLM guidance integration strategies for visual question answering, showing shallow late fusion outperforms deeper attention injection. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 06:18	Not relevant Studies visual question answering, not industrial defect detection.
Scaling Unified Multimodal Models to Long-Horizon Interleaved Generation The paper introduces a visual-memory framework enabling unified multimodal models to handle long-horizon interleaved text and image generation. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 06:18	Not relevant Studies multimodal interleaved image generation, not industrial visual inspection.
Contrastive learning for video reuse detection The paper uses contrastive learning with a Siamese network to extract key visual frames and detect video reuse. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 06:18	Not relevant Covers video copyright reuse detection, not manufacturing anomaly detection.
Contrastive learning for video reuse detection The paper uses contrastive learning with a Siamese network to extract key visual frames and detect video reuse. Preprint	Multimodal models via arxiv · 15 Oct, 06:18	Not relevant Focuses strictly on video-to-video retrieval, not multimodal text-image models.
EgoProbe: Benchmarking Long-Range and Exact Memory in Streaming Egocentric Videos EgoProbe benchmarks long-range retention and event-specific recall under interference for streaming egocentric video models. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 06:18	Not relevant Evaluates streaming egocentric video memory, not industrial visual inspection.
MARS: Systematic Multi-Level Acceleration for World Action Models MARS accelerates world action model inference across transformer layers, diffusion denoising, and policy queries without retraining. Preprint	Multimodal models via arxiv · 15 Oct, 06:18	Not relevant Accelerates world action models for robotics, not multimodal understanding.
MARS: Systematic Multi-Level Acceleration for World Action Models MARS accelerates world action model inference across transformer layers, diffusion denoising, and policy queries without retraining. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 06:18	Not relevant Focuses on robotic closed-loop control, not industrial vision.
Multics: Generative Multi-Player World Model with Hierarchical Communication Multics provides a hierarchical communication framework for multi-player interactive video world models to reduce quadratic attention scaling costs. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 06:18	Not relevant Focuses on multi-player video generation, not manufacturing inspection.
Multics: Generative Multi-Player World Model with Hierarchical Communication Multics provides a hierarchical communication framework for multi-player interactive video world models to reduce quadratic attention scaling costs. Preprint	Multimodal models via arxiv · 15 Oct, 06:18	Not relevant Addresses multi-player video generation rather than cross-modal reasoning.
DexNinja: Learning Robust Dexterous Cutting Policy with a Real-to-Sim-to-Real Data Engine DexNinja introduces a real-to-sim-to-real framework to train robotic policies for dexterous cutting of deformable objects. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 06:18	Not relevant Focuses on dexterous robot cutting, not industrial defect inspection.
DexNinja: Learning Robust Dexterous Cutting Policy with a Real-to-Sim-to-Real Data Engine DexNinja introduces a real-to-sim-to-real framework to train robotic policies for dexterous cutting of deformable objects. Preprint	Multimodal models via arxiv · 15 Oct, 06:18	Not relevant Deals with robotic manipulation, not multimodal vision-language reasoning.

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