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
Multi-Modal Graph Neural Network-Inspired Vessel Identification and Tracking for UAV-Based Maritime Surveillance Combines multimodal graph learning and perspective transformation of visible and infrared feeds for drone-based vessel identification and tracking. Preprint	Multimodal models via arxiv · 12 Oct, 16:28	Not relevant Combines sensor data (infrared, AIS, visual) via GNNs rather than text-image multimodal foundation models.
Multi-Modal Graph Neural Network-Inspired Vessel Identification and Tracking for UAV-Based Maritime Surveillance Combines multimodal graph learning and perspective transformation of visible and infrared feeds for drone-based vessel identification and tracking. Preprint	Computer vision for industrial applications via arxiv · 12 Oct, 16:28	Not relevant Focuses on maritime UAV traffic surveillance rather than industrial inspection or factory operations.
Automated Detection and Encoding of Cartographic Signs in Historical Maps Presents a detection and embedding framework alongside a benchmark dataset for recognizing cartographic signs in historical archival maps. Preprint	Multimodal models via arxiv · 12 Oct, 16:28	Not relevant Focuses on object detection and metric learning for historical maps without multimodal language modeling.
Automated Detection and Encoding of Cartographic Signs in Historical Maps Presents a detection and embedding framework alongside a benchmark dataset for recognizing cartographic signs in historical archival maps. Preprint	Computer vision for industrial applications via arxiv · 12 Oct, 16:28	Not relevant Studies historical cartographic symbol detection, not industrial or manufacturing computer vision applications.
FPD-Drive: Future Policy Distillation for End-to-End Autonomous Driving Develops a distillation framework transferring privileged future trajectory knowledge and safety scoring to improve end-to-end autonomous driving planners. Preprint	Multimodal models via arxiv · 12 Oct, 16:28	Not relevant Focuses on end-to-end trajectory planning and policy distillation, not multimodal language-vision models.
FPD-Drive: Future Policy Distillation for End-to-End Autonomous Driving Develops a distillation framework transferring privileged future trajectory knowledge and safety scoring to improve end-to-end autonomous driving planners. Preprint	Computer vision for industrial applications via arxiv · 12 Oct, 16:28	Not relevant Targets autonomous vehicle motion planning rather than industrial inspection, factories, or warehouse robotics.
The Predictability Bias of Joint-Embedding Predictive Learning in Medical Imaging Audits joint-embedding predictive learning in medical CT scans, showing a bias that degrades performance on unpredictable localized pathology. Preprint	Multimodal models via arxiv · 12 Oct, 16:28	Not relevant Studies 3D CT vision encoders (JEPA) without language or general multimodal model integration.
The Predictability Bias of Joint-Embedding Predictive Learning in Medical Imaging Audits joint-embedding predictive learning in medical CT scans, showing a bias that degrades performance on unpredictable localized pathology. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:28	Not relevant Investigates medical imaging representation bias rather than AI deployment guardrails, jailbreaks, or LLM safety.
The Predictability Bias of Joint-Embedding Predictive Learning in Medical Imaging Audits joint-embedding predictive learning in medical CT scans, showing a bias that degrades performance on unpredictable localized pathology. Preprint	Computer vision for industrial applications via arxiv · 12 Oct, 16:28	Not relevant Focuses on clinical CT medical imaging analysis, not industrial inspection or manufacturing computer vision.
The Predictability Bias of Joint-Embedding Predictive Learning in Medical Imaging Audits joint-embedding predictive learning in medical CT scans, showing a bias that degrades performance on unpredictable localized pathology. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:28	Not relevant Audits representation bias in predictive learning rather than proposing an LLM evaluation benchmark.
The Predictability Bias of Joint-Embedding Predictive Learning in Medical Imaging Audits joint-embedding predictive learning in medical CT scans, showing a bias that degrades performance on unpredictable localized pathology. Preprint	Small and efficient models via arxiv · 12 Oct, 16:28	Not relevant Analyzes pretraining representation dynamics rather than model compression, quantization, or efficient serving.
Jag: Direct Box Prediction for Efficient Visual Grounding Adapts multimodal models for direct continuous bounding box prediction using a lightweight regression head in a single forward pass. Preprint	Computer vision for industrial applications via arxiv · 12 Oct, 16:28	Not relevant Presents general visual bounding box regression, not an application to industrial manufacturing or inspection.
Deep Learning Approach for RealTime Contraction Detection in Laboring Dairy Cattle Applies convolutional neural networks and vision transformers to video feeds for automated detection of labor contractions in dairy cattle. Preprint	Multimodal models via arxiv · 12 Oct, 16:28	Not relevant Uses standard CNNs and ViTs on video frames rather than multimodal reasoning or vision-language integration.
Beyond a Single Crop: Contextualized Embeddings for Player Re-Identification Introduces ContextReID, a framework utilizing co-occurring players and cross-frame sequences to improve player re-identification in broadcast sports video. Preprint	Computer vision for industrial applications via arxiv · 12 Oct, 16:28	Not relevant Addresses player re-identification in broadcast sports, not industrial inspection or manufacturing automation.
Beyond a Single Crop: Contextualized Embeddings for Player Re-Identification Introduces ContextReID, a framework utilizing co-occurring players and cross-frame sequences to improve player re-identification in broadcast sports video. Preprint	Multimodal models via arxiv · 12 Oct, 16:28	Not relevant Uses a vision-language model solely as an off-the-shelf tool for a narrow sports re-identification task.
SheafPrune: Sheaf-Guided Graph Propagation for Visual Token Pruning Proposes a graph-based token pruning method inspired by sheaf geometry to reduce prefill computation in multimodal large language models. Preprint	Computer vision for industrial applications via arxiv · 12 Oct, 16:28	Not relevant Focuses on general token pruning for MLLM prefill speedup, not industrial computer vision tasks.
CV-OPD: Contrastive Vision On-policy Distillation Introduces Contrastive Vision On-policy Distillation to prevent vision-language models from relying on answer shortcuts rather than visual evidence. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:28	Not relevant Introduces a training distillation method, not a new evaluation benchmark or LLM evaluation framework.
CV-OPD: Contrastive Vision On-policy Distillation Introduces Contrastive Vision On-policy Distillation to prevent vision-language models from relying on answer shortcuts rather than visual evidence. Preprint	Computer vision for industrial applications via arxiv · 12 Oct, 16:28	Not relevant Focuses on general vision-language distillation shortcutting, not industrial inspection, defect detection, or factory robotics.
ConClick: Element-Wise Contrastive Learning Improves GUI Grounding Proposes a contrastive learning framework to help GUI agents distinguish target interface elements from plausible distractor elements. Preprint	Computer vision for industrial applications via arxiv · 12 Oct, 16:28	Not relevant Focuses on graphical user interface grounding, not industrial vision applications.
OWLET: On-Policy Weighted Learning via Evidence Transfer for Video Large Language Model Post-Training Introduces an on-policy post-training framework that incorporates visual evidence transfer to improve reasoning in video large language models. Preprint	Computer vision for industrial applications via arxiv · 12 Oct, 16:28	Not relevant Studies post-training for Video Large Language Models, not industrial vision inspection.
QCLR: A Plug-in Robustification Module via Quantization for Contrastive Learning in Self-Supervised Skeleton-Based Human Action Recognition Proposes a quantization module to improve adversarial robustness in self-supervised skeleton-based human action recognition. Preprint	Computer vision for industrial applications via arxiv · 12 Oct, 16:28	Not relevant Focuses on action recognition and adversarial robustness, not industrial applications.
QCLR: A Plug-in Robustification Module via Quantization for Contrastive Learning in Self-Supervised Skeleton-Based Human Action Recognition Proposes a quantization module to improve adversarial robustness in self-supervised skeleton-based human action recognition. Preprint	Multimodal models via arxiv · 12 Oct, 16:28	Not relevant Addresses skeleton action recognition without multimodal language or cross-modal components.
Multi-Source and Multi-Dimensional Information Fusion in Resource-Constrained Edge Networks Develops a semantic information fusion framework that coordinates distributed edge terminal uploads and slot-based token aggregation. Preprint	Multimodal models via arxiv · 12 Oct, 16:28	Not relevant Focuses on distributed edge communication and token transmission rather than multimodal foundation models.
Multi-Source and Multi-Dimensional Information Fusion in Resource-Constrained Edge Networks Develops a semantic information fusion framework that coordinates distributed edge terminal uploads and slot-based token aggregation. Preprint	Computer vision for industrial applications via arxiv · 12 Oct, 16:28	Not relevant Deals with edge network information fusion and game theory, not industrial visual inspection.
Decoding Register Tokens Preserves Global Scene Details Investigates decoding register tokens alongside patch tokens in vision transformers to preserve global scene appearance details. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:28	Not relevant Studies feature representation recovery rather than deployment safety or jailbreaks.
Decoding Register Tokens Preserves Global Scene Details Investigates decoding register tokens alongside patch tokens in vision transformers to preserve global scene appearance details. Preprint	Small and efficient models via arxiv · 12 Oct, 16:28	Not relevant Focuses on token decoding in autoencoders, not model compression or efficiency.
Decoding Register Tokens Preserves Global Scene Details Investigates decoding register tokens alongside patch tokens in vision transformers to preserve global scene appearance details. Preprint	Multimodal models via arxiv · 12 Oct, 16:28	Not relevant Focuses exclusively on visual patch and register tokens within vision models.
Decoding Register Tokens Preserves Global Scene Details Investigates decoding register tokens alongside patch tokens in vision transformers to preserve global scene appearance details. Preprint	Computer vision for industrial applications via arxiv · 12 Oct, 16:28	Not relevant Studies vision transformer token decoding and appearance probes, not industrial vision.
Decoding Register Tokens Preserves Global Scene Details Investigates decoding register tokens alongside patch tokens in vision transformers to preserve global scene appearance details. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:28	Not relevant Investigates ViT internal token mechanics rather than LLM evaluation benchmarks.
Sonic-AMG: Streaming Human Motion Generation from Spatial Audio Introduces a streaming causal transformer framework for generating human motion continuously from spatial audio inputs. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:28	Not relevant Develops a motion generation framework rather than LLM evaluation or benchmarks.
Sonic-AMG: Streaming Human Motion Generation from Spatial Audio Introduces a streaming causal transformer framework for generating human motion continuously from spatial audio inputs. Preprint	Computer vision for industrial applications via arxiv · 12 Oct, 16:28	Not relevant Focuses on streaming human motion generation from spatial audio, not industrial vision.
Sonic-AMG: Streaming Human Motion Generation from Spatial Audio Introduces a streaming causal transformer framework for generating human motion continuously from spatial audio inputs. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:28	Not relevant Focuses on audio-to-motion generation, not AI safety, hallucination, or guardrails.
Sonic-AMG: Streaming Human Motion Generation from Spatial Audio Introduces a streaming causal transformer framework for generating human motion continuously from spatial audio inputs. Preprint	Small and efficient models via arxiv · 12 Oct, 16:28	Not relevant Addresses streaming generative modeling rather than model compression or efficient serving.
PAGE: Parts-Aware Guidance for Co-Speech Gesture Portrait Video Generation Presents parts-specific reweighting and classifier guidance to improve facial details and audio alignment in speech-driven gesture videos. Peer-reviewed	Computer vision for industrial applications via arxiv · 12 Oct, 16:28	Not relevant Focuses on generating co-speech portrait videos, not industrial computer vision.
NovaSplat: Model-Free 6D Object Pose Estimation via 3D Gaussian Splatting Onboarding and Refinement Presents a model-free pipeline using smartphone-derived 3D Gaussian Splatting for novel object onboarding and 6D pose estimation. Preprint	Multimodal models via arxiv · 12 Oct, 16:28	Not relevant Focuses on 3D computer vision and pose estimation without multimodal reasoning.
ThinkDrag: Semantic Drag-Based Image Editing with Visual Reasoning Proposes a multimodal drag-based image editing framework that generates visual reasoning traces to guide point-based transformations. Preprint	Computer vision for industrial applications via arxiv · 12 Oct, 16:28	Not relevant Addresses interactive drag-based image editing, not industrial computer vision.
CoVA: Composing VAE Representations for Generative Learning Composes internal VAE representations into stage-wise guidance to improve diffusion transformer generative learning without external encoders. Preprint	Computer vision for industrial applications via arxiv · 12 Oct, 16:28	Not relevant Focuses on VAE representations for generative image modeling, not industrial applications.
CoVA: Composing VAE Representations for Generative Learning Composes internal VAE representations into stage-wise guidance to improve diffusion transformer generative learning without external encoders. Preprint	Multimodal models via arxiv · 12 Oct, 16:28	Not relevant Investigates visual generative model representations without cross-modal multimodal integration.
VideoAWR: Reinforcing Video Flow Models with Decoupled-Timestep Advantage-Weighted Rectification Introduces a reinforcement learning method that decouples sampling from optimization anchors to stabilize video flow generation training. Preprint	Multimodal models via arxiv · 12 Oct, 16:28	Not relevant Focuses on generative video flow models rather than multimodal reasoning systems.
VideoAWR: Reinforcing Video Flow Models with Decoupled-Timestep Advantage-Weighted Rectification Introduces a reinforcement learning method that decouples sampling from optimization anchors to stabilize video flow generation training. Preprint	Computer vision for industrial applications via arxiv · 12 Oct, 16:28	Not relevant Develops reinforcement learning for video flow models, unrelated to industrial vision.
Why DiT Models Underperform as Representation Learners without Long Skip Connections Analyzes how long skip connections in Diffusion Transformers preserve feature representations for unified visual generation and understanding. Preprint	Multimodal models via arxiv · 12 Oct, 16:28	Not relevant Studies unimodal visual feature representations inside diffusion transformers.
Why DiT Models Underperform as Representation Learners without Long Skip Connections Analyzes how long skip connections in Diffusion Transformers preserve feature representations for unified visual generation and understanding. Preprint	Computer vision for industrial applications via arxiv · 12 Oct, 16:28	Not relevant Investigates representation learning in Diffusion Transformers, not industrial visual applications.
Radiomics-Based Thyroid Nodule Classification with Label-Conditional Conformal Prediction Combines radiomics features, an additive model, and conformal prediction for uncertainty-aware thyroid nodule ultrasound classification. Preprint	Multimodal models via arxiv · 12 Oct, 16:28	Not relevant Focuses on radiomics and tabular classification without multimodal model architectures.
Radiomics-Based Thyroid Nodule Classification with Label-Conditional Conformal Prediction Combines radiomics features, an additive model, and conformal prediction for uncertainty-aware thyroid nodule ultrasound classification. Preprint	Computer vision for industrial applications via arxiv · 12 Oct, 16:28	Not relevant Addresses ultrasound medical diagnostics and conformal prediction, not industrial vision.
TextVBench: Evaluating Text Rendering and Preservation in Video Generation Introduces a benchmark evaluating text rendering and temporal preservation in text-to-video and reference-to-video generation models. Preprint	Computer vision for industrial applications via arxiv · 12 Oct, 16:28	Not relevant Evaluates visual text rendering in generative video models, not industrial inspection.
SADL: A Size-Adaptive Decision Layer for Chronic Stroke Lesion Segmentation in ISLES 2026 Introduces an adaptive decision layer and ensemble scheme for chronic stroke lesion segmentation on brain MRI. Preprint	Computer vision for industrial applications via arxiv · 12 Oct, 16:28	Not relevant Focuses on clinical medical MRI lesion segmentation, not industrial computer vision.
SADL: A Size-Adaptive Decision Layer for Chronic Stroke Lesion Segmentation in ISLES 2026 Introduces an adaptive decision layer and ensemble scheme for chronic stroke lesion segmentation on brain MRI. Preprint	Multimodal models via arxiv · 12 Oct, 16:28	Not relevant Focuses strictly on medical image segmentation without multimodal language-vision capabilities.
A Real-World CAD Segmentation Benchmark: Comparing Rule-Based and Learning-Based Approaches through Hole Detection Introduces a real-world CAD segmentation benchmark focused on fastener hole detection, comparing rule-based approaches with deep learning methods. Preprint	Multimodal models via arxiv · 12 Oct, 16:28	Not relevant Evaluates 3D CAD mesh/B-Rep segmentation without multimodal or vision-language integration.
GraftOOD: Grafting Out-of-Distribution Exemplars into Advantage-Weighted Rectification for Video Reward Optimization Grafts out-of-distribution video exemplars into reward optimization to prevent motion collapse during video generation training. Preprint	Computer vision for industrial applications via arxiv · 12 Oct, 16:28	Not relevant Studies video generative models and reward optimization, not industrial computer vision.
GraftOOD: Grafting Out-of-Distribution Exemplars into Advantage-Weighted Rectification for Video Reward Optimization Grafts out-of-distribution video exemplars into reward optimization to prevent motion collapse during video generation training. Preprint	Multimodal models via arxiv · 12 Oct, 16:28	Not relevant Focuses on video generation dynamics and reward optimization rather than multimodal reasoning.
Stride: Scale-Aware Multi-Agent Evolutionary Refinement for Infographic Generation Proposes a multi-agent evolutionary framework that routes global composition edits and local retouching for constrained infographic generation. Preprint	Computer vision for industrial applications via arxiv · 12 Oct, 16:28	Not relevant Focuses on infographic image generation, not industrial computer vision tasks.
SplatQ: Interaction-Aware Pruning of 3D Gaussian Splatting via Quadratic Selection Presents a quadratic selection pruning algorithm for 3D Gaussian Splatting that coordinates regional budgets and accounts for joint deletion effects. Preprint	Multimodal models via arxiv · 12 Oct, 16:28	Not relevant Focuses purely on 3D scene reconstruction and graphics pruning without cross-modal interaction.
SplatQ: Interaction-Aware Pruning of 3D Gaussian Splatting via Quadratic Selection Presents a quadratic selection pruning algorithm for 3D Gaussian Splatting that coordinates regional budgets and accounts for joint deletion effects. Preprint	Computer vision for industrial applications via arxiv · 12 Oct, 16:28	Not relevant Proposes 3D Gaussian Splatting pruning for scene rendering, not industrial inspection or defect detection.
Beyond Successful Robot Demonstrations: Learning from Human Interactions and Modeling Failure Dynamics Develops a robotic action-conditioned world model trained on egocentric human interactions and failure dynamics to improve execution foresight. Preprint	Multimodal models via arxiv · 12 Oct, 16:28	Not relevant Focuses on robotic action-conditioned world models rather than multimodal reasoning or vision-language models.
Beyond Successful Robot Demonstrations: Learning from Human Interactions and Modeling Failure Dynamics Develops a robotic action-conditioned world model trained on egocentric human interactions and failure dynamics to improve execution foresight. Preprint	Computer vision for industrial applications via arxiv · 12 Oct, 16:28	Not relevant Focuses on general robot world modeling from human videos, not computer vision for industrial applications.
Knowing Whether, Not Just Where: Anchor-Probing Distillation for Open-Set Remote Sensing Visual Grounding Presents a teacher-student framework to evaluate whether remote sensing visual queries are genuinely supported before predicting bounding boxes. Preprint	Computer vision for industrial applications via arxiv · 12 Oct, 16:28	Not relevant Focuses on remote sensing visual grounding, not industrial manufacturing or visual inspection.
Efficient Confidence-Aware Densification for 3DGS Introduces an error-driven, confidence-aware densification method to place primitives more effectively in 3D Gaussian Splatting under fixed budgets. Preprint	Multimodal models via arxiv · 12 Oct, 16:28	Not relevant Deals strictly with 3D novel view synthesis rather than multimodal vision-language architectures.
Efficient Confidence-Aware Densification for 3DGS Introduces an error-driven, confidence-aware densification method to place primitives more effectively in 3D Gaussian Splatting under fixed budgets. Preprint	Computer vision for industrial applications via arxiv · 12 Oct, 16:28	Not relevant Focuses on general 3D Gaussian Splatting rendering, not industrial inspection or defect detection.
MOSAIC: Multi-Scale Object-Centric Slot Adaptation with Image-Guided Prototype Conditioning for Remote Sensing Image Super-Resolution Proposes an object-centric slot adaptation framework with prototype memory for remote sensing image super-resolution using diffusion Transformers. Preprint	Multimodal models via arxiv · 12 Oct, 16:28	Not relevant Deals with image-to-image super-resolution via diffusion Transformers without multimodal integration.
MOSAIC: Multi-Scale Object-Centric Slot Adaptation with Image-Guided Prototype Conditioning for Remote Sensing Image Super-Resolution Proposes an object-centric slot adaptation framework with prototype memory for remote sensing image super-resolution using diffusion Transformers. Preprint	Computer vision for industrial applications via arxiv · 12 Oct, 16:28	Not relevant Addresses remote sensing super-resolution, not industrial inspection, defect detection, or factory perception.

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