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
Squeeze-Aware-YOLO: Early Channel Recalibration and Multi-Scale Fusion for Real-Time Traffic Sign Detection This paper introduces a lightweight detection framework combining early channel recalibration and multiscale fusion for real-time traffic sign identification. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 06:18	Not relevant Focuses on traffic sign detection for autonomous driving, explicitly excluded.
Squeeze-Aware-YOLO: Early Channel Recalibration and Multi-Scale Fusion for Real-Time Traffic Sign Detection This paper introduces a lightweight detection framework combining early channel recalibration and multiscale fusion for real-time traffic sign identification. Preprint	Multimodal models via arxiv · 15 Oct, 06:18	Not relevant Focuses on standard object detection architectures (YOLO) for vision alone.
Lesion-Aware Attention for Mammography Segmentation with Heterogeneous Supervision This paper introduces a dual-branch framework combining mask-level and image-level supervision for open-vocabulary mammography lesion segmentation. Preprint	Multimodal models via arxiv · 15 Oct, 06:18	Not relevant Addresses medical image segmentation under mixed supervision, not multimodal cross-modal modeling.
Lesion-Aware Attention for Mammography Segmentation with Heterogeneous Supervision This paper introduces a dual-branch framework combining mask-level and image-level supervision for open-vocabulary mammography lesion segmentation. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 06:18	Not relevant Focuses on medical mammography segmentation, not industrial quality control.
1Step-WAM: Your World Action Model but Faster using 1-Step Action Generation This paper accelerates world action models by compressing multi-step action denoising into a single forward step parallelized with visual prefilling. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 06:18	Not relevant Focuses on robotic action generation, not industrial visual inspection.
1Step-WAM: Your World Action Model but Faster using 1-Step Action Generation This paper accelerates world action models by compressing multi-step action denoising into a single forward step parallelized with visual prefilling. Preprint	Multimodal models via arxiv · 15 Oct, 06:18	Not relevant Focuses on single-step action denoising in world action models for robotics.
SurgCast: Action-Conditioned Future Skeletons for Controllable Surgical World Models This paper proposes predicting future instrument skeletons from planned actions to guide visually and structurally consistent surgical video generation. Preprint	Multimodal models via arxiv · 15 Oct, 06:18	Not relevant Focuses on robot action-conditioned surgical video generation rather than multimodal reasoning.
SurgCast: Action-Conditioned Future Skeletons for Controllable Surgical World Models This paper proposes predicting future instrument skeletons from planned actions to guide visually and structurally consistent surgical video generation. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 06:18	Not relevant Applies to surgical video generation and robotics, not manufacturing inspection.
Sparse All-Layer Connector for Domain Generalised Semantic Segmentation This paper introduces a sparse connector allowing cross-layer feature sharing across different foundation models for domain-generalized semantic segmentation. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 06:18	Not relevant Studies domain generalized semantic segmentation, not industrial defect inspection.
ProtoPur: Prototype-Purity Pseudo-Labeling for Weakly Supervised Instance Learning in Pathology This paper introduces prototype-purity pseudo-labeling to extract accurate instance-level annotations from weakly supervised whole-slide pathology images. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 06:18	Not relevant Focuses on digital pathology whole-slide images, not industrial inspection.
ProtoPur: Prototype-Purity Pseudo-Labeling for Weakly Supervised Instance Learning in Pathology This paper introduces prototype-purity pseudo-labeling to extract accurate instance-level annotations from weakly supervised whole-slide pathology images. Preprint	Multimodal models via arxiv · 15 Oct, 06:18	Not relevant Focuses on multiple instance learning in pathology images, not multimodal models.
ProtoPur: Prototype-Purity Pseudo-Labeling for Weakly Supervised Instance Learning in Pathology This paper introduces prototype-purity pseudo-labeling to extract accurate instance-level annotations from weakly supervised whole-slide pathology images. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Proposes a pathology pseudo-labeling method, not LLM evaluation or benchmarks.
ProtoPur: Prototype-Purity Pseudo-Labeling for Weakly Supervised Instance Learning in Pathology This paper introduces prototype-purity pseudo-labeling to extract accurate instance-level annotations from weakly supervised whole-slide pathology images. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Studies weakly supervised pathology learning, not AI safety or deployment guardrails.
ProtoPur: Prototype-Purity Pseudo-Labeling for Weakly Supervised Instance Learning in Pathology This paper introduces prototype-purity pseudo-labeling to extract accurate instance-level annotations from weakly supervised whole-slide pathology images. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Does not address small models, pruning, quantization, or efficient serving.
Recovering Low-Bit Failures in Vision-Language-Action Policies via Quantization-Conditioned Reinforcement Learning This paper proposes offline reinforcement learning conditioned on quantization levels to recover task completion performance in quantized vision-language-action policies. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 06:18	Not relevant Focuses on robotic vision-language-action policies, not industrial defect detection.
PhySimCode: A Benchmark and Evaluation Method for Physics Video to Code Generation This paper introduces a benchmark evaluating whether vision-language models can infer underlying physical laws from motion videos and generate runnable simulation code. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 06:18	Not relevant Focuses on physics video-to-code generation, not manufacturing defect detection.
Visually Legible but Adversarial: Watermark-Constrained Attacks on End-to-End VLMs for Document Understanding This paper introduces a question-agnostic adversarial text-watermarking method that severely degrades document understanding in vision-language models. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 06:18	Not relevant Evaluates document understanding attacks, not industrial manufacturing inspection.
ScenePlacer: Reformulating Scene-Level Object Pose Estimation as Visibility Field Learning This paper proposes estimating 3D object poses within partial scenes by predicting visibility fields in canonical object space. Preprint	Multimodal models via arxiv · 15 Oct, 06:18	Not relevant Addresses 3D shape alignment and point clouds, not cross-modal vision-language reasoning.
ScenePlacer: Reformulating Scene-Level Object Pose Estimation as Visibility Field Learning This paper proposes estimating 3D object poses within partial scenes by predicting visibility fields in canonical object space. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 06:18	Not relevant Focuses on 3D object pose estimation for generative graphics scenes.
RSAJEV: Training-Free Abstaining Prototype Memory for Eliminating Silent Hallucination in JEV-Based Remote Sensing Classification This paper introduces a training-free prototype memory readout to prevent out-of-vocabulary overconfidence and silent hallucinations in remote sensing classification. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 06:18	Not relevant Focuses on remote sensing scene classification, not industrial quality inspection.
SpikeOcc: Towards Resilient Multimodal 3D Occupancy Perception via Spiking Experts This paper presents a spiking mixture-of-experts architecture to improve the resilience of multimodal 3D occupancy perception against missing sensor inputs. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 06:18	Not relevant Addresses autonomous driving 3D occupancy perception, explicitly out of scope.
Frozen History: When Temporal Losses Stop Accumulation in Learned Real-Time Supersampling This paper demonstrates that accurate registration can cause temporal consistency losses to freeze history accumulation and degrade real-time supersampling accuracy. Preprint	Multimodal models via arxiv · 15 Oct, 06:18	Not relevant Focuses on video supersampling and temporal losses, not multimodal reasoning.
Frozen History: When Temporal Losses Stop Accumulation in Learned Real-Time Supersampling This paper demonstrates that accurate registration can cause temporal consistency losses to freeze history accumulation and degrade real-time supersampling accuracy. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 06:18	Not relevant Investigates real-time supersampling losses, not industrial visual inspection.
SlotAVS: Object-Centric Audio-Visual Segmentation via Bi-Modal Slot Attention This paper introduces a competitive bi-modal slot attention method to cleanly segment distinct sounding objects in multi-source audio-visual scenes. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 06:18	Not relevant Focuses on audio-visual segmentation, not industrial inspection or defect detection.
Hand2Robot: Object-Centric 4D Hand Reconstruction from Human Demonstrations for Robotics This paper presents a framework to reconstruct 4D hand-object interactions from human demonstration videos to supervise robot manipulation policies. Preprint	Multimodal models via arxiv · 15 Oct, 06:18	Not relevant Focuses on 3D/4D pose reconstruction and robotics retargeting rather than multimodal foundation models.
Hand2Robot: Object-Centric 4D Hand Reconstruction from Human Demonstrations for Robotics This paper presents a framework to reconstruct 4D hand-object interactions from human demonstration videos to supervise robot manipulation policies. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 06:18	Not relevant Studies 4D hand reconstruction for robotics, not industrial manufacturing inspection.
Data-Centric Recursive Self-Improvement for Navigation via Policy-Synchronized Language-Action Dual Bootstrapping This paper introduces a recursive self-improvement method that unifies instruction-following and instruction generation within a shared model for vision-language navigation. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 06:18	Not relevant Focuses on vision-language navigation, not industrial inspection or defect detection.
Reference-Efficient Multimodal Bitstream-Corrupted Video Recovery via Depth-Conditioned Adaptation This paper presents a depth-conditioned video recovery framework that selects a single reliable RGB reference to restore damaged video bitstreams efficiently. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 06:18	Not relevant Studies corrupted video bitstream restoration, not manufacturing visual inspection.
Totem: Object-Centric 3D Memory for World-Consistent Video Generation This paper proposes an object-centric 3D memory framework to maintain persistent object representations and world consistency during video generation. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 06:18	Not relevant Focuses on video generation under camera trajectories, not industrial inspection.
Totem: Object-Centric 3D Memory for World-Consistent Video Generation This paper proposes an object-centric 3D memory framework to maintain persistent object representations and world consistency during video generation. Preprint	Multimodal models via arxiv · 15 Oct, 06:18	Not relevant Proposes 3D object-centric memory for video synthesis, not multimodal models.
ChangeT$^3$: Evaluating Vision Test-Time Training for Remote Sensing Change Detection This paper evaluates visual test-time training for remote sensing change detection using a Siamese encoder-decoder architecture with adaptive fast weights. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 06:18	Not relevant Addresses satellite remote sensing change detection, not industrial production line vision.
ChangeT$^3$: Evaluating Vision Test-Time Training for Remote Sensing Change Detection This paper evaluates visual test-time training for remote sensing change detection using a Siamese encoder-decoder architecture with adaptive fast weights. Preprint	Multimodal models via arxiv · 15 Oct, 06:18	Not relevant Deals with bitemporal satellite image change detection without multimodal models.
ML-CLAPPS: A Probabilistic Framework for Imbalanced Multilabel Learning under Class Prioritization This paper introduces a learnable probabilistic instance sampler to handle severe class imbalance and prioritize critical diagnoses in multilabel learning. Preprint	Multimodal models via arxiv · 15 Oct, 06:18	Not relevant Targets multilabel ECG image classification without multimodal reasoning.
ML-CLAPPS: A Probabilistic Framework for Imbalanced Multilabel Learning under Class Prioritization This paper introduces a learnable probabilistic instance sampler to handle severe class imbalance and prioritize critical diagnoses in multilabel learning. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Addresses training sampling under class imbalance, not model compression or efficiency.
ML-CLAPPS: A Probabilistic Framework for Imbalanced Multilabel Learning under Class Prioritization This paper introduces a learnable probabilistic instance sampler to handle severe class imbalance and prioritize critical diagnoses in multilabel learning. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 06:18	Not relevant Evaluates ECG signal and image classification, not industrial defect inspection.
ML-CLAPPS: A Probabilistic Framework for Imbalanced Multilabel Learning under Class Prioritization This paper introduces a learnable probabilistic instance sampler to handle severe class imbalance and prioritize critical diagnoses in multilabel learning. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Proposes an imbalanced training sampler rather than an evaluation benchmark.
Mobility Helps Learning: Unsupervised Model Adaptation for Object Recognition via Movement This paper presents an unsupervised test-time adaptation method that uses movement-induced prediction variance to adapt object recognition models in mobile agents. Peer-reviewed	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Introduces an unsupervised test-time adaptation technique, not an evaluation framework.
Mobility Helps Learning: Unsupervised Model Adaptation for Object Recognition via Movement This paper presents an unsupervised test-time adaptation method that uses movement-induced prediction variance to adapt object recognition models in mobile agents. Peer-reviewed	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Focuses on test-time adaptation accuracy rather than model compression or efficiency.
Mobility Helps Learning: Unsupervised Model Adaptation for Object Recognition via Movement This paper presents an unsupervised test-time adaptation method that uses movement-induced prediction variance to adapt object recognition models in mobile agents. Peer-reviewed	Computer vision for industrial applications via arxiv · 15 Oct, 06:18	Not relevant Targets mobile robotics and autonomous vehicles, not manufacturing inspection.
Mobility Helps Learning: Unsupervised Model Adaptation for Object Recognition via Movement This paper presents an unsupervised test-time adaptation method that uses movement-induced prediction variance to adapt object recognition models in mobile agents. Peer-reviewed	Multimodal models via arxiv · 15 Oct, 06:18	Not relevant Focuses purely on vision test-time adaptation without multimodal elements.
Sharper Frames, Worse Rooms: Viewpoint Coverage Dominates Image Quality in Feed-Forward 3D Reconstruction This paper shows that wide viewpoint coverage produces significantly better 3D room reconstructions than selecting only sharp, motion-free camera frames. Preprint	Multimodal models via arxiv · 15 Oct, 06:18	Not relevant Studies geometric frame selection for 3D reconstruction without multimodal models.
Sharper Frames, Worse Rooms: Viewpoint Coverage Dominates Image Quality in Feed-Forward 3D Reconstruction This paper shows that wide viewpoint coverage produces significantly better 3D room reconstructions than selecting only sharp, motion-free camera frames. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 06:18	Not relevant Analyzes handheld indoor room 3D reconstruction, not industrial visual inspection.
VIEWCRAFT: Evolving What Multimodal Agents See and How They Read It This paper proposes a framework that jointly evolves visual evidence presentation and reading procedures to improve multimodal agent task performance. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 06:18	Not relevant Studies general multimodal agent skills, not industrial defect detection.
Frequency-Step Cache: Separating Synchronization and Repair for Efficient Autoregressive Video Diffusion This paper introduces a training-free caching method that separates cache synchronization and computation repair to accelerate autoregressive video diffusion generation. Preprint	Multimodal models via arxiv · 15 Oct, 06:18	Not relevant Concerns video generation diffusion caching rather than multimodal text-image-video understanding.
Frequency-Step Cache: Separating Synchronization and Repair for Efficient Autoregressive Video Diffusion This paper introduces a training-free caching method that separates cache synchronization and computation repair to accelerate autoregressive video diffusion generation. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 06:18	Not relevant Focuses on generative video diffusion backbones, not industrial visual inspection.
Frequency-Step Cache: Separating Synchronization and Repair for Efficient Autoregressive Video Diffusion This paper introduces a training-free caching method that separates cache synchronization and computation repair to accelerate autoregressive video diffusion generation. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Introduces a caching algorithm rather than an evaluation benchmark.
Frequency-Step Cache: Separating Synchronization and Repair for Efficient Autoregressive Video Diffusion This paper introduces a training-free caching method that separates cache synchronization and computation repair to accelerate autoregressive video diffusion generation. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Focuses on caching performance rather than safety or reliability guardrails.
When Trackers Fail: VLM-Guided Verification and Recovery for Robust Video Object Segmentation This paper presents a verification and recovery framework using vision-language models to correct tracking errors and mask drift in video object segmentation. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 06:18	Not relevant Addresses general video object segmentation, not industrial quality or defect inspection.
Representational Difference Distillation Proposes a knowledge distillation method that focuses learning signals on specific structural disagreements between teacher and student representations. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 06:18	Not relevant Focuses on general representation distillation on CIFAR, not industrial vision.
Representational Difference Distillation Proposes a knowledge distillation method that focuses learning signals on specific structural disagreements between teacher and student representations. Preprint	Multimodal models via arxiv · 15 Oct, 06:18	Not relevant Applies knowledge distillation to vision classification without multimodal interaction.
Representational Difference Distillation Proposes a knowledge distillation method that focuses learning signals on specific structural disagreements between teacher and student representations. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Proposes a distillation loss formulation rather than benchmarking methodologies.
Representational Difference Distillation Proposes a knowledge distillation method that focuses learning signals on specific structural disagreements between teacher and student representations. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Does not address model safety, alignment, or deployment guardrails.
Meaningful Moments: A Corpus of VLM-Derived Temporal Importance Labels for Video Action Recognition Introduces a corpus of vision-language model generated importance scores to improve temporal sampling in video action recognition. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 06:18	Not relevant Studies human action recognition datasets, not industrial defect or quality control.
Removing the Compute Bottleneck for Event-based Visual Place Recognition with Spiking Neural Networks Implements an aggressively pruned spiking neural network to perform event-based visual place recognition on commodity CPUs in microseconds. Preprint	Multimodal models via arxiv · 15 Oct, 06:18	Not relevant Focuses on single-modality event-based sensing rather than multimodal models.
Removing the Compute Bottleneck for Event-based Visual Place Recognition with Spiking Neural Networks Implements an aggressively pruned spiking neural network to perform event-based visual place recognition on commodity CPUs in microseconds. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 06:18	Not relevant Addresses event-based visual place recognition for mobile robotics, not industrial manufacturing.
Hybrid Context Forests and Bilateral Viterbi Matching for 3D Rib Centerline Tracing in CT Presents an open-source framework using context forests and Viterbi dynamic programming to extract and number rib centerlines in CT scans. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 06:18	Not relevant Addresses medical CT rib tracing, not industrial visual inspection.
Hybrid Context Forests and Bilateral Viterbi Matching for 3D Rib Centerline Tracing in CT Presents an open-source framework using context forests and Viterbi dynamic programming to extract and number rib centerlines in CT scans. Preprint	Multimodal models via arxiv · 15 Oct, 06:18	Not relevant Exclusively targets 3D medical CT image processing without multimodal reasoning.
From Failure Prediction to Component Selection in Semantic Segmentation Analyzes failure prediction and component selection methods to evaluate which semantic segmentation predictions to retain or inspect. Preprint	Multimodal models via arxiv · 15 Oct, 06:18	Not relevant Studies failure prediction in 2D image semantic segmentation, not multimodal models.
From Failure Prediction to Component Selection in Semantic Segmentation Analyzes failure prediction and component selection methods to evaluate which semantic segmentation predictions to retain or inspect. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 06:18	Not relevant Focuses on autonomous driving scene segmentation, not industrial defect detection.
PIGEONS: Incremental Long-Video Captioning with Persistent Identity Gallery and Evolving Overarching Narrative Summary Develops an incremental long-video captioning method using a persistent identity gallery and evolving narrative summary. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 06:18	Not relevant Addresses general long-video captioning rather than industrial visual inspection.

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