Uppdateras dagligen1519 verifierade berättelser
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A new arXiv preprint proposes ProViP, a training-free method that progressively removes redundant visual tokens and focuses pruning on the attention heads most useful for selecting critical visual information. In one reported LLaVA-1.5-7B experiment, it retained 95.9% of the original performance while delivering a…
An audit of 15 frozen hematology, pathology and general-vision foundation models reports steep drops in cross-dataset accuracy and confidence calibration when white-blood-cell images come from different acquisition conditions.
A new preprint introduces LiDAR-SAM2, which transfers video segmentation from SAM2 to temporally consistent 4D LiDAR labeling using multi-view projection and spatio-temporal aggregation.
A new preprint describes SHIFT-LLM, a post-pruning correction method that uses lightweight linear adapters to approximate the computations removed from large language models. The authors report accuracy gains of up to 15.7 points on Llama-3.1-8B-Instruct across seven zero-shot benchmarks.
A new paper introduces YOLOEZ, an open-source graphical tool that combines image labeling, YOLO model training and defect inference in one no-code workflow for structural inspection.
A new arXiv preprint reports a compact monocular-vision pipeline that identifies sidewalk paths and plans routes on CPU hardware. In the authors’ tests, its SegFormer-B0 model reached a hand-annotated intersection-over-union score of 0.946 at 11.7 milliseconds per frame, while image-space midpoint planning produced…
An arXiv paper introduces PointRL, a reinforcement-learning method that uses hidden annotation evidence to train vision-language models to point more reliably at targets.
A new preprint introduces VGA-BenchV2, a benchmark built from 1,016 prompts, more than 60,000 videos and 36,000 human annotations across 12 video-generation models. Its evaluators are also designed to guide reinforcement-learning fine-tuning.
A new arXiv study finds that AI weld-seam segmentation is highly sensitive to image-acquisition conditions. Polarimetric imaging and transformer architectures performed more reliably than conventional CNNs when welds appeared from previously unseen viewpoints.
A revised arXiv paper proposes a learning framework for deciding which LLM APIs to query and which generated answer to deploy when API costs vary and only the selected answer’s downstream reward is observed.
An arXiv study combines two vision models with open-weight multimodal language models to resolve conflicting evidence in plant-disease images. The approach improved PlantDoc accuracy from 63.9% to 68.5% with Gemma, while tests on two Cornell robot-acquired datasets reached 98.9% and 99.3% accuracy. The researchers…
A revised arXiv paper proposes TSDS, a framework that stops local reasoning when an action stabilizes and sends uncertain actions to a cloud model. The authors report 43%-65% lower per-episode thinking compute on three of four tested tasks while maintaining stated guarantees on expected reward and cloud-call rate.
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