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9 nyayaInnovation
Radiology Vision-Language Models Show Hidden Failures Under Data Shifts, Preprint Finds
A new preprint reports that medical vision-language models can appear reliable on familiar data while failing cross-dataset transfer, multimodal alignment and shortcut tests.arxiv.orgInnovation
ProViP uses head-aware pruning to cut visual tokens in vision-language models
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…arxiv.orgInnovation
Frozen Hematology AI Models Inorasikirwa Nechokwadi uye Calibration Pasi peKuwana Shift, Dzidzo Inowanikwa.
Ongororo yegumi neshanu hematology yechando, pathology uye general-vision foundation modhi inoshuma kudonha kwakadzika mukuyambuka-dataset kunyatsoita uye kusavimbika kuenzanisa kana chena-yeropa-sero mifananidzo ichibva mumamiriro akasiyana ekutora.arxiv.orgInnovation
LiDAR-SAM2 uses a video foundation model to create 4D LiDAR labels without human annotation
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.arxiv.orgInnovation
SHIFT-LLM reports a training-free way to recover accuracy after pruning LLM layers
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.arxiv.orgInnovation
YOLOEZ offers a no-code workflow for AI-based structural-defect inspection
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.arxiv.orgInnovation
Iyo yakareruka AI yekuona sisitimu inobvisa nzira dzepadivi pezvishoma-simba micromobility zvishandiso
Iyo nyowani arXiv preprint inoshuma compact monocular-yekuona pombi inoratidza nzira dzepadivi uye zvirongwa nzira paCPU Hardware. Mumiyedzo yevanyori, yayo SegFormer-B0 modhi yakasvika paruoko-inotsanangurwa mharadzano-pamusoro-mubatanidzwa mamakisi e0.946 pa11.7 milliseconds pafuremu, ukuwo mufananidzo-nzvimbo yepakati kuronga kwakagadzirwa…arxiv.orgInnovation
PointRL inoshuma yakagadziridzwa nzvimbo-yepamusoro-pasi yemhando yemutauro wechiratidzo
Bepa rearXiv rinosuma PointRL, nzira yekusimbisa-yekudzidza inoshandisa humbowo hwakavanzika kudzidzisa mamodheru emutauro wechiratidzo kunongedza zvakavimbika pane zvinangwa.arxiv.orgInnovation
VGA-BenchV2 inowedzera kuongororwa kweAI-yakagadzirwa vhidhiyo mhando uye aesthetics
Imwe preprint itsva inosuma VGA-BenchV2, bhenji rakavakwa kubva ku1,016 kukurumidza, mavhidhiyo anopfuura zviuru makumi matanhatu uye 36,000 zvirevo zvevanhu mumhando gumi nemaviri echizvarwa. Vaongorori vayo vakagadzirirwawo kutungamira kusimbisa-kudzidza zvakanaka-tuning.arxiv.org
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