LePaX enables high-resolution chest X-ray report generation by learning to allocate resolution to diagnostically relevant regions and fusing high-res patches back into global features without increasing token count.
In: Proceedings of the acl workshop on intrinsic and extrinsic evaluation measures for machine translation and/or summa- rization
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A prompting pipeline and statement-level metrics show that six state-of-the-art text-based explainable recommendation models achieve high semantic similarity but very low factual consistency on Amazon review data.
Hi-GaTA is a hierarchical gated temporal aggregation adapter that uses short-to-long temporal pyramids and gated fusion to enable surgical video report generation, backed by a new 214-video benchmark and a surgical ViViT pretrained on 40,000 minutes of video.
CT-SpatialVQA benchmark reveals that eight 3D medical VLMs achieve only 34% average accuracy on semantic-spatial reasoning tasks from CT data, frequently below random performance.
GEST-Engine turns game engines into zero-cost dense ground-truth video generators; GTASA reveals frozen video encoders fail inter-entity spatial relation probes.
VLMs caption real objects effectively but degrade on 3D-printed fakes in robotic scenes, while some standard metrics fail to detect the factual errors from this domain shift.
citing papers explorer
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Seeing What Matters: Lesion-Aware High-Resolution Patch Discovery and Fusion for Chest X-ray Report Generation
LePaX enables high-resolution chest X-ray report generation by learning to allocate resolution to diagnostically relevant regions and fusing high-res patches back into global features without increasing token count.
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On the Factual Consistency of Text-based Explainable Recommendation Models
A prompting pipeline and statement-level metrics show that six state-of-the-art text-based explainable recommendation models achieve high semantic similarity but very low factual consistency on Amazon review data.
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Hi-GaTA: Hierarchical Gated Temporal Aggregation Adapter for Surgical Video Report Generation
Hi-GaTA is a hierarchical gated temporal aggregation adapter that uses short-to-long temporal pyramids and gated fusion to enable surgical video report generation, backed by a new 214-video benchmark and a surgical ViViT pretrained on 40,000 minutes of video.
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Lost in Volume: The CT-SpatialVQA Benchmark for Evaluating Semantic-Spatial Understanding of 3D Medical Vision-Language Models
CT-SpatialVQA benchmark reveals that eight 3D medical VLMs achieve only 34% average accuracy on semantic-spatial reasoning tasks from CT data, frequently below random performance.
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GTASA: Ground Truth Annotations for Spatiotemporal Analysis, Evaluation and Training of Video Models
GEST-Engine turns game engines into zero-cost dense ground-truth video generators; GTASA reveals frozen video encoders fail inter-entity spatial relation probes.
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Fake or Real, Can Robots Tell? Evaluating VLM Robustness to Domain Shift in Single-View Robotic Scene Understanding
VLMs caption real objects effectively but degrade on 3D-printed fakes in robotic scenes, while some standard metrics fail to detect the factual errors from this domain shift.
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