CT-SpatialVQA benchmark shows 3D medical VLMs achieve only 34% average accuracy on semantic-spatial reasoning tasks in CT volumes, often below random chance.
In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics
7 Pith papers cite this work. Polarity classification is still indexing.
7
Pith papers citing it
citation-role summary
background 1
citation-polarity summary
verdicts
UNVERDICTED 7roles
background 1polarities
background 1representative citing papers
GTASA supplies annotated multi-actor videos with exact 3D spatial and temporal ground truth that outperforms neural video generators in physical and semantic validity while enabling new probes of video encoders.
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.
IMU-to-4D uses wearable IMU data and repurposed LLMs to predict coherent 4D human motion plus coarse scene structure, outperforming cascaded state-of-the-art pipelines in temporal stability.
Proposes MedFM-Robust benchmark to evaluate robustness of medical vision-language and segmentation foundation models for clinical reliability.
Curr-RLCER applies curriculum reinforcement learning with coherence-driven rewards to align generated explanations with predicted ratings in explainable recommendation systems.
citing papers explorer
-
Lost in Volume: The CT-SpatialVQA Benchmark for Evaluating Semantic-Spatial Understanding of 3D Medical Vision-Language Models
CT-SpatialVQA benchmark shows 3D medical VLMs achieve only 34% average accuracy on semantic-spatial reasoning tasks in CT volumes, often below random chance.
-
GTASA: Ground Truth Annotations for Spatiotemporal Analysis, Evaluation and Training of Video Models
GTASA supplies annotated multi-actor videos with exact 3D spatial and temporal ground truth that outperforms neural video generators in physical and semantic validity while enabling new probes of video encoders.
-
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.
-
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.
-
Seeing Without Eyes: 4D Human-Scene Understanding from Wearable IMUs
IMU-to-4D uses wearable IMU data and repurposed LLMs to predict coherent 4D human motion plus coarse scene structure, outperforming cascaded state-of-the-art pipelines in temporal stability.
-
MedFM-Robust: Benchmarking Robustness of Medical Foundation Models
Proposes MedFM-Robust benchmark to evaluate robustness of medical vision-language and segmentation foundation models for clinical reliability.
-
Curr-RLCER:Curriculum Reinforcement Learning For Coherence Explainable Recommendation
Curr-RLCER applies curriculum reinforcement learning with coherence-driven rewards to align generated explanations with predicted ratings in explainable recommendation systems.