FAROS uses flow-guided propagation from zero-shot masks and optical flow to create dense temporally consistent labels from sparse keyframes, improving joint multi-task learning across temporal and spatial surgical tasks on GraSP, MISAW, and AutoLaparo.
arXiv preprint arXiv:2408.07931 (2024)
4 Pith papers cite this work. Polarity classification is still indexing.
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An RL framework uses digital twin representations with hierarchical uncertainty estimates and a novel clinical plausibility reward to train LLMs for surgical VideoQA, achieving SOTA on a new 2000-pair benchmark and two existing datasets.
EndoGSim integrates MLLM-guided material initialization with 4D Gaussian Splatting and differentiable Material Point Method to achieve physics-aware 4D reconstruction and simulation of endoscopic scenes.
PanoSAM2 adapts SAM2 with a Pano-Aware Decoder, Distortion-Guided Mask Loss, and Long-Short Memory Module to improve 360 video object segmentation, reporting +5.6 and +6.7 gains over base SAM2 on two benchmarks.
citing papers explorer
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Temporally Consistent Label Interpolation for Robust Surgical Multi-Task Learning under Challenging Conditions
FAROS uses flow-guided propagation from zero-shot masks and optical flow to create dense temporally consistent labels from sparse keyframes, improving joint multi-task learning across temporal and spatial surgical tasks on GraSP, MISAW, and AutoLaparo.
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Training LLMs with Reinforcement Learning over Digital Twin Representations for Reasoning-Intensive Surgical VideoQA
An RL framework uses digital twin representations with hierarchical uncertainty estimates and a novel clinical plausibility reward to train LLMs for surgical VideoQA, achieving SOTA on a new 2000-pair benchmark and two existing datasets.
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EndoGSim: Physics-Aware 4D Dynamic Endoscopic Scene Simulations via MLLM-Guided Gaussian Splatting
EndoGSim integrates MLLM-guided material initialization with 4D Gaussian Splatting and differentiable Material Point Method to achieve physics-aware 4D reconstruction and simulation of endoscopic scenes.
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PanoSAM2: Lightweight Distortion- and Memory-aware Adaptions of SAM2 for 360 Video Object Segmentation
PanoSAM2 adapts SAM2 with a Pano-Aware Decoder, Distortion-Guided Mask Loss, and Long-Short Memory Module to improve 360 video object segmentation, reporting +5.6 and +6.7 gains over base SAM2 on two benchmarks.