245 citations · 783 across the 82 of their papers we have counts for
8 papers · 1 filter
Generating Natural-Language Surgical Feedback: From Structured Representation to Domain-Grounded Evaluation
Firdavs Nasriddinov, Rafal Kocielnik, Anima Anandkumar +1
High-quality intraoperative feedback from a surgical trainer is pivotal for improving trainee performance and long-term skill acquisition. Automating natural, trainer-style feedbac…
Robust Representation Consistency Model via Contrastive Denoising
Jiachen Lei, Julius Berner, Jiongxiao Wang +5
Robustness is essential for deep neural networks, especially in security-sensitive applications. To this end, randomized smoothing provides theoretical guarantees for certifying ro…
ARDuP: Active Region Video Diffusion for Universal Policies
Shuaiyi Huang, Mara Levy, Zhenyu Jiang +5
Sequential decision-making can be formulated as a text-conditioned video generation problem, where a video planner, guided by a text-defined goal, generates future frames visualizi…
What is Point Supervision Worth in Video Instance Segmentation?
Shuaiyi Huang, De-An Huang, Zhiding Yu +5
Video instance segmentation (VIS) is a challenging vision task that aims to detect, segment, and track objects in videos. Conventional VIS methods rely on densely-annotated object…
Efficient Video Diffusion Models via Content-Frame Motion-Latent Decomposition
Sihyun Yu, Weili Nie, De-An Huang +3
Video diffusion models have recently made great progress in generation quality, but are still limited by the high memory and computational requirements. This is because current vid…
Improving Distant 3D Object Detection Using 2D Box Supervision
Zetong Yang, Zhiding Yu, Chris Choy +3
Improving the detection of distant 3d objects is an important yet challenging task. For camera-based 3D perception, the annotation of 3d bounding relies heavily on LiDAR for accura…
T-Stitch: Accelerating Sampling in Pre-Trained Diffusion Models with Trajectory Stitching
Zizheng Pan, Bohan Zhuang, De-An Huang +5
Sampling from diffusion probabilistic models (DPMs) is often expensive for high-quality image generation and typically requires many steps with a large model. In this paper, we int…
Fully Attentional Networks with Self-emerging Token Labeling
Bingyin Zhao, Zhiding Yu, Shiyi Lan +4
Recent studies indicate that Vision Transformers (ViTs) are robust against out-of-distribution scenarios. In particular, the Fully Attentional Network (FAN) - a family of ViT backb…