134 citations · 701 across the 33 of their papers we have counts for
23 papers · 1 filter
LOVM: Language-Only Vision Model Selection
Orr Zohar, Shih-Cheng Huang, Kuan-Chieh Wang +1
Pre-trained multi-modal vision-language models (VLMs) are becoming increasingly popular due to their exceptional performance on downstream vision applications, particularly in the…
Hyperbolic Deep Learning in Computer Vision: A Survey
Pascal Mettes, Mina Ghadimi Atigh, Martin Keller-Ressel +2
Deep representation learning is a ubiquitous part of modern computer vision. While Euclidean space has been the de facto standard manifold for learning visual representations, hype…
Adapting Pre-trained Vision Transformers from 2D to 3D through Weight Inflation Improves Medical Image Segmentation
Yuhui Zhang, Shih-Cheng Huang, Zhengping Zhou +2
Given the prevalence of 3D medical imaging technologies such as MRI and CT that are widely used in diagnosing and treating diverse diseases, 3D segmentation is one of the fundament…
NeMo: 3D Neural Motion Fields from Multiple Video Instances of the Same Action
Kuan-Chieh Wang, Zhenzhen Weng, Maria Xenochristou +4
The task of reconstructing 3D human motion has wideranging applications. The gold standard Motion capture (MoCap) systems are accurate but inaccessible to the general public due to…
PROB: Probabilistic Objectness for Open World Object Detection
Orr Zohar, Kuan-Chieh Wang, Serena Yeung
Open World Object Detection (OWOD) is a new and challenging computer vision task that bridges the gap between classic object detection (OD) benchmarks and object detection in the r…
Adaptation of Surgical Activity Recognition Models Across Operating Rooms
Ali Mottaghi, Aidean Sharghi, Serena Yeung +1
Automatic surgical activity recognition enables more intelligent surgical devices and a more efficient workflow. Integration of such technology in new operating rooms has the poten…
Domain Adaptive 3D Pose Augmentation for In-the-wild Human Mesh Recovery
Zhenzhen Weng, Kuan-Chieh Wang, Angjoo Kanazawa +1
The ability to perceive 3D human bodies from a single image has a multitude of applications ranging from entertainment and robotics to neuroscience and healthcare. A fundamental ch…
A real-time spatiotemporal AI model analyzes skill in open surgical videos
Emmett D. Goodman, Krishna K. Patel, Yilun Zhang +10
Open procedures represent the dominant form of surgery worldwide. Artificial intelligence (AI) has the potential to optimize surgical practice and improve patient outcomes, but eff…
FlowVOS: Weakly-Supervised Visual Warping for Detail-Preserving and Temporally Consistent Single-Shot Video Object Segmentation
Julia Gong, F. Christopher Holsinger, Serena Yeung
We consider the task of semi-supervised video object segmentation (VOS). Our approach mitigates shortcomings in previous VOS work by addressing detail preservation and temporal con…
Staying in Shape: Learning Invariant Shape Representations using Contrastive Learning
Jeffrey Gu, Serena Yeung
Creating representations of shapes that are invari-ant to isometric or almost-isometric transforma-tions has long been an area of interest in shape anal-ysis, since enforcing invar…
DARCNN: Domain Adaptive Region-based Convolutional Neural Network for Unsupervised Instance Segmentation in Biomedical Images
Joy Hsu, Wah Chiu, Serena Yeung
In the biomedical domain, there is an abundance of dense, complex data where objects of interest may be challenging to detect or constrained by limits of human knowledge. Labelled…
Unsupervised Discovery of the Long-Tail in Instance Segmentation Using Hierarchical Self-Supervision
Zhenzhen Weng, Mehmet Giray Ogut, Shai Limonchik +1
Instance segmentation is an active topic in computer vision that is usually solved by using supervised learning approaches over very large datasets composed of object level masks.…
Using Computer Vision to Automate Hand Detection and Tracking of Surgeon Movements in Videos of Open Surgery
Michael Zhang, Xiaotian Cheng, Daniel Copeland +4
Open, or non-laparoscopic surgery, represents the vast majority of all operating room procedures, but few tools exist to objectively evaluate these techniques at scale. Current eff…
Capturing implicit hierarchical structure in 3D biomedical images with self-supervised hyperbolic representations
Joy Hsu, Jeffrey Gu, Gong-Her Wu +2
We consider the task of representation learning for unsupervised segmentation of 3D voxel-grid biomedical images. We show that models that capture implicit hierarchical relationshi…
Holistic 3D Human and Scene Mesh Estimation from Single View Images
Zhenzhen Weng, Serena Yeung
The 3D world limits the human body pose and the human body pose conveys information about the surrounding objects. Indeed, from a single image of a person placed in an indoor scene…
Adversarial Representation Active Learning
Ali Mottaghi, Serena Yeung
Active learning aims to develop label-efficient algorithms by querying the most informative samples to be labeled by an oracle. The design of efficient training methods that requir…
Tool Detection and Operative Skill Assessment in Surgical Videos Using Region-Based Convolutional Neural Networks
Amy Jin, Serena Yeung, Jeffrey Jopling +4
Five billion people in the world lack access to quality surgical care. Surgeon skill varies dramatically, and many surgical patients suffer complications and avoidable harm. Improv…
Towards Vision-Based Smart Hospitals: A System for Tracking and Monitoring Hand Hygiene Compliance
Albert Haque, Michelle Guo, Alexandre Alahi +10
One in twenty-five patients admitted to a hospital will suffer from a hospital acquired infection. If we can intelligently track healthcare staff, patients, and visitors, we can be…
Learning to Learn from Noisy Web Videos
Serena Yeung, Vignesh Ramanathan, Olga Russakovsky +3
Understanding the simultaneously very diverse and intricately fine-grained set of possible human actions is a critical open problem in computer vision. Manually labeling training v…
Towards Viewpoint Invariant 3D Human Pose Estimation
Albert Haque, Boya Peng, Zelun Luo +3
We propose a viewpoint invariant model for 3D human pose estimation from a single depth image. To achieve this, our discriminative model embeds local regions into a learned viewpoi…
End-to-end Learning of Action Detection from Frame Glimpses in Videos
Serena Yeung, Olga Russakovsky, Greg Mori +1
In this work we introduce a fully end-to-end approach for action detection in videos that learns to directly predict the temporal bounds of actions. Our intuition is that the proce…
Every Moment Counts: Dense Detailed Labeling of Actions in Complex Videos
Serena Yeung, Olga Russakovsky, Ning Jin +3
Every moment counts in action recognition. A comprehensive understanding of human activity in video requires labeling every frame according to the actions occurring, placing multip…
VideoSET: Video Summary Evaluation through Text
Serena Yeung, Alireza Fathi, Li Fei-Fei
In this paper we present VideoSET, a method for Video Summary Evaluation through Text that can evaluate how well a video summary is able to retain the semantic information containe…