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Found 39 papers, 15 papers with code
Hyperbolic Deep Learning in Computer Vision: A Survey
In this paper, we provide a categorization and in-depth overview of current literature on hyperbolic learning for computer vision.
Video Pretraining Advances 3D Deep Learning on Chest CT Tasks
We demonstrate video pretraining improves the average performance of seven 3D models on two chest CT datasets, regardless of finetuning dataset size, and that video pretraining allows 3D models to outperform 2D baselines.
Diffusion-HPC: Generating Synthetic Images with Realistic Humans
Recent text-to-image generative models have exhibited remarkable abilities in generating high-fidelity and photo-realistic images.
Adapting Pre-trained Vision Transformers from 2D to 3D through Weight Inflation Improves Medical Image Segmentation
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 fundamental tasks of medical image analysis.
Diagnosing and Rectifying Vision Models using Language
Our proposed method can discover high-error data slices, identify influential attributes and further rectify undesirable model behaviors, without requiring any visual data.
NeMo: Learning 3D Neural Motion Fields From Multiple Video Instances of the Same Action
Empirically, we show that NeMo can recover 3D motion in sports using videos from the Penn Action dataset, where NeMo outperforms existing HMR methods in terms of 2D keypoint detection.
NeMo: 3D Neural Motion Fields from Multiple Video Instances of the Same Action
Empirically, we show that NeMo can recover 3D motion in sports using videos from the Penn Action dataset, where NeMo outperforms existing HMR methods in terms of 2D keypoint detection.
PROB: Probabilistic Objectness for Open World Object Detection
The resulting Probabilistic Objectness transformer-based open-world detector, PROB, integrates our framework into traditional object detection models, adapting them for the open-world setting.
DataPerf: Benchmarks for Data-Centric AI Development
Machine learning (ML) research has generally focused on models, while the most prominent datasets have been employed for everyday ML tasks without regard for the breadth, difficulty, and faithfulness of these datasets to the underlying problem.
Adaptation of Surgical Activity Recognition Models Across Operating Rooms
We propose a new domain adaptation method to improve the performance of the surgical activity recognition model in a new operating room for which we only have unlabeled videos.
Domain Adaptive 3D Pose Augmentation for In-the-wild Human Mesh Recovery
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.
Mind the Gap: Understanding the Modality Gap in Multi-modal Contrastive Representation Learning
Our systematic analysis demonstrates that this gap is caused by a combination of model initialization and contrastive learning optimization.
A real-time spatiotemporal AI model analyzes skill in open surgical videos
Open procedures represent the dominant form of surgery worldwide.
FlowVOS: Weakly-Supervised Visual Warping for Detail-Preserving and Temporally Consistent Single-Shot Video Object Segmentation
In contrast to prior work that uses full optical flow, we introduce a new foreground-targeted visual warping approach that learns flow fields from VOS data.
Staying in Shape: Learning Invariant Shape Representations using Contrastive Learning
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 invariance allows the learningof more effective and robust shape representations. Most existing invariant shape representations arehandcrafted, and previous work on learning shaperepresentations do not focus on producing invariantrepresentations.
DARCNN: Domain Adaptive Region-based Convolutional Neural Network for Unsupervised Instance Segmentation in Biomedical Images
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.
Unsupervised Discovery of the Long-Tail in Instance Segmentation Using Hierarchical Self-Supervision
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.
GLoRIA: A Multimodal Global-Local Representation Learning Framework for Label-Efficient Medical Image Recognition
In recent years, the growing number of medical imaging studies is placing an ever-increasing burden on radiologists.
Personalized Federated Learning with First Order Model Optimization
While federated learning traditionally aims to train a single global model across decentralized local datasets, one model may not always be ideal for all participating clients.
Using Computer Vision to Automate Hand Detection and Tracking of Surgeon Movements in Videos of Open Surgery
A state-of-the-art convolutional neural network architecture for object detection was used to detect operating hands in open surgery videos.
Capturing implicit hierarchical structure in 3D biomedical images with self-supervised hyperbolic representations
To that end, we consider encoder-decoder architectures with a hyperbolic latent space, to explicitly capture hierarchical relationships present in subvolumes of the data.
Holistic 3D Human and Scene Mesh Estimation from Single View Images
Indeed, from a single image of a person placed in an indoor scene, we as humans are adept at resolving ambiguities of the human pose and room layout through our knowledge of the physical laws and prior perception of the plausible object and human poses.
Medical symptom recognition from patient text: An active learning approach for long-tailed multilabel distributions
We study the problem of medical symptoms recognition from patient text, for the purposes of gathering pertinent information from the patient (known as history-taking).
Learning Hyperbolic Representations for Unsupervised 3D Segmentation
There exists a need for unsupervised 3D segmentation on complex volumetric data, particularly when annotation ability is limited or discovery of new categories is desired.
Rapidly Personalizing Mobile Health Treatment Policies with Limited Data
In mobile health (mHealth), reinforcement learning algorithms that adapt to one's context without learning personalized policies might fail to distinguish between the needs of individuals.
Adversarial Representation Active Learning
Active learning aims to develop label-efficient algorithms by querying the most informative samples to be labeled by an oracle.
Faster CryptoNets: Leveraging Sparsity for Real-World Encrypted Inference
Homomorphic encryption enables arbitrary computation over data while it remains encrypted.
Cryptography and Security
Dynamic Task Prioritization for Multitask Learning
We propose dynamic task prioritization for multitask learning.
Temporal Modular Networks for Retrieving Complex Compositional Activities in Videos
A major challenge in computer vision is scaling activity understanding to the long tail of complex activities without requiring collecting large quantities of data for new actions.
Neural Graph Matching Networks for Fewshot 3D Action Recognition
We propose Neural Graph Matching (NGM) Networks, a novel framework that can learn to recognize a previous unseen 3D action class with only a few examples.
Ranked #1 on
Skeleton Based Action Recognition
on CAD-120
Tool Detection and Operative Skill Assessment in Surgical Videos Using Region-Based Convolutional Neural Networks
We show that our method both effectively detects the spatial bounds of tools as well as significantly outperforms existing methods on tool presence detection.
Towards Vision-Based Smart Hospitals: A System for Tracking and Monitoring Hand Hygiene Compliance
One in twenty-five patients admitted to a hospital will suffer from a hospital acquired infection.
Jointly Learning Energy Expenditures and Activities Using Egocentric Multimodal Signals
Physiological signals such as heart rate can provide valuable information about an individual's state and activity.
Tackling Over-pruning in Variational Autoencoders
The so-called epitomes of this model are groups of mutually exclusive latent factors that compete to explain the data.
Learning to Learn from Noisy Web Videos
Our method uses Q-learning to learn a data labeling policy on a small labeled training dataset, and then uses this to automatically label noisy web data for new visual concepts.
Towards Viewpoint Invariant 3D Human Pose Estimation
We propose a viewpoint invariant model for 3D human pose estimation from a single depth image.
Ranked #4 on
Pose Estimation
on ITOP top-view
End-to-end Learning of Action Detection from Frame Glimpses in Videos
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.
Ranked #9 on
Temporal Action Localization
on THUMOS’14
(mAP IOU@0.2 metric)
Every Moment Counts: Dense Detailed Labeling of Actions in Complex Videos
Every moment counts in action recognition.
Ranked #7 on
Action Detection
on Multi-THUMOS
VideoSET: Video Summary Evaluation through Text
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 contained in its original video.
