Search Results for author:
Found 22 papers, 11 papers with code
SkeleTR: Towrads Skeleton-based Action Recognition in the Wild
It first models the intra-person skeleton dynamics for each skeleton sequence with graph convolutions, and then uses stacked Transformer encoders to capture person interactions that are important for action recognition in general scenarios.
SkeleTR: Towards Skeleton-based Action Recognition in the Wild
It first models the intra-person skeleton dynamics for each skeleton sequence with graph convolutions, and then uses stacked Transformer encoders to capture person interactions that are important for action recognition in the wild.
Ranked #2 on
Human Interaction Recognition
on NTU RGB+D
An In-depth Study of Stochastic Backpropagation
In this paper, we provide an in-depth study of Stochastic Backpropagation (SBP) when training deep neural networks for standard image classification and object detection tasks.
Stochastic Backpropagation: A Memory Efficient Strategy for Training Video Models
We propose a memory efficient method, named Stochastic Backpropagation (SBP), for training deep neural networks on videos.
MeMOT: Multi-Object Tracking with Memory
We propose an online tracking algorithm that performs the object detection and data association under a common framework, capable of linking objects after a long time span.
Long Short-Term Transformer for Online Action Detection
We present Long Short-term TRansformer (LSTR), a temporal modeling algorithm for online action detection, which employs a long- and short-term memory mechanism to model prolonged sequence data.
Ranked #3 on
Online Action Detection
on TVSeries
Semi-TCL: Semi-Supervised Track Contrastive Representation Learning
We design a new instance-to-track matching objective to learn appearance embedding that compares a candidate detection to the embedding of the tracks persisted in the tracker.
TubeR: Tubelet Transformer for Video Action Detection
We propose TubeR: a simple solution for spatio-temporal video action detection.
Stepwise Goal-Driven Networks for Trajectory Prediction
We propose to predict the future trajectories of observed agents (e. g., pedestrians or vehicles) by estimating and using their goals at multiple time scales.
Ranked #1 on
Trajectory Prediction
on HEV-I
Learning Self-Consistency for Deepfake Detection
We propose a new method to detect deepfake images using the cue of the source feature inconsistency within the forged images.
Deep Tiered Image Segmentation For Detecting Internal Ice Layers in Radar Imagery
Understanding the structure of Earth's polar ice sheets is important for modeling how global warming will impact polar ice and, in turn, the Earth's climate.
When, Where, and What? A New Dataset for Anomaly Detection in Driving Videos
A new spatial-temporal area under curve (STAUC) evaluation metric is proposed and used with DoTA.
Embodied Visual Recognition
Passive visual systems typically fail to recognize objects in the amodal setting where they are heavily occluded.
StartNet: Online Detection of Action Start in Untrimmed Videos
We propose StartNet to address Online Detection of Action Start (ODAS) where action starts and their associated categories are detected in untrimmed, streaming videos.
Unsupervised Traffic Accident Detection in First-Person Videos
Recognizing abnormal events such as traffic violations and accidents in natural driving scenes is essential for successful autonomous driving and advanced driver assistance systems.
Ranked #1 on
Traffic Accident Detection
on A3D
Temporal Recurrent Networks for Online Action Detection
Most work on temporal action detection is formulated as an offline problem, in which the start and end times of actions are determined after the entire video is fully observed.
Ranked #12 on
Online Action Detection
on TVSeries
Egocentric Vision-based Future Vehicle Localization for Intelligent Driving Assistance Systems
Predicting the future location of vehicles is essential for safety-critical applications such as advanced driver assistance systems (ADAS) and autonomous driving.
Joint Person Segmentation and Identification in Synchronized First- and Third-person Videos
In this paper, we wish to solve two specific problems: (1) given two or more synchronized third-person videos of a scene, produce a pixel-level segmentation of each visible person and identify corresponding people across different views (i. e., determine who in camera A corresponds with whom in camera B), and (2) given one or more synchronized third-person videos as well as a first-person video taken by a mobile or wearable camera, segment and identify the camera wearer in the third-person videos.
Multi-Task Spatiotemporal Neural Networks for Structured Surface Reconstruction
Deep learning methods have surpassed the performance of traditional techniques on a wide range of problems in computer vision, but nearly all of this work has studied consumer photos, where precisely correct output is often not critical.
Fully-Coupled Two-Stream Spatiotemporal Networks for Extremely Low Resolution Action Recognition
A major emerging challenge is how to protect people's privacy as cameras and computer vision are increasingly integrated into our daily lives, including in smart devices inside homes.
Automatic Estimation of Ice Bottom Surfaces from Radar Imagery
Ground-penetrating radar on planes and satellites now makes it practical to collect 3D observations of the subsurface structure of the polar ice sheets, providing crucial data for understanding and tracking global climate change.
Identifying First-person Camera Wearers in Third-person Videos
We consider scenarios in which we wish to perform joint scene understanding, object tracking, activity recognition, and other tasks in environments in which multiple people are wearing body-worn cameras while a third-person static camera also captures the scene.
