Search Results for author:
Found 20 papers, 9 papers with code
GC-MVSNet: Multi-View, Multi-Scale, Geometrically-Consistent Multi-View Stereo
Traditional multi-view stereo (MVS) methods rely heavily on photometric and geometric consistency constraints, but newer machine learning-based MVS methods check geometric consistency across multiple source views only as a post-processing step.
Ranked #5 on
3D Reconstruction
on DTU
The Affective Growth of Computer Vision
The success of deep learning has led to intense growth and interest in computer vision, along with concerns about its potential impact on society.
Semantically Stealthy Adversarial Attacks against Segmentation Models
One challenge is making semantically meaningful manipulations across datasets and models.
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
Dynamic Dual-Attentive Aggregation Learning for Visible-Infrared Person Re-Identification
In this paper, we propose a novel dynamic dual-attentive aggregation (DDAG) learning method by mining both intra-modality part-level and cross-modality graph-level contextual cues for VI-ReID.
Pose-Guided Knowledge Transfer for Object Part Segmentation
Object part segmentation is an important problem for many applications, but generating the annotations to train a part segmentation model is typically quite labor-intensive.
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.
Minimizing Supervision for Free-space Segmentation
Our work demonstrates the potential for performing free-space segmentation without tedious and costly manual annotation, which will be important for adapting autonomous driving systems to different types of vehicles and environments
A Study of Cross-domain Generative Models applied to Cartoon Series
We investigate Generative Adversarial Networks (GANs) to model one particular kind of image: frames from TV cartoons.
A Unified Model for Near and Remote Sensing
To evaluate our approach, we created a large dataset of overhead and ground-level images from a major urban area with three sets of labels: land use, building function, and building age.
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.
DeepDiary: Automatic Caption Generation for Lifelogging Image Streams
Lifelogging cameras capture everyday life from a first-person perspective, but generate so much data that it is hard for users to browse and organize their image collections effectively.
Lending A Hand: Detecting Hands and Recognizing Activities in Complex Egocentric Interactions
Hands appear very often in egocentric video, and their appearance and pose give important cues about what people are doing and what they are paying attention to.
Multimodal Learning in Loosely-organized Web Images
Photo-sharing websites have become very popular in the last few years, leading to huge collections of online images.
