Search Results for author:
Found 16 papers, 7 papers with code
TALL: Temporal Activity Localization via Language Query
For evaluation, we adopt TaCoS dataset, and build a new dataset for this task on top of Charades by adding sentence temporal annotations, called Charades-STA.
Joint Unsupervised Learning of Optical Flow and Depth by Watching Stereo Videos
Then the whole scene is decomposed into moving foreground and static background by compar- ing the estimated optical flow and rigid flow derived from the depth and ego-motion.
TURN TAP: Temporal Unit Regression Network for Temporal Action Proposals
Temporal Action Proposal (TAP) generation is an important problem, as fast and accurate extraction of semantically important (e. g. human actions) segments from untrimmed videos is an important step for large-scale video analysis.
Ranked #8 on
Action Recognition
on THUMOS’14
LEGO: Learning Edge with Geometry all at Once by Watching Videos
In our framework, the predicted depths, normals and edges are forced to be consistent all the time.
SPAN: Spatial Pyramid Attention Network for Image Manipulation Localization
Tehchniques for manipulating images are advancing rapidly; while these are helpful for many useful tasks, they also pose a threat to society with their ability to create believable misinformation.
Ranked #5 on
Image Manipulation Localization
on Columbia
Every Pixel Counts ++: Joint Learning of Geometry and Motion with 3D Holistic Understanding
Performance on the five tasks of depth estimation, optical flow estimation, odometry, moving object segmentation and scene flow estimation shows that our approach outperforms other SoTA methods.
RED: Reinforced Encoder-Decoder Networks for Action Anticipation
RED takes multiple history representations as input and learns to anticipate a sequence of future representations.
Occlusion Aware Unsupervised Learning of Optical Flow
Especially on KITTI dataset where abundant unlabeled samples exist, our unsupervised method outperforms its counterpart trained with supervised learning.
Unsupervised Learning of Geometry with Edge-aware Depth-Normal Consistency
Learning to reconstruct depths in a single image by watching unlabeled videos via deep convolutional network (DCN) is attracting significant attention in recent years.
Spatio-Temporal Action Detection with Cascade Proposal and Location Anticipation
In this work, we address the problem of spatio-temporal action detection in temporally untrimmed videos.
Cascaded Boundary Regression for Temporal Action Detection
CBR uses temporal coordinate regression to refine the temporal boundaries of the sliding windows.
Ranked #6 on
Temporal Action Localization
on THUMOS’14
(mAP IOU@0.1 metric)
A Multi-Scale Cascade Fully Convolutional Network Face Detector
The number of proposals is decreased after each level, and the areas of regions are decreased to more precisely fit the face.
Every Pixel Counts: Unsupervised Geometry Learning with Holistic 3D Motion Understanding
The four types of information, i. e. 2D flow, camera pose, segment mask and depth maps, are integrated into a differentiable holistic 3D motion parser (HMP), where per-pixel 3D motion for rigid background and moving objects are recovered.
Activity Driven Weakly Supervised Object Detection
Weakly supervised object detection aims at reducing the amount of supervision required to train detection models.
Ranked #1 on
Weakly Supervised Object Detection
on Charades
SPAN: Spatial Pyramid Attention Network forImage Manipulation Localization
We present a novel framework, Spatial Pyramid Attention Network (SPAN) for detection and localization of multiple types of image manipulations.
Weakly Supervised Instance Segmentation for Videos with Temporal Mask Consistency
However, existing approaches which rely only on image-level class labels predominantly suffer from errors due to (a) partial segmentation of objects and (b) missing object predictions.
