Search Results for author:
Found 30 papers, 11 papers with code
Max-Margin Structured Output Regression for Spatio-Temporal Action Localization
The mapping between a video and a spatio-temporal action trajectory is learned.
EXMOVES: Classifier-based Features for Scalable Action Recognition
We show the generality of our approach by building our mid-level descriptors from two different low-level feature representations.
Learning Spatiotemporal Features with 3D Convolutional Networks
We propose a simple, yet effective approach for spatiotemporal feature learning using deep 3-dimensional convolutional networks (3D ConvNets) trained on a large scale supervised video dataset.
Ranked #8 on
Action Recognition
on Sports-1M
Action Recognition In Videos
Dynamic Facial Expression Recognition
Deep End2End Voxel2Voxel Prediction
Over the last few years deep learning methods have emerged as one of the most prominent approaches for video analysis.
VideoMCC: a New Benchmark for Video Comprehension
Language has been exploited to sidestep the problem of defining video categories, by formulating video understanding as the task of captioning or description.
Transformation-Based Models of Video Sequences
In this work we propose a simple unsupervised approach for next frame prediction in video.
ConvNet Architecture Search for Spatiotemporal Feature Learning
Learning image representations with ConvNets by pre-training on ImageNet has proven useful across many visual understanding tasks including object detection, semantic segmentation, and image captioning.
Ranked #71 on
Action Recognition
on HMDB-51
A Closer Look at Spatiotemporal Convolutions for Action Recognition
In this paper we discuss several forms of spatiotemporal convolutions for video analysis and study their effects on action recognition.
Ranked #3 on
Action Recognition
on Sports-1M
Detect-and-Track: Efficient Pose Estimation in Videos
This paper addresses the problem of estimating and tracking human body keypoints in complex, multi-person video.
Ranked #8 on
Pose Tracking
on PoseTrack2017
(using extra training data)
Cooperative Learning of Audio and Video Models from Self-Supervised Synchronization
There is a natural correlation between the visual and auditive elements of a video.
Ranked #7 on
Self-Supervised Audio Classification
on ESC-50
Scenes-Objects-Actions: A Multi-Task, Multi-Label Video Dataset
The videos retrieved by the search engines are then veried for correctness by human annotators.
Learning Discriminative Motion Features Through Detection
Our network learns to spatially sample features from Frame B in order to maximize pose detection accuracy in Frame A.
DistInit: Learning Video Representations Without a Single Labeled Video
In this work, we propose an alternative approach to learning video representations that require no semantically labeled videos and instead leverages the years of effort in collecting and labeling large and clean still-image datasets.
Ranked #72 on
Action Recognition
on HMDB-51
(using extra training data)
Video Classification with Channel-Separated Convolutional Networks
It is natural to ask: 1) if group convolution can help to alleviate the high computational cost of video classification networks; 2) what factors matter the most in 3D group convolutional networks; and 3) what are good computation/accuracy trade-offs with 3D group convolutional networks.
Ranked #1 on
Action Recognition
on Sports-1M
SCSampler: Sampling Salient Clips from Video for Efficient Action Recognition
We demonstrate that the computational cost of action recognition on untrimmed videos can be dramatically reduced by invoking recognition only on these most salient clips.
Ranked #1 on
Action Recognition
on miniSports
Large-scale weakly-supervised pre-training for video action recognition
Second, frame-based models perform quite well on action recognition; is pre-training for good image features sufficient or is pre-training for spatio-temporal features valuable for optimal transfer learning?
Ranked #2 on
Egocentric Activity Recognition
on EPIC-KITCHENS-55
(Actions Top-1 (S2) metric)
What Makes Training Multi-Modal Classification Networks Hard?
Consider end-to-end training of a multi-modal vs. a single-modal network on a task with multiple input modalities: the multi-modal network receives more information, so it should match or outperform its single-modal counterpart.
Ranked #1 on
Action Recognition In Videos
on miniSports
Learning Temporal Pose Estimation from Sparsely-Labeled Videos
To reduce the need for dense annotations, we propose a PoseWarper network that leverages training videos with sparse annotations (every k frames) to learn to perform dense temporal pose propagation and estimation.
Ranked #2 on
Multi-Person Pose Estimation
on PoseTrack2018
(using extra training data)
Video Modeling with Correlation Networks
Motion is a salient cue to recognize actions in video.
Ranked #108 on
Action Classification
on Kinetics-400
UniDual: A Unified Model for Image and Video Understanding
It consists of a shared 2D spatial convolution followed by two parallel point-wise convolutional layers, one devoted to images and the other one used for videos.
FASTER Recurrent Networks for Efficient Video Classification
FASTER aims to leverage the redundancy between neighboring clips and reduce the computational cost by learning to aggregate the predictions from models of different complexities.
Ranked #26 on
Action Recognition
on UCF101
Self-Supervised Learning by Cross-Modal Audio-Video Clustering
To the best of our knowledge, XDC is the first self-supervised learning method that outperforms large-scale fully-supervised pretraining for action recognition on the same architecture.
FLAVR: Flow-Agnostic Video Representations for Fast Frame Interpolation
A majority of methods for video frame interpolation compute bidirectional optical flow between adjacent frames of a video, followed by a suitable warping algorithm to generate the output frames.
Ranked #2 on
Video Frame Interpolation
on GoPro
Unidentified Video Objects: A Benchmark for Dense, Open-World Segmentation
Current state-of-the-art object detection and segmentation methods work well under the closed-world assumption.
Long-Short Temporal Contrastive Learning of Video Transformers
Our approach, named Long-Short Temporal Contrastive Learning (LSTCL), enables video transformers to learn an effective clip-level representation by predicting temporal context captured from a longer temporal extent.
Open-World Instance Segmentation: Exploiting Pseudo Ground Truth From Learned Pairwise Affinity
From PA we construct a large set of pseudo-ground-truth instance masks; combined with human-annotated instance masks we train GGNs and significantly outperform the SOTA on open-world instance segmentation on various benchmarks including COCO, LVIS, ADE20K, and UVO.
Relational Space-Time Query in Long-Form Videos
In this paper, we propose to study these problems in a joint framework for long video understanding.
MINOTAUR: Multi-task Video Grounding From Multimodal Queries
Video understanding tasks take many forms, from action detection to visual query localization and spatio-temporal grounding of sentences.
Open-world Instance Segmentation: Top-down Learning with Bottom-up Supervision
Many top-down architectures for instance segmentation achieve significant success when trained and tested on pre-defined closed-world taxonomy.
Learning Space-Time Semantic Correspondences
We propose a new task of space-time semantic correspondence prediction in videos.
