Video Alignment
22 papers with code • 2 benchmarks • 4 datasets
Benchmarks
These leaderboards are used to track progress in Video Alignment
| Trend | Dataset | Best Model | Paper | Code | Compare |
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Penn Action
N-Digit MNIST
IAW Dataset
Latest papers with no code
Learning by Aligning 2D Skeleton Sequences and Multi-Modality Fusion
This paper presents a self-supervised temporal video alignment framework which is useful for several fine-grained human activity understanding applications.
Edit As You Wish: Video Description Editing with Multi-grained Commands
In this paper, we propose a novel Video Description Editing (VDEdit) task to automatically revise an existing video description guided by flexible user requests.
Video alignment using unsupervised learning of local and global features
We introduce an unsupervised method for alignment that uses global and local features of the frames.
VADER: Video Alignment Differencing and Retrieval
We propose VADER, a spatio-temporal matching, alignment, and change summarization method to help fight misinformation spread via manipulated videos.
Weakly-supervised Representation Learning for Video Alignment and Analysis
This paper introduces LRProp -- a novel weakly-supervised representation learning approach, with an emphasis on the application of temporal alignment between pairs of videos of the same action category.
PIDRo: Parallel Isomeric Attention with Dynamic Routing for Text-Video Retrieval
The parallel isomeric attention module is used as the video encoder, which consists of two parallel branches modeling the spatial-temporal information of videos from both patch and frame levels.
Self-supervised and Weakly Supervised Contrastive Learning for Frame-wise Action Representations
In this paper, we introduce a new framework of contrastive action representation learning (CARL) to learn frame-wise action representation in a self-supervised or weakly-supervised manner, especially for long videos.
Learning by Aligning Videos in Time
To overcome this problem, we propose a temporal regularization term (i. e., Contrastive-IDM) which encourages different frames to be mapped to different points in the embedding space.
Normalized Human Pose Features for Human Action Video Alignment
We present a novel approach for extracting human pose features from human action videos.
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