Benchmarks
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Greatest papers with code
Self-Supervised MultiModal Versatile Networks
NeurIPS 2020
• deepmind/deepmind-research
• 
In particular, we explore how best to combine the modalities, such that fine-grained representations of the visual and audio modalities can be maintained, whilst also integrating text into a common embedding.
Ranked #2 on
Self-Supervised Action Recognition
on HMDB51
ACTION RECOGNITION IN VIDEOS SELF-SUPERVISED ACTION RECOGNITION
Temporal Segment Networks for Action Recognition in Videos
Furthermore, based on the temporal segment networks, we won the video classification track at the ActivityNet challenge 2016 among 24 teams, which demonstrates the effectiveness of TSN and the proposed good practices.
Ranked #5 on
Action Classification
on Moments in Time
(Top 5 Accuracy metric)
ACTION CLASSIFICATION ACTION RECOGNITION ACTION RECOGNITION IN VIDEOS ACTION RECOGNITION IN VIDEOS
Temporal Segment Networks: Towards Good Practices for Deep Action Recognition
• yjxiong/temporal-segment-networks
• 
The other contribution is our study on a series of good practices in learning ConvNets on video data with the help of temporal segment network.
Ranked #3 on
Multimodal Activity Recognition
on EV-Action
ACTION RECOGNITION ACTION RECOGNITION IN VIDEOS ACTION RECOGNITION IN VIDEOS MULTIMODAL ACTIVITY RECOGNITION
Convolutional Two-Stream Network Fusion for Video Action Recognition
Recent applications of Convolutional Neural Networks (ConvNets) for human action recognition in videos have proposed different solutions for incorporating the appearance and motion information.
Ranked #21 on
Action Recognition
on UCF101
ACTION RECOGNITION ACTION RECOGNITION IN VIDEOS ACTION RECOGNITION IN VIDEOS
Towards Good Practices for Very Deep Two-Stream ConvNets
However, for action recognition in videos, the improvement of deep convolutional networks is not so evident.
Ranked #25 on
Action Recognition
on UCF101
ACTION RECOGNITION ACTION RECOGNITION IN VIDEOS ACTION RECOGNITION IN VIDEOS DATA AUGMENTATION
Learning Spatiotemporal Features with 3D Convolutional Networks
ICCV 2015
• open-mmlab/mmaction2
•
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 #1 on
Action Recognition In Videos
on Sports-1M
Action Recognition using Visual Attention
We propose a soft attention based model for the task of action recognition in videos.
ACTION RECOGNITION ACTION RECOGNITION IN VIDEOS ACTION RECOGNITION IN VIDEOS
Optical Flow Guided Feature: A Fast and Robust Motion Representation for Video Action Recognition
In this study, we introduce a novel compact motion representation for video action recognition, named Optical Flow guided Feature (OFF), which enables the network to distill temporal information through a fast and robust approach.
Ranked #12 on
Action Recognition
on UCF101
ACTION RECOGNITION ACTION RECOGNITION IN VIDEOS ACTION RECOGNITION IN VIDEOS OPTICAL FLOW ESTIMATION
Spatiotemporal Residual Networks for Video Action Recognition
Two-stream Convolutional Networks (ConvNets) have shown strong performance for human action recognition in videos.
ACTION RECOGNITION ACTION RECOGNITION IN VIDEOS ACTION RECOGNITION IN VIDEOS
Two-Stream Convolutional Networks for Action Recognition in Videos
Our architecture is trained and evaluated on the standard video actions benchmarks of UCF-101 and HMDB-51, where it is competitive with the state of the art.
Ranked #1 on
Action Classification
on HMDB51
ACTION CLASSIFICATION ACTION CLASSIFICATION ACTION RECOGNITION ACTION RECOGNITION IN VIDEOS ACTION RECOGNITION IN VIDEOS MULTI-TASK LEARNING OPTICAL FLOW ESTIMATION