Video Classification
State-of-the-art leaderboards
Greatest papers with code
Group Normalization
ECCV 2018 • facebookresearch/detectron
FAIR's research platform for object detection research, implementing popular algorithms like Mask R-CNN and RetinaNet.
#22 best model for
Object Detection
on COCO
Non-local Neural Networks
CVPR 2018 • facebookresearch/detectron
Both convolutional and recurrent operations are building blocks that process one local neighborhood at a time.
#4 best model for
Instance Segmentation
on COCO
INSTANCE SEGMENTATION KEYPOINT DETECTION OBJECT DETECTION VIDEO CLASSIFICATION
YouTube-8M: A Large-Scale Video Classification Benchmark
Despite the size of the dataset, some of our models train to convergence in less than a day on a single machine using TensorFlow.
Temporal Segment Networks for Action Recognition in Videos
• yjxiong/temporal-segment-networks
• 
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.
#4 best model for
Action Classification
on Moments in Time
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.
TS-LSTM and Temporal-Inception: Exploiting Spatiotemporal Dynamics for Activity Recognition
• jeffreyhuang1/two-stream-action-recognition
We demonstrate that using both RNNs (using LSTMs) and Temporal-ConvNets on spatiotemporal feature matrices are able to exploit spatiotemporal dynamics to improve the overall performance.
ACTION CLASSIFICATION ACTION RECOGNITION IN VIDEOS ACTIVITY RECOGNITION VIDEO UNDERSTANDING
Learnable pooling with Context Gating for video classification
• antoine77340/Youtube-8M-WILLOW
• 
In particular, we evaluate our method on the large-scale multi-modal Youtube-8M v2 dataset and outperform all other methods in the Youtube 8M Large-Scale Video Understanding challenge.
Deep Temporal Linear Encoding Networks
CVPR 2017
• bryanyzhu/two-stream-pytorch
•
Advantages of TLEs are: (a) they encode the entire video into a compact feature representation, learning the semantics and a discriminative feature space; (b) they are applicable to all kinds of networks like 2D and 3D CNNs for video classification; and (c) they model feature interactions in a more expressive way and without loss of information.
Learning Spatio-Temporal Representation with Pseudo-3D Residual Networks
ICCV 2017
• ZhaofanQiu/pseudo-3d-residual-networks
•
In this paper, we devise multiple variants of bottleneck building blocks in a residual learning framework by simulating $3\times3\times3$ convolutions with $1\times3\times3$ convolutional filters on spatial domain (equivalent to 2D CNN) plus $3\times1\times1$ convolutions to construct temporal connections on adjacent feature maps in time.
Learning Representations from EEG with Deep Recurrent-Convolutional Neural Networks
One of the challenges in modeling cognitive events from electroencephalogram (EEG) data is finding representations that are invariant to inter- and intra-subject differences, as well as to inherent noise associated with such data.