Video Recognition
147 papers with code • 0 benchmarks • 10 datasets
Video Recognition is a process of obtaining, processing, and analysing data that it receives from a visual source, specifically video.
Benchmarks
These leaderboards are used to track progress in Video Recognition
Libraries
Use these libraries to find Video Recognition models and implementationsDatasets
Kinetics
BosphorusSign22k
MIDV-2019
3DYoga90
L-SVD
Crowd 11
RAISE-LPBF
Most implemented papers
Revisiting 3D ResNets for Video Recognition
A recent work from Bello shows that training and scaling strategies may be more significant than model architectures for visual recognition.
Revisiting Classifier: Transferring Vision-Language Models for Video Recognition
whwu95/text4vis
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In this study, we focus on transferring knowledge for video classification tasks.
Bidirectional Cross-Modal Knowledge Exploration for Video Recognition with Pre-trained Vision-Language Models
whwu95/BIKE
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CVPR 2023
In this paper, we propose a novel framework called BIKE, which utilizes the cross-modal bridge to explore bidirectional knowledge: i) We introduce the Video Attribute Association mechanism, which leverages the Video-to-Text knowledge to generate textual auxiliary attributes for complementing video recognition.
TokenLearner: What Can 8 Learned Tokens Do for Images and Videos?
In this paper, we introduce a novel visual representation learning which relies on a handful of adaptively learned tokens, and which is applicable to both image and video understanding tasks.
TSM: Temporal Shift Module for Efficient and Scalable Video Understanding on Edge Device
MIT-HAN-LAB/temporal-shift-module
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Secondly, TSM has high efficiency; it achieves a high frame rate of 74fps and 29fps for online video recognition on Jetson Nano and Galaxy Note8.
Deep Feature Flow for Video Recognition
msracver/Deep-Feature-Flow
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CVPR 2017
Yet, it is non-trivial to transfer the state-of-the-art image recognition networks to videos as per-frame evaluation is too slow and unaffordable.
Audiovisual SlowFast Networks for Video Recognition
We present Audiovisual SlowFast Networks, an architecture for integrated audiovisual perception.
Omni-sourced Webly-supervised Learning for Video Recognition
open-mmlab/mmaction
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ECCV 2020
Then a joint-training strategy is proposed to deal with the domain gaps between multiple data sources and formats in webly-supervised learning.
Pyramidal Convolution: Rethinking Convolutional Neural Networks for Visual Recognition
iduta/pyconv
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This work introduces pyramidal convolution (PyConv), which is capable of processing the input at multiple filter scales.
MVFNet: Multi-View Fusion Network for Efficient Video Recognition
whwu95/MVFNet
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Existing state-of-the-art methods have achieved excellent accuracy regardless of the complexity meanwhile efficient spatiotemporal modeling solutions are slightly inferior in performance.
