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
Latest papers
Open-VCLIP: Transforming CLIP to an Open-vocabulary Video Model via Interpolated Weight Optimization
Our framework extends CLIP with minimal modifications to model spatial-temporal relationships in videos, making it a specialized video classifier, while striving for generalization.
Revisiting Temporal Modeling for CLIP-based Image-to-Video Knowledge Transferring
farewellthree/stan
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CVPR 2023
In this paper, based on the CLIP model, we revisit temporal modeling in the context of image-to-video knowledge transferring, which is the key point for extending image-text pretrained models to the video domain.
Efficient Robustness Assessment via Adversarial Spatial-Temporal Focus on Videos
To implement this idea, we design the novel Adversarial spatial-temporal Focus (AstFocus) attack on videos, which performs attacks on the simultaneously focused key frames and key regions from the inter-frames and intra-frames in the video.
Bidirectional Cross-Modal Knowledge Exploration for Video Recognition with Pre-trained Vision-Language Models
whwu95/Cap4Video
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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.
Efficient Movie Scene Detection using State-Space Transformers
md-mohaiminul/trans4mer
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CVPR 2023
Given a sequence of frames divided into movie shots (uninterrupted periods where the camera position does not change), the S4A block first applies self-attention to capture short-range intra-shot dependencies.
VLG: General Video Recognition with Web Textual Knowledge
mcg-nju/vlg
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Our VLG is first pre-trained on video and language datasets to learn a shared feature space, and then devises a flexible bi-modal attention head to collaborate high-level semantic concepts under different settings.
SVFormer: Semi-supervised Video Transformer for Action Recognition
chenhsing/svformer
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CVPR 2023
In this paper, we investigate the use of transformer models under the SSL setting for action recognition.
Look More but Care Less in Video Recognition
bespontaneous/afnet-pytorch
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To tackle this problem, we propose Ample and Focal Network (AFNet), which is composed of two branches to utilize more frames but with less computation.
Cluster and Aggregate: Face Recognition with Large Probe Set
Advances in attention and recurrent modules have led to feature fusion that can model the relationship among the images in the input set.
Towards a Unified View on Visual Parameter-Efficient Transfer Learning
bruceyo/V-PETL
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Towards this goal, we propose a framework with a unified view of PETL called visual-PETL (V-PETL) to investigate the effects of different PETL techniques, data scales of downstream domains, positions of trainable parameters, and other aspects affecting the trade-off.
