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Found 7 papers, 4 papers with code
Layered Rendering Diffusion Model for Zero-Shot Guided Image Synthesis
The LRDiff framework constructs an image-rendering process with multiple layers, each of which applies the vision guidance to instructively estimate the denoising direction for a single object.
Masked Image Residual Learning for Scaling Deeper Vision Transformers
With the same level of computational complexity as ViT-Base and ViT-Large, we instantiate 4. 5$\times$ and 2$\times$ deeper ViTs, dubbed ViT-S-54 and ViT-B-48.
Dynamic Appearance: A Video Representation for Action Recognition with Joint Training
Static appearance of video may impede the ability of a deep neural network to learn motion-relevant features in video action recognition.
BQN: Busy-Quiet Net Enabled by Motion Band-Pass Module for Action Recognition
Through experiments we show that the proposed MBPM can be used as a plug-in module in various CNN backbone architectures, significantly boosting their performance.
Busy-Quiet Video Disentangling for Video Classification
We design a trainable Motion Band-Pass Module (MBPM) for separating busy information from quiet information in raw video data.
Ranked #15 on
Action Recognition
on UCF101
Region-based Non-local Operation for Video Classification
Convolutional Neural Networks (CNNs) model long-range dependencies by deeply stacking convolution operations with small window sizes, which makes the optimizations difficult.
Ranked #32 on
Action Recognition
on Something-Something V1
Learning spatio-temporal representations with temporal squeeze pooling
In this paper, we propose a new video representation learning method, named Temporal Squeeze (TS) pooling, which can extract the essential movement information from a long sequence of video frames and map it into a set of few images, named Squeezed Images.
Ranked #43 on
Action Recognition
on UCF101
(using extra training data)
