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Found 6 papers, 4 papers with code
Delving Deeper into Data Scaling in Masked Image Modeling
The study reveals that: 1) MIM can be viewed as an effective method to improve the model capacity when the scale of the training data is relatively small; 2) Strong reconstruction targets can endow the models with increased capacities on downstream tasks; 3) MIM pre-training is data-agnostic under most scenarios, which means that the strategy of sampling pre-training data is non-critical.
CMAE-V: Contrastive Masked Autoencoders for Video Action Recognition
Contrastive Masked Autoencoder (CMAE), as a new self-supervised framework, has shown its potential of learning expressive feature representations in visual image recognition.
Conv2Former: A Simple Transformer-Style ConvNet for Visual Recognition
This paper does not attempt to design a state-of-the-art method for visual recognition but investigates a more efficient way to make use of convolutions to encode spatial features.
SegNeXt: Rethinking Convolutional Attention Design for Semantic Segmentation
Notably, SegNeXt outperforms EfficientNet-L2 w/ NAS-FPN and achieves 90. 6% mIoU on the Pascal VOC 2012 test leaderboard using only 1/10 parameters of it.
Ranked #1 on
Semantic Segmentation
on iSAID
Towards An End-to-End Framework for Flow-Guided Video Inpainting
Optical flow, which captures motion information across frames, is exploited in recent video inpainting methods through propagating pixels along its trajectories.
Ranked #2 on
Seeing Beyond the Visible
on KITTI360-EX
Visual Attention Network
In this paper, we propose a novel linear attention named large kernel attention (LKA) to enable self-adaptive and long-range correlations in self-attention while avoiding its shortcomings.
Ranked #1 on
Panoptic Segmentation
on COCO panoptic
