Self-Supervised Learning by Estimating Twin Class Distribution

Proceedings Paper Computer Science, Artificial Intelligence

SimMIM: a Simple Framework for Masked Image Modeling

Zhenda Xie et al.

Summary: This paper presents SimMIM, a simple framework for masked image modeling. The study shows that the simple designs of each component have strong representation learning performance and achieve high accuracy on ImageNet-1K. Additionally, this approach addresses the data-hungry issue faced by large-scale model training.

2022 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR) (2022)

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Proceedings Paper Computer Science, Artificial Intelligence

Masked Autoencoders Are Scalable Vision Learners

Kaiming He et al.

Summary: This paper presents a self-supervised learning method for computer vision based on masked autoencoders. By masking a portion of the input image and reconstructing the missing pixels, large models can be trained efficiently and effectively. The approach achieves high generalization performance and outperforms supervised pretraining in transfer learning tasks.

2022 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR 2022) (2022)

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Article Computer Science, Artificial Intelligence

Cascade R-CNN: High Quality Object Detection and Instance Segmentation

Zhaowei Cai et al.

Summary: In object detection, the commonly used IoU threshold of 0.5 can lead to noisy detections, and performance may degrade for larger thresholds. The Cascade R-CNN architecture addresses this issue by training detectors sequentially with increasing IoU thresholds and eliminating quality mismatches at inference, resulting in state-of-the-art performance and significant improvement in high-quality detection across various datasets. The model is also generalized to instance segmentation, achieving nontrivial improvements over existing methods.

IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE (2021)

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Proceedings Paper Computer Science, Artificial Intelligence