A Simple Framework for Contrastive Learning of Visual Representations
Abstract: This paper presents SimCLR: a simple framework for contrastive learning of visual representations. We simplify recently proposed contrastive self-supervised learning algorithms without requiring specialized architectures or a memory bank. In order to understand what enables the contrastive prediction tasks to learn useful representations, we systematically study the major components of our framework. We show that (1) composition of data augmentations plays a critical role in defining effective predictive tasks, (2) introducing a learnable nonlinear transformation between the representation and the contrastive loss substantially improves the quality of the learned representations, and (3) contrastive learning benefits from larger batch sizes and more training steps compared to supervised learning. By combining these findings, we are able to considerably outperform previous methods for self-supervised and semi-supervised learning on ImageNet. A linear classifier trained on self-supervised representations learned by SimCLR achieves 76.5% top-1 accuracy, which is a 7% relative improvement over previous state-of-the-art, matching the performance of a supervised ResNet-50. When fine-tuned on only 1% of the labels, we achieve 85.8% top-5 accuracy, outperforming AlexNet with 100X fewer labels.
CRVOS: Clue Refining Network for Video Object Segmentation
Abstract: The encoder-decoder based methods for semi-supervised video object segmentation (Semi-VOS) have received extensive attentions due to their superior performances. However, most of them have complex intermediate networks which generate strong specifiers, to be robust against challenging scenarios, and this is quite inefficient when dealing with relatively simple scenarios. To solve this problem, we propose a real-time Clue Refining Network for Video Object Segmentation (CRVOS) which does not have complex intermediate network. In this work, we propose a simple specifier, referred to as the Clue, which consists of the previous frame's coarse mask and coordinates information. We also propose a novel refine module which shows higher performance than general ones by using deconvolution layer instead of bilinear upsampling. Our proposed network, CRVOS, is the fastest method with the competitive performance. On DAVIS16 validation set, CRVOS achieves 61 FPS and J&F score of 81.6%.