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Return of Frustratingly Easy Domain Adaptation

This work proposes a simple, effective, and efficient method for unsupervised domain adaptation called CORrelation ALignment (CORAL), which minimizes domain shift by aligning the second-order statistics of source and target distributions, without requiring any target labels.
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Deep Joint Rain Detection and Removal from a Single Image

A recurrent rain detection and removal network that removes rain streaks and clears up the rain accumulation iteratively and progressively is proposed and a new contextualized dilated network is developed to exploit regional contextual information and to produce better representations for rain detection.
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Decoupling Representation and Classifier for Long-Tailed Recognition

It is shown that it is possible to outperform carefully designed losses, sampling strategies, even complex modules with memory, by using a straightforward approach that decouples representation and classification.
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Do We Really Need to Access the Source Data? Source Hypothesis Transfer for Unsupervised Domain Adaptation

This work proposes a simple yet generic representation learning framework, named SHOT, which freezes the classifier module (hypothesis) of the source model and learns the target-specific feature extraction module by exploiting both information maximization and self-supervised pseudo-labeling to implicitly align representations from the target domains to the source hypothesis.
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Tokens-to-Token ViT: Training Vision Transformers from Scratch on ImageNet

A new Tokens-To-Token Vision Transformer (T2T-VTT), which incorporates an efficient backbone with a deep-narrow structure for vision transformer motivated by CNN architecture design after empirical study and reduces the parameter count and MACs of vanilla ViT by half.
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PANet: Few-Shot Image Semantic Segmentation With Prototype Alignment

This paper tackles the challenging few-shot segmentation problem from a metric learning perspective and presents PANet, a novel prototype alignment network to better utilize the information of the support set to better generalize to unseen object categories.
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Few-Shot Object Detection via Feature Reweighting

This work develops a few-shot object detector that can learn to detect novel objects from only a few annotated examples, using a meta feature learner and a reweighting module within a one-stage detection architecture.
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Tensor Robust Principal Component Analysis with a New Tensor Nuclear Norm

This paper considers the Tensor Robust Principal Component Analysis (TRPCA) problem, which aims to exactly recover the low-rank and sparse components from their sum, and proposes a model based on the recently proposed tensor-tensor product, which includes matrix RPCA as a special case.
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Dual Path Networks

This work reveals the equivalence of the state-of-the-art Residual Network (ResNet) and Densely Convolutional Network (DenseNet) within the HORNN framework, and finds that ResNet enables feature re-usage while DenseNet enables new features exploration which are both important for learning good representations.
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Tensor Robust Principal Component Analysis: Exact Recovery of Corrupted Low-Rank Tensors via Convex Optimization

This work proves that under certain suitable assumptions, it can recover both the low-rank and the sparse components exactly by simply solving a convex program whose objective is a weighted combination of the tensor nuclear norm and the l1-norm.
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