In few-shot unsupervised domain adaptation (FS-UDA), most existing methods
followed the few-shot learning (FSL) methods to leverage the low-level local
features (learned from conventional convolutional models, e.g., ResNet) for
classification. However, the goal of FS-UDA and FSL are relevant yet distinct,
since FS-UDA aims to classify the samples in target domain rather than source
domain. We found that the local features are insufficient to FS-UDA, which
could introduce noise or bias against classification, and not be used to
effectively align the domains. To address the above issues, we aim to refine
the local features to be more discriminative and relevant to classification.
Thus, we propose a novel task-specific semantic feature learning method (TSECS)
for FS-UDA. TSECS learns high-level semantic features for image-to-class
similarity measurement. Based on the high-level features, we design a
cross-domain self-training strategy to leverage the few labeled samples in
source domain to build the classifier in target domain. In addition, we
minimize the KL divergence of the high-level feature distributions between
source and target domains to shorten the distance of the samples between the
two domains. Extensive experiments on DomainNet show that the proposed method
significantly outperforms SOTA methods in FS-UDA by a large margin (i.e., 10%).

本文提出了一种新的针对少样本无监督域自适应（FS-UDA）的任务特定语义特征学习方法（TSECS），以提高图像分类的效果，包括跨域自我训练策略和最小化源和目标域之间高级特征分布的 KL 散度。在 DomainNet 数据集上的实验结果表明，该方法在 FS-UDA 领域取得了明显的性能优势（即 10%）。

高层语义特征在少样本无监督领域适应中的重要性

High-level semantic feature matters few-shot unsupervised domain  adaptation

For unsupervised pretraining, mask-reconstruction pretraining (MRP)
approaches, e.g. MAE and data2vec, randomly mask input patches and then
reconstruct the pixels or semantic features of these masked patches via an
auto-encoder. Then for a downstream task, supervised fine-tuning the pretrained
encoder remarkably surpasses the conventional ``supervised learning'' (SL)
trained from scratch. However, it is still unclear 1) how MRP performs semantic
feature learning in the pretraining phase and 2) why it helps in downstream
tasks. To solve these problems, we first theoretically show that on an
auto-encoder of a two/one-layered convolution encoder/decoder, MRP can capture
all discriminative features of each potential semantic class in the pretraining
dataset. Then considering the fact that the pretraining dataset is of huge size
and high diversity and thus covers most features in downstream dataset, in
fine-tuning phase, the pretrained encoder can capture as much features as it
can in downstream datasets, and would not lost these features with theoretical
guarantees. In contrast, SL only randomly captures some features due to lottery
ticket hypothesis. So MRP provably achieves better performance than SL on the
classification tasks. Experimental results testify to our data assumptions and
also our theoretical implications.

该论文研究了在无监督预训练中，通过使用自编码器结合随机遮盖的方法，可以更好地提取语义特征，从而应用于监督微调，实现更好的性能表现。

探究口罩重建预训练为何对下游任务有所帮助

Towards Understanding Why Mask-Reconstruction Pretraining Helps in  Downstream Tasks

Zero-shot detection (ZSD) is crucial to large-scale object detection with the
aim of simultaneously localizing and recognizing unseen objects. There remain
several challenges for ZSD, including reducing the ambiguity between background
and unseen objects as well as improving the alignment between visual and
semantic concept. In this work, we propose a novel framework named Background
Learnable Cascade (BLC) to improve ZSD performance. The major contributions for
BLC are as follows: (i) we propose a multi-stage cascade structure named
Cascade Semantic R-CNN to progressively refine the alignment between visual and
semantic of ZSD; (ii) we develop the semantic information flow structure and
directly add it between each stage in Cascade Semantic RCNN to further improve
the semantic feature learning; (iii) we propose the background learnable region
proposal network (BLRPN) to learn an appropriate word vector for background
class and use this learned vector in Cascade Semantic R CNN, this design makes
\Background Learnable" and reduces the confusion between background and unseen
classes. Our extensive experiments show BLC obtains significantly performance
improvements for MS-COCO over state-of-the-art methods.

本篇论文提出一种称作 “背景可学习级联” 的框架，采用级联语义 R-CNN 和背景可学习区域建议网络（BLRPN）结构，以改善零样本目标检测的性能，实验结果表明，该方法在 MS-COCO 上显著优于现有的方法。