source dataset
Mixed Supervised Object Detection by Transferring Mask Prior and Semantic Similarity
Object detection has achieved promising success, but requires large-scale fullyannotated data, which is time-consuming and labor-extensive. Therefore, we consider object detection with mixed supervision, which learns novel object categories using weak annotations with the help of full annotations of existing base object categories. Previous works using mixed supervision mainly learn the classagnostic objectness from fully-annotated categories, which can be transferred to upgrade the weak annotations to pseudo full annotations for novel categories. In this paper, we further transfer mask prior and semantic similarity to bridge the gap between novel categories and base categories. Specifically, the ability of using mask prior to help detect objects is learned from base categories and transferred to novel categories. Moreover, the semantic similarity between objects learned from base categories is transferred to denoise the pseudo full annotations for novel categories. Experimental results on three benchmark datasets demonstrate the effectiveness of our method over existing methods.
ASelf Supervised Learning Methods
L.1 Source Dataset: ImageNet Table 13 and Table 14 describe 5-way 1-shot and 5-way 5-shot CD-FSL performance when ImageNet is used as the source dataset, respectively. Note that Table 14 is added for convenience and this is the same with Table 3 in the main paper.
A Datasheet for S
These sentence pairs will serve as a means to evaluate machines' commonsense reasoning abilities under different extra-linguistic contexts.
Chasing Fairness Under Distribution Shift: A Model Weight Perturbation Approach
Fairness in machine learning has attracted increasing attention in recent years.
A Closer Look at the CLS Token for Cross-Domain Few-Shot Learning
Vision Transformer (ViT) has shown great power in learning from large-scale datasets. However, collecting sufficient data for expert knowledge is always difficult. To handle this problem, Cross-Domain Few-Shot Learning (CDFSL) has been proposed to transfer the source-domain knowledge learned from sufficient data to target domains where only scarce data is available.
UDA
Cleaning missing values: The human-generated questions may be unanswerable. Thus, we remove the Q&A items that lack available answers. Additionally, documents lacking any valid Q&A pairs are also removed.
