U-Nets have been tremendously successful in many imaging inverse problems. In an effort to understand the source of this success, we show that one can reduce a U-Net to a tractable, well-understood sparsity-driven dictionary model while retaining its strong empirical performance… We achieve this by extracting a certain multiscale convolutional dictionary from the standard U-Net. This dictionary imitates the structure of the U-Net in its convolution, scale-separation, and skip connection aspects, while doing away with the nonlinear parts. We show […]

TASK DATASET MODEL METRIC NAME METRIC VALUE GLOBAL RANK REMOVE Object Detection COCO minival Sparse R-CNN (ResNet-50, FPN) box AP 42.3 # 31 Object Detection COCO minival Sparse R-CNN (ResNet-50, FPN) AP50 61.2 # 27 Object Detection COCO minival Sparse R-CNN (ResNet-50, FPN) AP75 45.7 # 23 Object Detection COCO minival  

Few-shot learning (FSL) approaches are usually based on an assumption that the pre-trained knowledge can be obtained from base (seen) categories and can be well transferred to novel (unseen) categories. However, there is no guarantee, especially for the latter part… This issue leads to the unknown nature of the inference process in most FSL methods, which hampers its application in some risk-sensitive areas. In this paper, we reveal a new way to perform FSL for image classification, using visual representations […]

In real-world classification tasks, each class often comprises multiple finer-grained “subclasses.” As the subclass labels are frequently unavailable, models trained using only the coarser-grained class labels often exhibit highly variable performance across different subclasses… This phenomenon, known as hidden stratification, has important consequences for models deployed in safety-critical applications such as medicine. We propose GEORGE, a method to both measure and mitigate hidden stratification even when subclass labels are unknown. We first observe that unlabeled subclasses are often separable in […]

TASK DATASET MODEL METRIC NAME METRIC VALUE GLOBAL RANK REMOVE Object Detection COCO test-dev GFLV2 (ResNet-50) box AP 44.3 # 45 Object Detection COCO test-dev GFLV2 (ResNet-50) AP50 62.3 # 54 Object Detection COCO test-dev GFLV2 (ResNet-50) AP75 48.5 # 47 Object Detection COCO test-dev GFLV2 (ResNet-50) APS 26.8  

This paper presents a comprehensive framework for studying methods of pulse rate estimation relying on remote photoplethysmography (rPPG). There has been a remarkable development of rPPG techniques in recent years, and the publication of several surveys too, yet a sound assessment of their performance has been overlooked at best, whether not undeveloped… The methodological rationale behind the framework we propose is that in order to study, develop and compare new rPPG methods in a principled and reproducible way, the following […]