Rediscovery of the Effectiveness of Standard Convolution for Lightweight Face Detection

This paper analyses the design choices of face detection architecture that improve efficiency between computation cost and accuracy. Specifically, we re-examine the effectiveness of the standard convolutional block as a lightweight backbone architecture on face detection. Unlike the current tendency of lightweight architecture design, which heavily utilizes depthwise separable convolution layers, we show that heavily channel-pruned standard convolution layer can achieve better accuracy and inference speed when using a similar parameter size. This observation is supported by the analyses concerning the characteristics of the target data domain, face. Based on our observation, we propose to employ ResNet with a highly reduced channel, which surprisingly allows high efficiency compared to other mobile-friendly networks (e.g., MobileNet-V1,-V2,-V3). From the extensive experiments, we show that the proposed backbone can replace that of the state-of-the-art face detector with a faster inference speed. Also, we further propose a new feature aggregation method maximizing the detection performance. Our proposed detector EResFD obtained 80.4% mAP on WIDER FACE Hard subset which only takes 37.7 ms for VGA image inference in on CPU. Code will be available at https://github.com/clovaai/EResFD

The Devil is in the Points: Weakly Semi-Supervised Instance Segmentation via Point-Guided Mask Representation

In this paper, we introduce a novel learning scheme named weakly semi-supervised instance segmentation (WSSIS) with point labels for budget-efficient and high-performance instance segmentation. Namely, we consider a dataset setting consisting of a few fully-labeled images and a lot of point-labeled images. Motivated by the main challenge of semi-supervised approaches mainly derives from the trade-off between false-negative and false-positive instance proposals, we propose a method for WSSIS that can effectively leverage the budget-friendly point labels as a powerful weak supervision source to resolve the challenge. Furthermore, to deal with the hard case where the amount of fully-labeled data is extremely limited, we propose a MaskRefineNet that refines noise in rough masks. We conduct extensive experiments on COCO and BDD100K datasets, and the proposed method achieves promising results comparable to those of the fully-supervised model, even with 50% of the fully labeled COCO data (38.8% vs. 39.7%). Moreover, when using as little as 5% of fully labeled COCO data, our method shows significantly superior performance over the state-of-the-art semi-supervised learning method (33.7% vs. 24.9%). The code is available at https://github.com/clovaai/PointWSSIS.Comment: CVPR 202

Observations on K-Image Expansion of Image-Mixing Augmentation

Image-mixing augmentations (e.g., Mixup and CutMix), which typically involve mix ing two images, have become the de-facto training techniques for image classification. Despite their huge success in image classification, the number of images to be mix ed has not been elucidated in the literature: only the naive K-image expansion has been shown to lead to performance degradation. This study derives a new K-image mixing augmentation based on the stick-breaking process under Dirichlet prior distribution. We demonstrate the superiority of our K-image expansion augmentation over conventional two-image mixing augmentation methods through extensive experiments and analyses: 1) more robust and generalized classifiers; 2) a more desirable loss landscape shape; 3) better adversarial robustness. Moreover, we show that our probabilistic model can measure the sample-wise uncertainty and boost the efficiency for network architecture search by achieving a 7-fold reduction in the search time. Code will be available at https://github.com/yjyoo3312/DCutMix-PyTorch.git

Alternative Patterning Process for Realization of Large-Area, Full-Color, Active Quantum Dot Display

Although various colloidal quantum dot (QD) coating and patterning techniques have been developed to meet the demands in optoelectronic applications over the past years, each of the previously demonstrated methods has one or more limitations and trade-offs in forming multicolor, high-resolution, or large-area patterns of QDs. In this study, we present an alternative QD patterning technique using conventional photolithography combined with charge-assisted layer-by-layer (LbL) assembly to solve the trade-offs of the traditional patterning processes. From our demonstrations, we show repeatable QD patterning process that allows multicolor QD patterns in both large-area and microscale. Also, we show that the QD patterns are robust against additional photolithography processes and that the thickness of the QD patterns can be controlled at each position. To validate that this process can be applied to actual device applications as an active material, we have fabricated inverted, differently colored, active QD light-emitting device (QD-LED) on a pixelated substrate, which achieved maximum electroluminescence intensity of 23 770 cd/m², and discussed the results. From our findings, we believe that our process provides a solution to achieving both high-resolution and large-scale QD pattern applicable to not only display, but also to practical photonic device research and development