Showing papers on "Object-class detection published in 2021"

New Generation Deep Learning for Video Object Detection: A Survey

Abstract: Video object detection, a basic task in the computer vision field, is rapidly evolving and widely used In recent years, deep learning methods have rapidly become widespread in the field of video object detection, achieving excellent results compared with those of traditional methods However, the presence of duplicate information and abundant spatiotemporal information in video data poses a serious challenge to video object detection Therefore, in recent years, many scholars have investigated deep learning detection algorithms in the context of video data and have achieved remarkable results Considering the wide range of applications, a comprehensive review of the research related to video object detection is both a necessary and challenging task This survey attempts to link and systematize the latest cutting-edge research on video object detection with the goal of classifying and analyzing video detection algorithms based on specific representative models The differences and connections between video object detection and similar tasks are systematically demonstrated, and the evaluation metrics and video detection performance of nearly 40 models on two data sets are presented Finally, the various applications and challenges facing video object detection are discussed

Recurrent Exposure Generation for Low-Light Face Detection

Abstract: Face detection from low-light images is challenging due to limited photons and inevitable noise, which, to make the task even harder, are often spatially unevenly distributed. A natural solution is to borrow the idea from multi-exposure, which captures multiple shots to obtain well-exposed images under challenging conditions. High-quality implementation/approximation of multi-exposure from a single image is however nontrivial. Fortunately, as shown in this paper, neither is such high-quality necessary since our task is face detection rather than image enhancement. Specifically, we propose a novel Recurrent Exposure Generation (REG) module and couple it seamlessly with a Multi-Exposure Detection (MED) module, and thus significantly improve face detection performance by effectively inhibiting non-uniform illumination and noise issues. REG produces progressively and efficiently intermediate images corresponding to various exposure settings, and such pseudo-exposures are then fused by MED to detect faces across different lighting conditions. The proposed method, named REGDet, is the first ‘detection-with-enhancement’ framework for low-light face detection. It not only encourages rich interaction and feature fusion across different illumination levels, but also enables effective end-to-end learning of the REG component to be better tailored for face detection. Moreover, as clearly shown in our experiments, REG can be flexibly coupled with different face detectors without extra low/normal-light image pairs for training. We tested REGDet on the DARK FACE low-light face benchmark with thorough ablation study, where REGDet outperforms previous state-of-the-arts by a significant margin, with only negligible extra parameters.

A Survey of Self-Supervised and Few-Shot Object Detection

Abstract: Labeling data is often expensive and time-consuming, especially for tasks such as object detection and instance segmentation, which require dense labeling of the image. While few-shot object detection is about training a model on novel (unseen) object classes with little data, it still requires prior training on many labeled examples of base (seen) classes. On the other hand, self-supervised methods aim at learning representations from unlabeled data which transfer well to downstream tasks such as object detection. Combining few-shot and self-supervised object detection is a promising research direction. In this survey, we review and characterize the most recent approaches on few-shot and self-supervised object detection. Then, we give our main takeaways and discuss future research directions.

Utilizing multiple object detection models to automatically select user-requested objects in images

Abstract: The present disclosure relates to an object selection system that accurately detects and automatically selects user-requested objects (e.g., query objects) in a digital image. For example, the object selection system builds and utilizes an object selection pipeline to determine which object detection neural network to utilize to detect a query object based on analysing the object class of the query object. In addition, the object selection system can add, update, or replace portions of the object selection pipeline to improve overall accuracy and efficiency of automatic object selection within an image. The application identifies from a selection query an object to be selected in a digital image then selects, based on an analysis of the query object, a first object detection neural network from a plurality of object detection neural networks such a plurality comprising a specialist object detection neural network, a concept-based object detection neural network, a known object class detection neural network and an unknown object class detection neural network. The selected neural network then detects the query object in the image from which a first object mask of the query object is generated using an object mask neural network and the image with object mask is provided in response to the selection query.