We introduce a novel single-shot object detector to ease the imbalance of foreground-background class by suppressing the easy negatives while increasing the positives. To achieve this, we propose an Anchor Promotion Module (APM) which predicts the probability of each anchor as positive and adjusts their initial locations and shapes to promote both the quality and quantity of positive anchors. In addition, we design an efficient Feature Alignment Module (FAM) to extract aligned features for fitting the promoted anchors with the help of both the location and shape transformation information from the APM. We assemble the two proposed modules to the backbone of VGG-16 and ResNet-101 network with an encoder-decoder architecture. Extensive experiments on MS COCO well demonstrate our model performs competitively with alternative methods (40.0\% mAP on \textit{test-dev} set) and runs faster (28.6 \textit{fps}).
Huazhong University of Science and Technology（华中科技大学）
For most of the anchor-based detectors, Intersection over Union(IoU) is widely utilized to assign targets for the anchors during training. However, IoU pays insufficient attention to the closeness of the anchor's center to the truth box's center. This results in two problems: (1) only one anchor is assigned to most of the slender objects which leads to insufficient supervision information for the slender objects during training and the performance on the slender objects is hurt; (2) IoU can not accurately represent the alignment degree between the receptive field of the feature at the anchor's…
Most existing object detectors suffer from class imbalance problems that hinder balanced performance. In particular, anchor free object detectors have to solve the background imbalance problem due to detection in a per-pixel prediction fashion as well as foreground imbalance problem simultaneously. In this work, we propose Balance-oriented focal loss that can induce balanced learning by considering both background and foreground balance comprehensively. This work aims to address imbalance problem in the situation of using a general unbalanced data of non-extreme distribution not including few …
Real-time single-stage object detectors based on deep learning still remain less accurate than more complex ones. The trade-off between model performance and computational speed is a major challenge. In this paper, we propose a new way to efficiently learn a single-shot detector which offers a very good compromise between these two objectives. To this end, we introduce LapNet, an anchor based detector, trained end-to-end without any sampling strategy. Our approach aims to overcome two important problems encountered in training an anchor based detector: (1) ambiguity in the assignment of anchor…
Balanced Learning for Domain Adaptive Semantic Segmentation
MoE Key Laboratory of Brain-inspired Intelligent Perception and Cognition（脑启发智能感知与认知关键实验室）；University of Science and Technology of China（中国科学技术大学）；Deep Space Exploration Laboratory（深空探测实验室）
Detecting tiny objects is one of the main obstacles hindering the development of object detection. The performance of generic object detectors tends to drastically deteriorate on tiny object detection tasks. In this paper, we point out that either box prior in the anchor-based detector or point prior in the anchor-free detector is sub-optimal for tiny objects. Our key observation is that the current anchor-based or anchor-free label assignment paradigms will incur many outlier tiny-sized ground truth samples, leading to detectors imposing less focus on the tiny objects. To this end, we propose…
Object detection is a typical multi-task learning application, which optimizes classification and regression simultaneously. However, classification loss always dominates the multi-task loss in anchor-based methods, hampering the consistent and balanced optimization of the tasks. In this paper, we find that shifting the bounding boxes can change the division of positive and negative samples in classification, meaning classification depends on regression. Moreover, we summarize three important conclusions about fine-tuning loss weights, considering different datasets, optimizers and regression …
Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；Baidu, Inc.（百度公司）
With the rapid development of deep convolutional neural network, face detection has made great progress in recent years. WIDER FACE dataset, as a main benchmark, contributes greatly to this area. A large amount of methods have been put forward where PyramidBox designs an effective data augmentation strategy (Data-anchor-sampling) and context-based module for face detector. In this report, we improve each part to further boost the performance, including Balanced-data-anchor-sampling, Dual-PyramidAnchors and Dense Context Module. Specifically, Balanced-data-anchor-sampling obtains more uniform s…
Contextrast++: Robust Multi-Scale Contextual Contrastive Learning for Semantic Segmentation
Information & Electronics Research Institute, KAIST（韩国科学技术院信息与电子研究院）；Zoox Inc.（Zoox公司）；KAIST (Korea Advanced Institute of Science and Technology)（韩国科学技术院）；Hanwha Aerospace（韩华宇航）；School of Electrical Engineering, KAIST（韩国科学技术院电气工程学院）
University of Chinese Academy of Sciences（中国科学院大学）；Institute of North Electronic Equipment（北方电子设备研究所）；Picsart AI Research（Picsart人工智能研究院）
Object detection using single point supervision has received increasing attention over the years. However, the performance gap between point supervised object detection (PSOD) and bounding box supervised detection remains large. In this paper, we attribute such a large performance gap to the failure of generating high-quality proposal bags which are crucial for multiple instance learning (MIL). To address this problem, we introduce a lightweight alternative to the off-the-shelf proposal (OTSP) method and thereby create the Point-to-Box Network (P2BNet), which can construct an inter-objects bal…
Decoupling Wavelet Sub-bands for Single Source Domain Generalization in Fundus Image Segmentation
Indian Statistical Institute（印度统计研究所）；University of Oxford（牛津大学）；Radcliffe Department of Medicine, University of Oxford（牛津大学拉德克利夫医学系）；Department of Engineering Science, University of Oxford（牛津大学工程科学系）
Enhancing Object Detection Performance for Small Objects through Synthetic Data Generation and Proportional Class-Balancing Technique: A Comparative Study in Industrial Scenarios
German Research Center for Artificial Intelligence (DFKI)（德国人工智能研究中心（DFKI））；Rheinland-Pfälzische Technische Universität (RPTU) Kaiserslautern-Landau（莱茵兰-普法尔茨凯泽斯劳滕-兰道工业大学（RPTU））
