Instance-Aware, Context-Focused, and Memory-Efficient Weakly Supervised Object Detection

Ren, Zhongzheng; Yu, Zhiding; Yang, Xiaodong; Li, Mingyu; Lee, Yong Jae; Schwing, Alexander G.; Kautz, Jan

doi:10.1109/cvpr42600.2020.01061

Cited by 165 publications

(140 citation statements)

References 43 publications

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“…Lin et al [28] proposed object instance mining algorithm that can help detect more possible objects. [13], [29] and [14] proposed to combine the MIL branch with a single or multiple online regression branch to achieve relocalization of proposals. These methods are all based on a multiple instance detection network, so it is hard to avoid the non-convex optimization problem brought by MIL.…”

Section: B Weakly Supervised Object Detectionmentioning

confidence: 99%

“…A common procedure adpoted in WSOD methods is to train a fully-supervised obejct detector using annotations generated from the WSOD detection results. Inspired by this procedure, some works [12]- [14] try to introduce a regression branch to the MIDN directly, where the annotations are mined in various ways. In the approach proposed by Zeng et al [12], pseudo annotations are mined based on low-level image features.…”

Section: Introductionmentioning

confidence: 99%

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Efficient Weakly-Supervised Object Detection With Pseudo Annotations

et al. 2021

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Weakly-supervised object detection (WSOD) has attracted lots of attention in recent years. However, there is still a big gap between WSOD and generic object detection. The main barriers to the efficiency of WSOD are the ineffective data augmentations and inaccurate bounding box predictions. Given only the image-level annotations, it's hard for WSOD to effectively utilize variant data augmentations and accurately regress the bounding boxes. Although a fully-supervised object detector can be trained using annotations generated from the weakly-supervised obejct detector, the performance is still severely limited due to the low quality of mined pseudo annotations. This paper proposes an efficient WSOD method with pseudo annotations (EWPA) to make better use of imperfect annotations. With the assistance of pseudo annotations, EWPA can effectively regress more accurate bounding boxes while the traditional WSOD can only locate the salient parts of an object. Furthermore, pseudo annotations can help design more complex data augmentations to drive the network learning more discriminative feature representations. Extensive experiments are conducted on PASCAL VOC 2007 and 2012 datasets and validate the effectiveness of EWPA.

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Section: B Weakly Supervised Object Detectionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Efficient Weakly-Supervised Object Detection With Pseudo Annotations

et al. 2021

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“…The contribution of different anchors to the network loss was specified, which makes the model tend to choose those samples with higher cleanliness score. Ren et al [13] developed a unified framework that supports examples and contexts. Weakly supervised learning has become a compelling object detection tool by reducing the need for strong supervision during training.…”

Section: Related Workmentioning

confidence: 99%

Deep Learning-Based Object Detection Improvement for Fine-Grained Birds

Yang

Song

2021

IEEE Access

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When the object detection algorithm is applied to the bird protection project, there are many problems like large model parameters, high similarity between bird species and single sample scene. In order to further improve the detection accuracy and stability of the object detection model, a multi-object detection algorithm for fine-grained birds is proposed. Firstly, the algorithm introduces Depthwise separable convolution into the feature extraction layer of YOLOv3 algorithm. The convolution process is divided into two parts: deep convolution and point-by-point convolution. The separation between intra-channel convolution and inter-channel convolution is realized. On the basis of high detection accuracy, the number of algorithm model parameters and calculation amount are greatly reduced. Finally, Focal loss was added to the loss function to solve the serious imbalance of positive and negative samples. By reducing the weight of the large number of simple background classes, the algorithm was more focused on detecting foreground classes. The experimental results show that, in the bird data set, the average precision mean (mAP) index of this algorithm is 2.71% higher than YOLOv3 algorithm, the number of parameters is 79.88% lower than YOLOv3 basic model, and the number of frames per second (FPS) is 19.98% higher than YOLOv3 algorithm. This algorithm not only greatly reduces the number of model parameters and computation, but also improves the detection speed and mAP.

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“…They extracted region correspondence maps to use as pseudo-target regions for training the agent. A teacherstudent learning approach through multiple instance selftraining is used by Ren et al [29]. They trained the student network with the pseudo-labels from teacher network.…”

Section: Related Workmentioning

confidence: 99%

“…These methods [27], [28], [29] have achieved suitable performance, however, these approaches still have the problems of missing detection in case of occluded objects and wrong detections due to objects cluster. Since the training image is decomposed into thousands of proposals, and each approximately correct training instance is flooded with many incorrect training instances.…”

Section: Related Workmentioning

confidence: 99%

A Robust Context-Aware Proposal Refinement Method for Weakly Supervised Object Detection

Awan

Shin

2020

IEEE Access

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Supervised object detection models require fully annotated data for training the network. However, labeling large datasets is a very time-consuming task, therefore, weakly supervised object detection (WSOD) is a substitute approach to fully supervised learning for the object detection task. Many methods have been proposed for WSOD to date, their performance is still lower than supervised approaches since WSOD is a very challenging task. The major problem with existing WSOD methods is partial object detection and false detection in an objects cluster with the same category. The majority of the methods on WSOD follow multiple instance learning approaches, which does not guarantee the completeness of detected objects. To address these issues, we propose a three-fold refinement strategy to proposals to learn complete instances. We generate class-specific localization maps by fused class activation maps obtained from fused complementary classification networks. These localization maps are used to amend the detected proposals from the instance classification branch (detection network). Deep reinforcement learning networks are proposed to learn decisive-agent and rectifying-agent based on policy gradient algorithm to further refine the proposals. The refined bounding boxes are then fed to instance classification network. The refinement operations result in learning complete objects and greatly improve detection performance. Experimental results show better detection performance by the proposed WSOD method compared to the state-of-the-art methods on PASCAL VOC2007 and VOC2012 benchmarks.INDEX TERMS Weakly supervised object detection, complementary learning, discriminative features, proposal refinement, class activation maps, reinforcement learning, and deep learning.

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Instance-Aware, Context-Focused, and Memory-Efficient Weakly Supervised Object Detection

Cited by 165 publications

References 43 publications

Efficient Weakly-Supervised Object Detection With Pseudo Annotations

Efficient Weakly-Supervised Object Detection With Pseudo Annotations

Deep Learning-Based Object Detection Improvement for Fine-Grained Birds

A Robust Context-Aware Proposal Refinement Method for Weakly Supervised Object Detection

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