Instance-level Annotations Research Articles

WSODPB: Weakly supervised object detection with PCSNet and box regression module

Weakly supervised object detection (WSOD) task uses only image-level annotations to train object detection task. WSOD does not require time-consuming instance-level annotations, which attracts more and more attention. Previous weakly supervised object detection methods iteratively update detectors and pseudo-labels or use rules-based methods, which could not generate complete and accurate proposals. We utilize the features extracted by the convolutional layers to optimize the proposals generated by rules-based methods, and solve the above problem through combining the two different features. Then, a box regression module is added to the weakly supervised object detection network, which supervised by a proposal completeness scoring network (PCSNet). The box regression module modifies proposals to obtain higher intersection-over-Union (IoU) with ground truth. PCSNet scores the proposal output from the box regression network and utilizes the score to improve the box regression module. In addition, we take advantage of the random proposal scoring (RPS) algorithm for generating more accurate pseudo labels to train the box regression module. The results show that our methods have made significant improvements on PASCAL VOC 2007 and 2012 datasets.

A Two-Branch Network for Weakly Supervised Object Localization

Weakly supervised object localization (WSOL) has attracted intense interest in computer vision for instance level annotations. As a hot research topic, a number of existing works concentrated on utilizing convolutional neural network (CNN)-based methods, which are powerful in extracting and representing features. The main challenge in CNN-based WSOL methods is to obtain features covering the entire target objects, not only the most discriminative object parts. To overcome this challenge and to improve the detection performance of feature extracting related WSOL methods, a CNN-based two-branch model was presented in this paper to locate objects using supervised learning. Our method contained two branches, including a detection branch and a self-attention branch. During the training process, the two branches interacted with each other by regarding the segmentation mask from the other branch as the pseudo ground truth labels of itself. Our model was able to focus on capturing the information of all the object parts due to the self-attention mechanism. Additionally, we embedded multi-scale detection into our two-branch method to output two-scale features. We evaluated our two-branch network on the CUB-200-2011 and VOC2007 datasets. The pointing localization, intersection over union (IoU) localization, and correct localization precision (CorLoc) results demonstrated competitive performance with other state-of-the-art methods in WSOL.