SLV: Spatial Likelihood Voting for Weakly Supervised Object Detection

Abstract

Based on the framework of multiple instance learning (MIL), tremendous works have promoted the advances of weakly supervised object detection (WSOD). However, most MIL-based methods tend to localize instances to their discriminative parts instead of the whole content. In this paper, we propose a spatial likelihood voting (SLV) module to converge the proposal localizing process without any bounding box annotations. Specifically, all region proposals in a given image play the role of voters every iteration during training, voting for the likelihood of each category in spatial dimensions. After dilating alignment on the area with large likelihood values, the voting results are regularized as bounding boxes, being used for the final classification and localization. Based on SLV, we further propose an end-toend training framework for multi-task learning. The classification and localization tasks promote each other, which further improves the detection performance. Extensive experiments on the PASCAL VOC 2007 and 2012 datasets demonstrate the superior performance of SLV.

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

“…Using low activation values in phase II refinement can cause ambiguity and may [29] has the highest mAP (54.9%), however, our method (MIC+PI+PII) has an enormous improvement with 6.7% mAP to [29]. From Table 3, Chen et al [28] have the highest mean Corloc 71.0% among all other compared methods. In comparison to [28], the proposed method has a significantly high score with 6.0% gain in mean CorLoc.…”

“…From Table 3, Chen et al [28] have the highest mean Corloc 71.0% among all other compared methods. In comparison to [28], the proposed method has a significantly high score with 6.0% gain in mean CorLoc. Table 4 and Table 5 illustrate the results by proposed and compared methods on PASCAL VOC2012 test and trainval sets in terms of mAP and CorLoc, respectively.…”

“…They trained each task with the supervision of the accompanying task. Lately, spatial likelihood voting method is proposed by Chen et al [28] to converge proposal localization for object detection with image-level supervision using multi-task learning. Recently, Zhang et al [14] proposed region-searching paradigm for WSOD with reinforcement learning approach under weak supervisions.…”

“…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.…”

Efficient Weakly-Supervised Object Detection With Pseudo Annotations

“…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.…”