We study on weakly-supervised object detection (WSOD) which plays a vital
role in relieving human involvement from object-level annotations. Predominant
works integrate region proposal mechanisms with convolutional neural networks
(CNN). Although CNN is proficient in extracting discriminative local features,
grand challenges still exist to measure the likelihood of a bounding box
containing a complete object (i.e., "objectness"). In this paper, we propose a
novel WSOD framework with Objectness Distillation (i.e., WSOD^2) by designing a
tailored training mechanism for weakly-supervised object detection. Multiple
regression targets are specifically determined by jointly considering bottom-up
(BU) and top-down (TD) objectness from low-level measurement and CNN
confidences with an adaptive linear combination. As bounding box regression can
facilitate a region proposal learning to approach its regression target with
high objectness during training, deep objectness representation learned from
bottom-up evidences can be gradually distilled into CNN by optimization. We
explore different adaptive training curves for BU/TD objectness, and show that
the proposed WSOD^2 can achieve state-of-the-art results.

本文介绍一种新型的弱监督目标检测框架 WSOD^2，该框架通过联合考虑低层测量，CNN 置信度和自适应线性组合来确定多个回归目标，并通过区域提议学习来逼近回归目标，最终取得了最先进的结果。

WSOD^2：学习自下而上和自上而下的目标重要性提取技术用于弱监督目标检测

WSOD^2: Learning Bottom-up and Top-down Objectness Distillation for  Weakly-supervised Object Detection

Region proposal mechanisms are essential for existing deep learning
approaches to object detection in images. Although they can generally achieve a
good detection performance under normal circumstances, their recall in a scene
with extreme cases is unacceptably low. This is mainly because bounding box
annotations contain much environment noise information, and non-maximum
suppression (NMS) is required to select target boxes. Therefore, in this paper,
we propose the first anchor-free and NMS-free object detection model called
weakly supervised multimodal annotation segmentation (WSMA-Seg), which utilizes
segmentation models to achieve an accurate and robust object detection without
NMS. In WSMA-Seg, multimodal annotations are proposed to achieve an
instance-aware segmentation using weakly supervised bounding boxes; we also
develop a run-data-based following algorithm to trace contours of objects. In
addition, we propose a multi-scale pooling segmentation (MSP-Seg) as the
underlying segmentation model of WSMA-Seg to achieve a more accurate
segmentation and to enhance the detection accuracy of WSMA-Seg. Experimental
results on multiple datasets show that the proposed WSMA-Seg approach
outperforms the state-of-the-art detectors.

本文提出了第一个无 anchor 和 NMS 的目标检测模型 WSMA-Seg，利用分割模型实现精确和稳健的目标检测，WSMA-Seg 使用多模态注释，开发了一种基于运行数据的跟踪算法来追踪物体轮廓，并提出了多尺度池化分割模型 (MSP-Seg)，实验结果表明该方法优于最先进的检测器。