We present Convolutional Oriented Boundaries (COB), which produces multiscale oriented contours and region hierarchies starting from generic image classification Convolutional Neural Networks (CNNs). ...COB is computationally efficient, because it requires a single CNN forward pass for multi-scale contour detection and it uses a novel sparse boundary representation for hierarchical segmentation; it gives a significant leap in performance over the state-of-the-art, and it generalizes very well to unseen categories and datasets. Particularly, we show that learning to estimate not only contour strength but also orientation provides more accurate results. We perform extensive experiments for low-level applications on BSDS, PASCAL Context, PASCAL Segmentation, and NYUD to evaluate boundary detection performance, showing that COB provides state-of-the-art contours and region hierarchies in all datasets. We also evaluate COB on high-level tasks when coupled with multiple pipelines for object proposals, semantic contours, semantic segmentation, and object detection on MS-COCO, SBD, and PASCAL; showing that COB also improves the results for all tasks.
This study examines the effect of shareholder proposals related to corporate social responsibility (CSR) on financial performance. Specifically, I focus on CSR proposals that pass or fail by a small ...margin of votes. The passage of such “close call” proposals is akin to a random assignment of CSR to companies and hence provides a quasi-experiment to study the effect of CSR on performance. I find that the adoption of close call CSR proposals leads to positive announcement returns and superior accounting performance, implying that these proposals are value enhancing. When I examine the channels through which companies benefit from CSR, I find that labor productivity and sales growth increase after the vote. Finally, I document that close call CSR proposals differ from non-close proposals along several dimensions. Accordingly, although my results imply that adopting close call CSR proposals is beneficial to companies, they do not necessarily imply that CSR proposals are beneficial in general.
Data, as supplemental material, are available at
http://dx.doi.org/10.1287/mnsc.2014.2038
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This paper was accepted by Wei Jiang, finance
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People normally watch 360° videos through a head-mounted display, inside which only the content of viewports can be seen. Therefore, viewport proposal, referring to detecting potential viewport ...candidates, plays an important role in many 360° video processing tasks. In this paper, we advance the viewport proposal by further aligning the predicted viewports across frames for individual subject. This provides a better methodology and a deeper perspective to learn the human perceptual behaviours on 360° videos. Specifically, we first analyze three 360° video datasets and obtain several findings on human consistency, objectness and motion of viewports. Inspired by these findings, we propose a bi-directional transformer approach, named BiT, for 360° video viewport proposal and alignment. Specifically, BiT is composed of a multi-level residual module, a bi-directional encoder-decoder module and a spherical matching module. This way, the viewports can be well proposed and aligned via considering multi-level, bi-directional and non-local information. Moreover, the aligned viewports by BiT are used to refine the viewports and improve viewport proposal accuracy in return. Finally, we validate that our BiT approach is superior on viewport proposal, compared with the state-of-the-art approaches. Besides, the aligned viewports from BiT is verified to be effective in multiple applications, such as saliency prediction, trajectory prediction and perceptual video compression.
The automatic defects detection for solar cell electroluminescence (EL) images is a challenging task, due to the similarity of defect features and complex background features. To address this ...problem, in this article a novel complementary attention network (CAN) is designed by connecting the novel channel-wise attention subnetwork with spatial attention subnetwork sequentially, which adaptively suppresses the background noise features and highlights the defect features simultaneously by employing the complementary advantage of the channel features and spatial position features. In CAN, the novel channel-wise attention subnetwork applies convolution operation to integrate the concatenated and discriminative output features extracted by global average pooling layer and global max pooling layer, which can make fully use of these informative features. Furthermore, a region proposal attention network (RPAN) is proposed by embedding CAN into region proposal network in faster R-CNN (convolution neutral network) to extract more refined defective region proposals, which is used to construct a novel end-to-end faster RPAN-CNN framework for detecting defects in raw EL image. Finally, some experimental results on a large-scale EL dataset including 3629 images, 2129 of which are defective, show that the proposed method performs much better than other methods in terms of defects classification and detection results in raw solar cell EL images.
Arbitrary-oriented object detection (AOOD) is a challenging task to detect objects in the wild with arbitrary orientations and cluttered arrangements. Existing approaches are mainly based on ...anchor-based boxes or dense points, which rely on complicated hand-designed processing steps and inductive bias, such as anchor generation, transformation, and non-maximum suppression reasoning. Recently, the emerging transformer-based approaches view object detection as a direct set prediction problem that effectively removes the need for hand-designed components and inductive biases. In this paper, we propose an Arbitrary-Oriented Object DEtection TRansformer framework, termed AO2-DETR, which comprises three dedicated components. More precisely, an oriented proposal generation mechanism is proposed to explicitly generate oriented proposals, which provides better positional priors for pooling features to modulate the cross-attention in the transformer decoder. An adaptive oriented proposal refinement module is introduced to extract rotation-invariant region features and eliminate the misalignment between region features and objects. And a rotation-aware set matching loss is used to ensure the one-to-one matching process for direct set prediction without duplicate predictions. Our method considerably simplifies the overall pipeline and presents a new AOOD paradigm. Comprehensive experiments on several challenging datasets show that our method achieves superior performance on the AOOD task.
This paper tackles the supervised evaluation of image segmentation and object proposal algorithms. It surveys, structures, and deduplicates the measures used to compare both segmentation results and ...object proposals with a ground truth database; and proposes a new measure: the precision-recall for objects and parts. To compare the quality of these measures, eight state-of-the-art object proposal techniques are analyzed and two quantitative meta-measures involving nine state of the art segmentation methods are presented. The meta-measures consist in assuming some plausible hypotheses about the results and assessing how well each measure reflects these hypotheses. As a conclusion of the performed experiments, this paper proposes the tandem of precision-recall curves for boundaries and for objects-and-parts as the tool of choice for the supervised evaluation of image segmentation. We make the datasets and code of all the measures publicly available.
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