Weakly supervised instance segmentation with imagelevel labels, instead of expensive pixel-level masks, remains unexplored. In this paper, we tackle this challenging problem by exploiting class peak responses to enable a classification network for instance mask extraction. With image labels supervision only, CNN classifiers in a fully convolutional manner can produce class response maps, which specify classification confidence at each image location. We observed that local maximums, i.e., peaks, in a class response map typically correspond to strong visual cues residing inside each instance. Motivated by this, we first design a process to stimulate peaks to emerge from a class response map. The emerged peaks are then back-propagated and effectively mapped to highly informative regions of each object instance, such as instance boundaries. We refer to the above maps generated from class peak responses as Peak Response Maps (PRMs). PRMs provide a fine-detailed instance-level representation, which allows instance masks to be extracted even with some off-the-shelf methods. To the best of our knowledge, we for the first time report results for the challenging image-level supervised instance segmentation task. Extensive experiments show that our method also boosts weakly supervised pointwise localization as well as semantic segmentation performance, and reports state-ofthe-art results on popular benchmarks, including PASCAL VOC 2012 and MS COCO. 1
A new X-ray absorption fine-structure (XAFS) spectroscopy beamline for fundamental and applied catalysis research, called XAFCA, has been built by the Institute of Chemical and Engineering Sciences, and the Singapore Synchrotron Light Source. XAFCA covers the photon energy range from 1.2 to 12.8 keV, making use of two sets of monochromator crystals, an Si (111) crystal for the range from 2.1 to 12.8 keV and a KTiOPO4 crystal [KTP (011)] for the range between 1.2 and 2.8 keV. Experiments can be carried out in the temperature range from 4.2 to 1000 K and pressures up to 30 bar for catalysis research. A safety system has been incorporated, allowing the use of flammable and toxic gases such as H2 and CO.
Weakly supervised object localization remains challenging, where only image labels instead of bounding boxes are available during training. Object proposal is an effective component in localization, but often computationally expensive and incapable of joint optimization with some of the remaining modules. In this paper, to the best of our knowledge, we for the first time integrate weakly supervised object proposal into convolutional neural networks (CNNs) in an end-to-end learning manner. We design a network component, Soft Proposal (SP), to be plugged into any standard convolutional architecture to introduce the nearly cost-free object proposal, orders of magnitude faster than state-of-the-art methods. In the SP-augmented CNNs, referred to as Soft Proposal Networks (SPNs), iteratively evolved object proposals are generated based on the deep feature maps then projected back, and further jointly optimized with network parameters, with image-level supervision only. Through the unified learning process, SPNs learn better object-centric filters, discover more discriminative visual evidence, and suppress background interference, significantly boosting both weakly supervised object localization and classification performance. We report the best results on popular benchmarks, including PASCAL VOC, MS COCO, and ImageNet.
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