From Volcano to Toyshop: Adaptive Discriminative Region Discovery for Scene Recognition

As deep learning approaches to scene recognition emerge, they have continued to leverage discriminative regions at multiple scales, building on practices established by conventional image classification research. However, approaches remain largely generic, and do not carefully consider the special properties of scenes. In this paper, inspired by the intuitive differences between scenes and objects, we propose Adi-Red, an adaptive approach to discriminative region discovery for scene recognition. Adi-Red uses a CNN classifier, which was pre-trained using only image-level scene labels, to discover discriminative image regions directly. These regions are then used as a source of features to perform scene recognition. The use of the CNN classifier makes it possible to adapt the number of discriminative regions per image using a simple, yet elegant, threshold, at relatively low computational cost. Experimental results on the scene recognition benchmark dataset SUN397 demonstrate the ability of Adi-Red to outperform the state of the art. Additional experimental analysis on the Places dataset reveals the advantages of Adi-Red, and highlight how they are specific to scenes. We attribute the effectiveness of Adi-Red to the ability of adaptive region discovery to avoid introducing noise, while also not missing out on important information.Comment: To appear at the ACM International Conference on Multimedia (ACM MM 2018). Code available at https://github.com/ZhengyuZhao/Adi-Red-Scen

Benchmarking Self-Supervised Learning on Diverse Pathology Datasets

Computational pathology can lead to saving human lives, but models are annotation hungry and pathology images are notoriously expensive to annotate. Self-supervised learning has shown to be an effective method for utilizing unlabeled data, and its application to pathology could greatly benefit its downstream tasks. Yet, there are no principled studies that compare SSL methods and discuss how to adapt them for pathology. To address this need, we execute the largest-scale study of SSL pre-training on pathology image data, to date. Our study is conducted using 4 representative SSL methods on diverse downstream tasks. We establish that large-scale domain-aligned pre-training in pathology consistently out-performs ImageNet pre-training in standard SSL settings such as linear and fine-tuning evaluations, as well as in low-label regimes. Moreover, we propose a set of domain-specific techniques that we experimentally show leads to a performance boost. Lastly, for the first time, we apply SSL to the challenging task of nuclei instance segmentation and show large and consistent performance improvements under diverse settings

Analysis of social welfare impact of crop pest and disease damages due to climate change: a case study of dried red peppers

Abstract Climate change can affect agricultural production both directly and indirectly. The direct impact is through climate change itself while the indirect impact is through the outbreak of pests and diseases (P&D) affected by climate change. We measured the difference in social welfare change of dried red peppers in monetary values between these two effects based on constructed three models. In the P&D damage model, the effects of climatic factors on P&D damages were analyzed. In the yield model, the direct and indirect effects of climatic factors on the dried red pepper yields were analyzed. Lastly, the effect of rising temperatures on the social welfare of dried red peppers was measured in monetary values using the equilibrium displacement model (EDM). As a key result, although these rising temperatures increase the yields and social welfare, there are differences in social welfare change between with and without P&D damages, and the difference increases over time. This implies that global climate change can affect agricultural production around the world, which can affect food security around the world beyond changes in crop prices and social welfare. So rigorous pest control and damage predictions are needed