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	WildFish: A Large Benchmark for Fish Recognition in the Wild
	Peiqin Zhuang; Yali Wang; Yu Qiao
	2018
会议日期	2018
会议地点	韩国
英文摘要	Fish recognition is an important task to understand the marine ecosystem and biodiversity. It is often challenging to identify fish species in the wild, due to the following difficulties. First, most fish benchmarks are small-scale, which may limit the representation power of machine learning models. Second, the number of fish species is huge, and there may still exist unknown categories in our planet. The traditional classifiers often fail to deal with this open-set scenario. Third, certain fish species are highly-confused. It is often hard to figure out the subtle differences, only by the unconstrained images. Motivated by these facts, we introduce a large-scale Wild- Fish benchmark for fish recognition in the wild. Specifically, we make three contributions in this paper. First, WildFish is the largest image data set for wild fish recognition, to our best knowledge. It consists of 1000 fish categories with 54,459 unconstrained images, allowing to train high-capacity models for automatic fish classifica- tion. Second, we propose a novel open-set fish classification task for realistic scenarios, and investigate the open-set deep learning framework with a number of practical designs. Third, we propose a novel fine-grained recognition task, with the guidance of pairwise textual descriptions. Via leveraging the comparison knowledge in the sentence, we design a multi-modal fish net to effectively distinguish two confused categories in a pair. Finally, we release WildFish (https://github.com/PeiqinZhuang/WildFish), in order to bring benefit to more research studies in multimedia and beyond
URL标识	查看原文
内容类型	会议论文
源URL	[http://ir.siat.ac.cn:8080/handle/172644/13694]
专题	深圳先进技术研究院_集成所
推荐引用方式 GB/T 7714	Peiqin Zhuang,Yali Wang,Yu Qiao. WildFish: A Large Benchmark for Fish Recognition in the Wild[C]. 见:. 韩国. 2018.

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