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The traditional object retrieval task aims to learn a discriminative featurerepresentation with intra-similarity and inter-dissimilarity, which supposesthat the objects in an image are manually or automatically pre-cropped exactly.However, in many real-world searching scenarios (e.g., video surveillance), theobjects (e.g., persons, vehicles, etc.) are seldom accurately detected orannotated. Therefore, object-level retrieval becomes intractable withoutbounding-box annotation, which leads to a new but challenging topic, i.e.image-level search. In this paper, to address the image search issue, we firstintroduce an end-to-end Integrated Net (I-Net), which has three merits: 1) ASiamese architecture and an on-line pairing strategy for similar and dissimilarobjects in the given images are designed. 2) A novel on-line pairing (OLP) lossis introduced with a dynamic feature dictionary, which alleviates themulti-task training stagnation problem, by automatically generating a number ofnegative pairs to restrict the positives. 3) A hard example priority (HEP)based softmax loss is proposed to improve the robustness of classification taskby selecting hard categories. With the philosophy of divide and conquer, wefurther propose an improved I-Net, called DC-I-Net, which makes two newcontributions: 1) two modules are tailored to handle different tasks separatelyin the integrated framework, such that the task specification is guaranteed. 2)A class-center guided HEP loss (C2HEP) by exploiting the stored class centersis proposed, such that the intra-similarity and inter-dissimilarity can becaptured for ultimate retrieval. Extensive experiments on famous image-levelsearch oriented benchmark datasets demonstrate that the proposed DC-I-Netoutperforms the state-of-the-art tasks-integrated and tasks-separated imagesearch models.

Tasks Integrated Networks: Joint Detection and Retrieval for Image Search