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Task-specific Part Discovery for Fine-grained Few-shot Classification

Machine Intelligence Research 2024年第5期21卷 954-965页

作者： Yongxian Wei Xiu-Shen Wei School of Computer Science and Technology Nanjing University of Science and TechnologyNanjing210094China

Localizing discriminative object parts(e.g.,bird head)is crucial for fine-grained classification tasks,especially for the more challenging fine-grained few-shot *** work always relies on the learned object parts in a unified manner,where they attend the same object parts(even with common attention weights)for different few-shot episodic *** this paper,we propose that it should adaptively capture the task-specific object parts that require attention for each few-shot task,since the parts that can distinguish different tasks are naturally *** for a few-shot task,after obtaining part-level deep features,we learn a task-specific part-based dictionary for both aligning and reweighting part features in an ***,part-level categorical prototypes are generated based on the part features of support data,which are later employed by calculating distances to classify query data for *** retain the discriminative ability of the part-level representations(i.e.,part features and part prototypes),we design an optimal transport solution that also utilizes query data in a transductive way to optimize the aforementioned distance calculation for the final *** experiments on five fine-grained benchmarks show the superiority of our method,especially for the 1-shot setting,gaining 0.12%,8.56%and 5.87%improvements over state-of-the-art methods on CUB,Stanford Dogs,and Stanford Cars,respectively.

关键词： Fine-grained image recognition few-shot learning transductive learning visual dictionary part feature discovery

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Salient region detection and segmentation for general object recognition and image understanding

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Science China(Information Sciences) 2011年第12期54卷 2481-2490页

作者： HUANG TieJun 1 , TIAN YongHong 1 , LI Jia 2 & YU HaoNan 11 National Engineering Laboratory for Video Technology, School of Electrical Engineering and Computer Science, Peking University, Beijing 100871, China 2 Key Laboratory of Intelligent Information Processing, Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China National Engineering Laboratory for Video Technology School of Electrical Engineering and Computer Science Peking University Beijing China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing China

General object recognition and image understanding is recognized as a dramatic goal for computer vision and multimedia retrieval. In spite of the great efforts devoted in the last two decades, it still remains an open problem. In this paper, we propose a selective attention-driven model for general image understanding, named GORIUM (general object recognition and image understanding model). The key idea of our model is to discover recurring visual objects by selective attention modeling and pairwise local invariant features matching on a large image set in an unsupervised manner. Towards this end, it can be formulated as a four-layer bottom-up model, i.e., salient region detection, object segmentation, automatic object discovering and visual dictionary construction. By exploiting multi-task learning methods to model visual saliency simultaneously with the bottom-up and top-down factors, the lowest layer can effectively detect salient objects in an image. The second layer exploits a simple yet effective learning approach to generate two complementary maps from several raw saliency maps, which then can be utilized to segment the salient objects precisely from a complex scene. For the third layer, we have also implemented an unsupervised approach to automatically discover general objects from large image set by pairwise matching with local invariant features. Afterwards, visual dictionary construction can be implemented by using many state-of-the-art algorithms and tools available nowadays.

关键词： object recognition image understanding visual saliency salient object segmentation visual dictionary

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