Attention-Based Dropout Layer for Weakly Supervised Single Object Localization and Semantic Segmentation | PERPUSTAKAAN UNIVERSITAS KATOLIK PARAHYANGAN

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Attention-Based Dropout Layer for Weakly Supervised Single Object Localization and Semantic Segmentation

Choe, Junsuk - Nama Orang; Lee, Seungho - Nama Orang; Shim, Hyunjung - Nama Orang;

Both weakly supervised single object localization and semantic segmentation techniques learn an object’s location using only image-level labels. However, these techniques are limited to cover only the most discriminative part of the object and not the entire object. To address this problem, we propose an attention-based dropout layer, which utilizes the attention mechanism to locate the entire object efficiently. To achieve this, we devise two key components, 1) hiding the most discriminative part from the model to capture the entire object, and 2) highlighting the informative region to improve the classification power of the model. These allow the classifier to be maintained with a reasonable accuracy while the entire object is covered. Through extensive experiments, we demonstrate that the proposed method effectively improves the weakly supervised single object localization accuracy, thereby achieving a new state-of-the-art localization accuracy on the CUB-200-2011 and a comparable accuracy existing state-of-the-arts on the ImageNet-1k. The proposed method is also effective in improving the weakly supervised semantic segmentation performance on the Pascal VOC and MS COCO. Furthermore, the proposed method is more efficient than existing techniques in terms of parameter and computation overheads. Additionally, the proposed method can be easily applied in various backbone networks.

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Barcode		Tipe Koleksi	Nomor Panggil	Lokasi	Status
art141097	null	Artikel		Gdg9-Lt3	Tersedia namun tidak untuk dipinjamkan - No Loan

Informasi Detail

Judul Seri: IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE; Vol.43 No.12 December 2021
No. Panggil: -
Penerbit: : .,
Deskripsi Fisik: p. 4256-4271
Bahasa: English
ISBN/ISSN: -
Klasifikasi: NONE
Tipe Isi: -
Tipe Media: -
Tipe Pembawa: -
Edisi: -
Subjek: ATTENTION
SEMANTIC SEGMENTATION
OBJECT LOCALIZATION
WEAKLY SUPERVISED
Info Detail Spesifik: DOI: 10.1109/TPAMI.2020.2999099
Pernyataan Tanggungjawab: Junsuk Choe, Seungho Lee, Hyunjung Shim

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