|
| | | | |
| | | |
| Stat |
Members: 3645
Articles: 2'504'928
Articles rated: 2609
25 April 2024 |
|
| | | | |
|
Article overview
| |
|
|
Weakly Supervised PatchNets: Describing and Aggregating Local Patches for Scene Recognition | | Zhe Wang
; Limin Wang
; Yali Wang
; Bowen Zhang
; Yu Qiao
; | | Date: |
1 Sep 2016 | | Abstract: | Conventional feature encoding scheme (e.g., Fisher vector) with local
descriptors (e.g., SIFT) and recent deep convolutional neural networks (CNNs)
are two classes of successful methods for image recognition. In this paper, we
propose a hybrid representation, which leverages the great discriminative
capacity of CNNs and the simplicity of descriptor encoding schema for image
recognition, with a focus on scene recognition. To this end, we make three main
contributions from the following aspects. First, we propose a patch-level and
end-to-end architecture to model the appearance of local patches, called as
PatchNet. PatchNet is essentially a customized network trained in a weakly
supervised manner, which uses the image-level supervision to guide the
patch-level feature extraction. Second, we present a hybrid visual
representation, called as VSAD, by utilizing the robust feature representations
of PatchNet to describe local patches and exploiting the semantic probabilities
of PatchNet to aggregate these local patches into a global representation.
Third, based on our VSAD representation, we propose a state-of-the-art scene
recognition approach, which achieves excellent performance on two standard
benchmarks: MIT Indoor67 (86.2%) and SUN397 (73.0%). | | Source: | arXiv, 1609.0153 | | Services: | Forum | Review | PDF | Favorites | |
|
|
No review found.
Did you like this article?
Note: answers to reviews or questions about the article must be posted in the forum section.
Authors are not allowed to review their own article. They can use the forum section.
browser Mozilla/5.0 AppleWebKit/537.36 (KHTML, like Gecko; compatible; ClaudeBot/1.0; +claudebot@anthropic.com)
|
| |
|
|
|
|
News, job offers and information for researchers and scientists:
| |
REGISTER