End-to-End Learning of Fisher Vector Encodings for Part Features in Fine-Grained Recognition

2021 ◽  
pp. 142-158
Author(s):  
Dimitri Korsch ◽  
Paul Bodesheim ◽  
Joachim Denzler
Keyword(s):  
Fine Grained ◽  
Fisher Vector ◽  
End To End

scholarly journals Revisiting the Fisher vector for fine-grained classification

2014 ◽  
Vol 49 ◽  
pp. 92-98 ◽  
Author(s):  
Philippe-Henri Gosselin ◽  
Naila Murray ◽  
Hervé Jégou ◽  
Florent Perronnin
Keyword(s):  
Fine Grained ◽  
Fisher Vector

scholarly journals Localizing Unseen Activities in Video via Image Query

2019 ◽  
Author(s):  
Zhu Zhang ◽  
Zhou Zhao ◽  
Zhijie Lin ◽  
Jingkuan Song ◽  
Deng Cai
Keyword(s):  
Relative Position ◽  
Image Region ◽  
Video Understanding ◽  
Fine Grained ◽  
Target Segment ◽  
Action Localization ◽  
End To End ◽  
Image Query

Action localization in untrimmed videos is an important topic in the field of video understanding. However, existing action localization methods are restricted to a pre-defined set of actions and cannot localize unseen activities. Thus, we consider a new task to localize unseen activities in videos via image queries, named Image-Based Activity Localization. This task faces three inherent challenges: (1) how to eliminate the influence of semantically inessential contents in image queries; (2) how to deal with the fuzzy localization of inaccurate image queries; (3) how to determine the precise boundaries of target segments. We then propose a novel self-attention interaction localizer to retrieve unseen activities in an end-to-end fashion. Specifically, we first devise a region self-attention method with relative position encoding to learn fine-grained image region representations. Then, we employ a local transformer encoder to build multi-step fusion and reasoning of image and video contents. We next adopt an order-sensitive localizer to directly retrieve the target segment. Furthermore, we construct a new dataset ActivityIBAL by reorganizing the ActivityNet dataset. The extensive experiments show the effectiveness of our method.

scholarly journals End-to-End Network Slices: From Network Function Profiles to Fine-Grained SLAs

2019 ◽  
Author(s):  
Raphael V. Rosa ◽  
Christian Esteve Rothenberg
Keyword(s):  
Augmented Reality ◽  
State Of The Art ◽  
Vehicular Communications ◽  
Automated Extraction ◽  
Network Slicing ◽  
Fine Grained ◽  
Network Function ◽  
End To End ◽  
Future Work ◽  
Administrative Domains

Towards end-to-end network slicing, diverse envisioned 5G services (eg, augmented reality, vehicular communications, IoT) Call for advanced multi-administrative domain service deployments, open challenges from vertical Agreement (SLA) -based orchestration hazards. Through different proposed methodologies and demonstrated prototypes, this work showcases: the automated extraction of network function profiles; the manners to analyze how such profiles compose programmable network slice footprints; and the means to perform fine-grained auditable SLAs for end-to-end network slicing among multiple administrative domains. Sustained on state-of-the-art networking concepts, this work presents contributions by detecting roots on standardization efforts and best-of-breed open source embodiments, each one standing prominent future work topics in shape of its shortcomings.

scholarly journals Fine-Grained Fashion Similarity Learning by Attribute-Specific Embedding Network

2020 ◽  
Vol 34 (07) ◽  
pp. 11741-11748 ◽  
Author(s):  
Zhe Ma ◽  
Jianfeng Dong ◽  
Zhongzi Long ◽  
Yao Zhang ◽  
Yuan He ◽  
...  
Keyword(s):  
Spatial Attention ◽  
Copyright Protection ◽  
Similarity Learning ◽  
Specific Design ◽  
Fine Grained ◽  
Multiple Attribute ◽  
End To End ◽  
Design Attribute

This paper strives to learn fine-grained fashion similarity. In this similarity paradigm, one should pay more attention to the similarity in terms of a specific design/attribute among fashion items, which has potential values in many fashion related applications such as fashion copyright protection. To this end, we propose an Attribute-Specific Embedding Network (ASEN) to jointly learn multiple attribute-specific embeddings in an end-to-end manner, thus measure the fine-grained similarity in the corresponding space. With two attention modules, i.e., Attribute-aware Spatial Attention and Attribute-aware Channel Attention, ASEN is able to locate the related regions and capture the essential patterns under the guidance of the specified attribute, thus make the learned attribute-specific embeddings better reflect the fine-grained similarity. Extensive experiments on four fashion-related datasets show the effectiveness of ASEN for fine-grained fashion similarity learning and its potential for fashion reranking. Code and data are available at https://github.com/Maryeon/asen.

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