Fine-Grained Fashion Similarity Learning by Attribute-Specific Embedding Network
نویسندگان
چکیده
منابع مشابه
Link Prediction using Network Embedding based on Global Similarity
Background: The link prediction issue is one of the most widely used problems in complex network analysis. Link prediction requires knowing the background of previous link connections and combining them with available information. The link prediction local approaches with node structure objectives are fast in case of speed but are not accurate enough. On the other hand, the global link predicti...
متن کاملLearning Fine-grained Image Similarity with Deep Ranking
001 002 003 004 005 006 007 008 009 010 011 012 013 014 015 016 017 018 019 020 021 022 023 024 025 026 027 028 029 030 031 032 033 034 035 036 037 038 039 040 041 042 043 044 045 046 047 048 049 050 051 052 053 054 055 056 057 058 059 060 061 062 063 064 065 066 067 068 069 070 071 072 073 074 075 076 077 078 079 080 081 082 083 084 085 086 087 088 089 090 091 092 093 094 095 096 097 098 099 1...
متن کاملTowards Fine-grained Service Matchmaking by Using Concept Similarity
Several description frameworks to semantically describe and match services on the one hand and service requests on the other have been presented in the literature. Many of the current proposals for defining notions of match between service advertisements and requests are based on subsumption checking in more or less expressive Description Logics, thus providing boolean match functions, rather t...
متن کاملLarge Scale Fine-Grained Categorization and Domain-Specific Transfer Learning
Transferring the knowledge learned from large scale datasets (e.g., ImageNet) via fine-tuning offers an effective solution for domain-specific fine-grained visual categorization (FGVC) tasks (e.g., recognizing bird species or car make & model). In such scenarios, data annotation often calls for specialized domain knowledge and thus is difficult to scale. In this work, we first tackle a problem ...
متن کاملContent Specific Feature Learning for Fine-Grained Plant Classification
We present the plant classification system submitted by the QUT RV team to the LifeCLEF 2015 plant task. Our system learns a content specific feature for various plant parts such as branch, leaf, fruit, flower and stem. These features are learned using a deep convolutional neural network. Experiments on the LifeCLEF 2015 plant dataset show that the proposed method achieves good performance with...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Proceedings of the AAAI Conference on Artificial Intelligence
سال: 2020
ISSN: 2374-3468,2159-5399
DOI: 10.1609/aaai.v34i07.6845