One-Shot Fine-Grained Instance Retrieval

@article{Yao2017OneShotFI,
  title={One-Shot Fine-Grained Instance Retrieval},
  author={Hantao Yao and Shiliang Zhang and Yongdong Zhang and Jintao Li and Qi Tian},
  journal={Proceedings of the 25th ACM international conference on Multimedia},
  year={2017}
}
Fine-Grained Visual Categorization (FGVC) has achieved significant progress recently. However, the number of fine-grained species could be huge and dynamically increasing in real scenarios, making it difficult to recognize unseen objects under the current FGVC framework. This raises an open issue to perform large-scale fine-grained identification without a complete training set. Aiming to conquer this issue, we propose a retrieval task named One-Shot Fine-Grained Instance Retrieval (OSFGIR… 
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16 Citations
Highly Influential Citations
2
Background Citations
8
Methods Citations
6
Results Citations
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16 Citations

Low-Rank Pairwise Alignment Bilinear Network For Few-Shot Fine-Grained Image Classification
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This paper addresses the Few-Shot Fine-Grained (FSFG) classification problem, which focuses on tackling the fine-grained classification under the challenging few-shot learning setting, and proposes a novel low-rank pairwise bilinear pooling operation to capture the nuanced differences between the support and query images for learning an effective distance metric.
Compare More Nuanced: Pairwise Alignment Bilinear Network for Few-Shot Fine-Grained Learning
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This work proposes a simple yet effective model for the Few-Shot Fine-Grained recognition, which tries to tackle the challenging fine-grained recognition task using meta-learning, and uses a novel pairwise bilinear pooling to compare the nuanced differences between base images and query images for learning a deep distance metric.
Fine-Grained Grocery Product Recognition by One-Shot Learning
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A novel hybrid classification approach that combines feature-based matching and one-shot deep learning with a coarse-to-fine strategy to improve the accuracy of fine-grained grocery products recognition effectively is presented.
Fine-Grained Visual-Textual Representation Learning
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A fine-grained visual-textual representation learning (VTRL) approach, which achieves the best categorization accuracy compared with the state-of-the-art methods.
Weighted Multi-feature Fusion Algorithm for Fine-Grained Image Retrieval
TLDR
A new constraint to select discriminative patches is designed, which makes use of the irregular but more accurate object region to select the discriminate patches, and the Deep Belief Network is introduced to effectively fuse multi-level features.
Fine-grained Recognition of 3D Shapes Based on Multi-view Recurrent Neural Network
TLDR
A recurrent neural network was adopted to fuse multi-view features instead of max-pooling operation, which is named as MVRNN, and a loss function taking the discrimination of fused features into consideration was defined to train the MVCNN.
Self-aligned Spatial Feature Extraction Network for UAV Vehicle Re-identification
TLDR
An unsupervised self-aligned network consisting of three branches is developed to extract efficient fine-grained features and avoid tedious annotating work, and achieves the best performance compared with recent ReID works.
VP-ReID: Vehicle and Person Re-Identification System
TLDR
A robust and efficient person and video ReID system named as VP-ReID is developed, build based on recent works including Deep Convolutional Neural Network design for discriminative feature extraction, efficient off-line indexing, as well as distance metric optimization for deep feature learning.
RAM: A Region-Aware Deep Model for Vehicle Re-Identification
TLDR
A novel learning algorithm is introduced to jointly use vehicle IDs, types/models, and colors to train the Region-Aware deep Model (RAM), which fuses more cues for training and results in more discriminative global and regional features.
Small variance among different subcategories Artic Tern Caspian Tern Large variance in the same subcategory Common Tern Salty Backed Gull Salty Backed Gull Salty Backed Gull
TLDR
Comprehensive experimental results on the widely-used CUB-200-2011 and Oxford Flowers-102 datasets demonstrate the effectiveness of the VTRL approach, which achieves the best categorization accuracy compared with state-of-the-art methods.
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