Local Similarity-Aware Deep Feature Embedding (1610.08904v1)

Abstract: Existing deep embedding methods in vision tasks are capable of learning a compact Euclidean space from images, where Euclidean distances correspond to a similarity metric. To make learning more effective and efficient, hard sample mining is usually employed, with samples identified through computing the Euclidean feature distance. However, the global Euclidean distance cannot faithfully characterize the true feature similarity in a complex visual feature space, where the intraclass distance in a high-density region may be larger than the interclass distance in low-density regions. In this paper, we introduce a Position-Dependent Deep Metric (PDDM) unit, which is capable of learning a similarity metric adaptive to local feature structure. The metric can be used to select genuinely hard samples in a local neighborhood to guide the deep embedding learning in an online and robust manner. The new layer is appealing in that it is pluggable to any convolutional networks and is trained end-to-end. Our local similarity-aware feature embedding not only demonstrates faster convergence and boosted performance on two complex image retrieval datasets, its large margin nature also leads to superior generalization results under the large and open set scenarios of transfer learning and zero-shot learning on ImageNet 2010 and ImageNet-10K datasets.

• The paper introduces a new Position-Dependent Deep Metric that tailors similarity evaluation to local feature distributions in embedding space.
• It employs hard sample mining and a joint optimization framework with a double-header hinge loss to improve convergence and accuracy.
• Empirical results show significant improvements in image retrieval, achieving 58.3% Recall@1 on CUB-200-2011 and robust transfer learning performance.

Local Similarity-Aware Deep Feature Embedding

The paper "Local Similarity-Aware Deep Feature Embedding" presents an advanced method for enhancing deep feature embedding in complex visual tasks by introducing a Position-Dependent Deep Metric (PDDM) unit. The authors argue against the use of a global Euclidean distance metric for characterizing feature similarity in deep embedding methods due to the heterogeneity of visual data, where the intraclass distances can sometimes exceed interclass distances in regions with varying feature densities.

Main Contributions

The paper introduces the concept of local similarity-aware deep feature embedding as an enhancement over existing global metrics. The key contributions include:

1. Position-Dependent Deep Metric (PDDM): The PDDM unit is a novel metric learning approach that adapts to the local structure of the feature space. Unlike global metrics, PDDM considers both feature difference and absolute position within the embedding space, enabling it to capture more nuanced similarities.
2. Hard Sample Mining: By leveraging PDDM, genuinely hard samples are identified within a local neighborhood, improving the robustness and convergence speed of deep embedding learning.
3. Joint Optimization Framework: The proposed system integrates metric learning and feature embedding optimization into a single framework, employing a double-header hinge loss that promotes a large margin criterion.
4. Improved Convergence and Performance: The local similarity-aware embedding introduced demonstrates faster convergence and superior generalization, as evidenced in complex image retrieval scenarios on datasets such as CUB-200-2011 and CARS196.

Numerical Results and Implications

In terms of numerical results, the proposed feature embedding method, when integrated with PDDM, significantly outperforms existing state-of-the-art methods such as contrastive, triplet, and lifted structured embeddings. The "PDDM+Quadruplet" approach achieves a Recall@1 score of 58.3% on CUB-200-2011, indicating a substantial improvement over previous techniques.

The work also evaluated the transfer learning potential of the learned embeddings, achieving a 48.2% flat top-5 accuracy for zero-shot learning on ImageNet 2010. This suggests strong generalization capabilities, crucial for emerging applications in large-scale and open-set scenarios.

Future Directions

The research provides a compelling case for further exploration in local similarity-aware metric learning, particularly its applications in visual-semantic embeddings and zero-shot learning beyond conventional hierarchical and attribute-based approaches. Moreover, its capacity for generalization opens opportunities in diverse domains such as autonomous driving and facial recognition, where real-world variability presents significant challenges.

In conclusion, the paper contributes significantly to the AI community, offering robust tools to overcome obstacles presented by complex feature space distributions. Its approach lays the groundwork for more sophisticated, adaptive systems capable of handling intricate visual tasks with increased efficiency and accuracy.