Learning and aggregating deep local descriptors for instance-level recognition (2007.13172v1)

Abstract: We propose an efficient method to learn deep local descriptors for instance-level recognition. The training only requires examples of positive and negative image pairs and is performed as metric learning of sum-pooled global image descriptors. At inference, the local descriptors are provided by the activations of internal components of the network. We demonstrate why such an approach learns local descriptors that work well for image similarity estimation with classical efficient match kernel methods. The experimental validation studies the trade-off between performance and memory requirements of the state-of-the-art image search approach based on match kernels. Compared to existing local descriptors, the proposed ones perform better in two instance-level recognition tasks and keep memory requirements lower. We experimentally show that global descriptors are not effective enough at large scale and that local descriptors are essential. We achieve state-of-the-art performance, in some cases even with a backbone network as small as ResNet18.

• The paper introduces a deep learning method using ASMK to aggregate local descriptors, enhancing instance-level recognition performance.
• It applies metric learning with image-level annotations to derive efficient, memory-conscious features from deep network activations.
• Experimental results on landmark datasets show the method outperforms hand-crafted and global descriptor approaches, boosting precision and recall.

Overview of Learning and Aggregating Deep Local Descriptors for Instance-Level Recognition

The paper "Learning and aggregating deep local descriptors for instance-level recognition" by Tolias, Jenicek, and Chum introduces a novel approach for developing efficient deep local descriptors aimed at instance-level recognition tasks. The primary motivation stems from the inadequacy of global descriptors in large-scale applications and the superiority of local descriptors in handling instance-level tasks, particularly when combined with efficient match kernel methods like ASMK.

Methodology Summary

The proposed method leverages a training regimen based on metric learning where image-level annotations are pivotal. Unlike traditional approaches that focus on spatial verification, this method capitalizes on ASMK to achieve effective instance-level recognition without precise localization being a requirement. The local descriptors are derived from the activations of internal components of deep networks, allowing for memory efficiency and reduced computational cost compared to existing methods.

Key to the methodology is the balance between performance and memory requirements, which is explored through empirical validation. This paper reveals that the combination of learned local descriptors with ASMK offers superior performance on various instance-level recognition tasks in landmark domains, outclassing both traditional hand-crafted methods and newer global descriptor-based approaches. Notably, state-of-the-art results are achieved with modest architectures, such as ResNet18, exemplifying the efficiency of the proposed system.

Experimental Outcomes

The authors validate their method using two primary instance-level recognition tasks: search and classification in landmark-focused datasets. Results indicate that the proposed local descriptors significantly outperform global descriptors on large-scale datasets, demonstrating robust image similarity estimation. In the context of image search in well-known datasets such as R and R, the proposed descriptors exhibit a noticeable improvement over contemporary models, including DELF, when combined with ASMK.

In terms of classification performance, evaluated on the extensive Google Landmarks Dataset, the proposed method consistently outperforms existing global and local descriptor approaches, maintaining computational efficiency. This is achieved through the effective aggregation of strength-weighted local features, enhancing both precision and recall.

Implications and Future Directions

The implications of this work are twofold. Practically, the deployment to real-world systems where memory and efficiency constraints are paramount could benefit significantly from using this method. Theoretically, the successful leveraging of image-level annotations to optimize local descriptors hints at potential expansions in other domains where fine-grained recognition tasks are critical.

Future research directions may explore scaling the model to incorporate more sophisticated architectures beyond ResNet and adapting the method to register improvements with the incorporation of spatial verification steps where necessary. Also, extending this approach to canonical global descriptor techniques might unify the advantages of both paradigms, promoting further advances in large-scale image recognition frameworks.

In conclusion, this work contributes a significant refinement in the field of instance-level recognition by providing a streamlined, efficient approach to the utilization of local descriptors, accentuating their importance in achieving state-of-the-art performance while managing resource constraints effectively.