Cross-Modal Retrieval in the Cooking Context: Learning Semantic Text-Image Embeddings (1804.11146v1)

Abstract: Designing powerful tools that support cooking activities has rapidly gained popularity due to the massive amounts of available data, as well as recent advances in machine learning that are capable of analyzing them. In this paper, we propose a cross-modal retrieval model aligning visual and textual data (like pictures of dishes and their recipes) in a shared representation space. We describe an effective learning scheme, capable of tackling large-scale problems, and validate it on the Recipe1M dataset containing nearly 1 million picture-recipe pairs. We show the effectiveness of our approach regarding previous state-of-the-art models and present qualitative results over computational cooking use cases.

• The paper introduces AdaMine, a novel model using a joint learning framework with a double-triplet loss and adaptive triplet mining to create semantic text-image embeddings for cross-modal retrieval.
• AdaMine employs a dual neural network architecture combining ResNet-50 for images and a hierarchical LSTM for text, effectively processing large-scale culinary data like the Recipe1M dataset.
• Evaluations show AdaMine significantly outperforms previous state-of-the-art methods on Recipe1M, achieving superior performance in median retrieval rank and recall metrics for retrieving recipes from images and vice versa.

Cross-Modal Retrieval in the Cooking Context: Learning Semantic Text-Image Embeddings

The paper introduces an innovative approach for cross-modal retrieval in the culinary context through a model named AdaMine, which aims to learn semantic text-image embeddings in a shared representation space. This model addresses the problem of aligning visual data, such as pictures of dishes, with textual data like recipes, facilitating the retrieval of one modality using the other. The researchers particularly focus on tackling large-scale data challenges presented by the Recipe1M dataset, which features approximately one million picture-recipe pairs.

Key Contributions

The authors present several novel contributions:

1. Joint Learning Framework: The model incorporates both retrieval and classification loss into the learning process, thus structuring the latent space. This is achieved through a double-triplet loss strategy that optimizes both fine-grained (instance-based) and high-level (semantic) representation. This combination ensures the model captures a multi-scale semantic structure more effectively than traditional pairwise or classification methods.
2. Adaptive Triplet Mining: By enhancing the gradient update procedure in stochastic gradient descent, the model selectively focuses on the most informative triplets. This adaptive approach mitigates vanishing gradients and allows the effective learning of embeddings by updating only on triplets that contribute actively to the loss.
3. Dual Neural Network Architecture: AdaMine employs a dual neural network framework, where the visual data stream is processed with a ResNet-50 network pretrained on ImageNet, and the textual data stream is handled by a hierarchical LSTM for both ingredients and instructions.
4. Performance Evaluation: The model's efficiency was demonstrated on the Recipe1M dataset, significantly surpassing the previous state-of-the-art models with improvements in median retrieval rank and recall metrics, both in 1k and 10k setups.

Numerical and Qualitative Results

The model showcases superior performance compared to methods like Canonical Correlation Analysis (CCA) and prior approaches utilizing pairwise loss plus a classification layer. For instance, AdaMine achieves a remarkable reduction in median rank compared to current state-of-the-art methods. Specifically, AdaMine scores a MedR of 1 compared to 5.2 from earlier models in a 1k retrieval setup, demonstrating its proficiency in aligning image and text representations effectively in a high-dimensional space.

Qualitative experiments further underscore the method’s capabilities like ingredient-to-image retrieval, highlighting AdaMine's ability to retrieve images containing specific ingredients, enhancing its application in personalized cooking tasks.

Implications and Future Directions

The implications of this research are essential in pushing the boundaries of multimodal retrieval and semantic understanding within AI systems. Practically, AdaMine can be instrumental in culinary applications such as recipe recommendation systems, smart cooking assistance devices, and health monitoring systems by efficiently aligning multimodal data. Theoretically, it presents a framework adaptable to other domains where cross-modal retrieval is crucial, such as medicine and fashion.

Looking forward, further development could explore refining the semantic structure with hierarchical clusters of food types, or extending this framework to accommodate additional data types, such as nutritional information or user ratings, which could further enhance the system's applicability and accuracy in varied computing contexts. By bridging the gap between modalities with deep learning, AdaMine sets a precedent for future exploration into combined language and vision understanding, expanding AI's capability for contextual intelligence beyond text and image datasets.