SwAMP: Swapped Assignment of Multi-Modal Pairs for Cross-Modal Retrieval

• URL: http://arxiv.org/abs/2111.05814v1
• Date: Wed, 10 Nov 2021 17:17:09 GMT
• Title: SwAMP: Swapped Assignment of Multi-Modal Pairs for Cross-Modal Retrieval
• Authors: Minyoung Kim
• Abstract summary: We propose a novel loss function that is based on self-labeling of the unknown classes. We tested our approach on several real-world cross-modal retrieval problems, including text-based video retrieval, sketch-based image retrieval, and image-text retrieval.
• Score: 15.522964295287425
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We tackle the cross-modal retrieval problem, where the training is only supervised by the relevant multi-modal pairs in the data. The contrastive learning is the most popular approach for this task. However, its sampling complexity for learning is quadratic in the number of training data points. Moreover, it makes potentially wrong assumption that the instances in different pairs are automatically irrelevant. To address these issues, we propose a novel loss function that is based on self-labeling of the unknown classes. Specifically, we aim to predict class labels of the data instances in each modality, and assign those labels to the corresponding instances in the other modality (i.e., swapping the pseudo labels). With these swapped labels, we learn the data embedding for each modality using the supervised cross-entropy loss, hence leading to linear sampling complexity. We also maintain the queues for storing the embeddings of the latest batches, for which clustering assignment and embedding learning are done at the same time in an online fashion. This removes computational overhead of injecting intermittent epochs of entire training data sweep for offline clustering. We tested our approach on several real-world cross-modal retrieval problems, including text-based video retrieval, sketch-based image retrieval, and image-text retrieval, and for all these tasks our method achieves significant performance improvement over the contrastive learning.

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