Dual-Path Convolutional Image-Text Embeddings with Instance Loss (1711.05535v4)

Abstract: Matching images and sentences demands a fine understanding of both modalities. In this paper, we propose a new system to discriminatively embed the image and text to a shared visual-textual space. In this field, most existing works apply the ranking loss to pull the positive image / text pairs close and push the negative pairs apart from each other. However, directly deploying the ranking loss is hard for network learning, since it starts from the two heterogeneous features to build inter-modal relationship. To address this problem, we propose the instance loss which explicitly considers the intra-modal data distribution. It is based on an unsupervised assumption that each image / text group can be viewed as a class. So the network can learn the fine granularity from every image/text group. The experiment shows that the instance loss offers better weight initialization for the ranking loss, so that more discriminative embeddings can be learned. Besides, existing works usually apply the off-the-shelf features, i.e., word2vec and fixed visual feature. So in a minor contribution, this paper constructs an end-to-end dual-path convolutional network to learn the image and text representations. End-to-end learning allows the system to directly learn from the data and fully utilize the supervision. On two generic retrieval datasets (Flickr30k and MSCOCO), experiments demonstrate that our method yields competitive accuracy compared to state-of-the-art methods. Moreover, in language based person retrieval, we improve the state of the art by a large margin. The code has been made publicly available.

Overview of "Learning Image-Text Embeddings with Instance Loss"

In the paper "Learning Image-Text Embeddings with Instance Loss," the authors propose a system that aims to improve the process of embedding images and text into a shared feature space, which is a significant challenge in cross-modal retrieval tasks. This is achieved by introducing an "instance loss" function designed to enhance weight initialization for the conventional ranking loss commonly used in such tasks. The system is evaluated on well-known datasets such as Flickr30k and MSCOCO and demonstrates competitive accuracy compared to the state-of-the-art, with particularly notable improvements in language-based person retrieval tasks.

The central contribution of this research is the introduction of the instance loss. Unlike typical ranking loss which only focuses on the distance between image and text modalities, instance loss explicitly accounts for intra-modal data distributions. This approach is predicated on an unsupervised assumption that each image/text group functions as a distinct class in the network, thereby allowing for greater granularity in discriminative embedding learning. Specifically, instance loss enhances intra-modal discrimination through softmax classification, which implicitly enhances the initialization of weights before the application of the ranking loss.

Methodology

The authors develop an end-to-end dual-path convolutional neural network (CNN) to learn both image and text representations. This model employs a ResNet-50 backbone, commonly used in image processing, while simultaneously employing a text CNN for sentence representation. The proposed instance loss, when combined with ranking loss, facilitates robust learning of image-text pairings. This dual approach addresses a notable gap in prior research, where often fixed CNN features were leveraged without optimizing the relationship between modalities through end-to-end training.

The research leverages large datasets that include both image and caption data, such as MSCOCO and Flickr30k, providing ample challenge and opportunity for evaluation and testing. The instance loss demonstrates its efficacy by outperforming baseline approaches that solely employ ranking loss, even when they are strengthened by well-established pre-trained networks and datasets.

Results

Significant performance improvements achieved by the proposed method demonstrate its potential. For instance, in the domain of language-based person search, the proposed method significantly outperforms existing methods by a large margin, suggesting its practical applicability and transferability across different types of semantic retrieval tasks. The experimental results illustrate a substantial boost in retrieval accuracy, particularly in rank-based evaluation metrics.

An empirical analysis of the instance loss shows its robustness in providing better initialization, resulting in fewer hard negative samples which often plague ranking loss-oriented models. This suggests the instance loss not only provides better initialization but also induces a regularization effect throughout the training process.

Implications and Future Directions

The implications of this research are twofold: practically, it advances methods for fine-grained cross-modal retrieval, a task integral to multiple domains such as multimedia search engines, recommendation systems, and human-computer interaction. Theoretically, it suggests a reliable pathway for addressing intra-modal nuances, which can effectively be utilized in future models that necessitate a shared feature space across heterogeneous data types.

Future work can explore scaling the methodology to more complex datasets and investigate the potential of integrating various semi-supervised or fully unsupervised learning approaches, leveraging the shared feature space to derive further semantic insights across modalities. Additionally, exploring transference of the instance loss framework to other domains involving multi-modal data such as video-audio retrieval could provide further pathways for innovation.