Learning Semantic Concepts and Order for Image and Sentence Matching (1712.02036v1)

Abstract: Image and sentence matching has made great progress recently, but it remains challenging due to the large visual-semantic discrepancy. This mainly arises from that the representation of pixel-level image usually lacks of high-level semantic information as in its matched sentence. In this work, we propose a semantic-enhanced image and sentence matching model, which can improve the image representation by learning semantic concepts and then organizing them in a correct semantic order. Given an image, we first use a multi-regional multi-label CNN to predict its semantic concepts, including objects, properties, actions, etc. Then, considering that different orders of semantic concepts lead to diverse semantic meanings, we use a context-gated sentence generation scheme for semantic order learning. It simultaneously uses the image global context containing concept relations as reference and the groundtruth semantic order in the matched sentence as supervision. After obtaining the improved image representation, we learn the sentence representation with a conventional LSTM, and then jointly perform image and sentence matching and sentence generation for model learning. Extensive experiments demonstrate the effectiveness of our learned semantic concepts and order, by achieving the state-of-the-art results on two public benchmark datasets.

Semantic-Enhanced Image and Sentence Matching: An Expert Overview

The paper "Learning Semantic Concepts and Order for Image and Sentence Matching" discusses an innovative model designed to address the challenge of visual-semantic discrepancy in image-sentence alignment tasks. This model explicitly targets the task of visual-semantic similarity measurement between images and textual descriptions, which is crucial for applications such as image annotation and text-based image retrieval.

Core Contributions and Methodology

The paper presents a semantic-enhanced model that improves image representations by learning semantic concepts and their corresponding semantic order. This is achieved through a multi-layered approach involving several key components:

1. Semantic Concept Extraction: Utilizing a multi-regional multi-label CNN, the method predicts semantic concepts of images, such as objects, properties, and actions. This involves leveraging a vocabulary derived from prominent semantic elements in sentences, allowing the model to gauge multiple concepts simultaneously across different image regions.
2. Semantic Order Learning: To organize semantic concepts in a meaningful sequence, the model uses a context-gated sentence generation scheme. This scheme effectively incorporates the global context of the image, providing a reference framework for aligning semantic concepts with their appropriate order as dictated by groundtruth sentence structure.
3. Joint Model Learning: The approach integrates image and sentence matching with sentence generation within a cohesive learning framework. This dual-task learning is facilitated through structured objectives that align images and sentences based on their similarities and concurrently optimize sentence generation accuracy.

Empirical Findings and Results

The model's efficacy is demonstrated through extensive experiments, yielding state-of-the-art results across two benchmark datasets: Flickr30k and MSCOCO. The key experimental results highlight the following:

• Image Annotation and Retrieval: The paper reports significant improvements in recall metrics, with the model achieving superior performance in matching images to sentences and vice versa. The results indicate a balanced understanding and representation of both visual and textual data, reinforcing the model's capacity to bridge semantic gaps.
• Ablation Studies: Examination of various model components reveals critical insights into the contributions of semantic concept and order learning. Notably, the incorporation of semantic order via sentence generation supervision provides marked benefits, demonstrating its crucial role in enhancing model performance.

Implications and Future Directions

The research outlined in this paper indicates notable implications for the development of cross-modal retrieval systems. By focusing on high-level semantics and structured visual learning, the model potentially enhances user experiences in applications requiring precise image-sentence interactions.

Looking forward, opportunities for further exploration include refining semantic concept prediction through advanced architectures such as the latest ResNet variants and investigating the model's applicability to related tasks such as image captioning. The proposed framework could also benefit from end-to-end training paradigms to enhance prediction accuracy and reduce computational overhead, particularly for scalability across broader datasets.

Overall, this work enriches the domain of image-sentence matching by integrating deep learning methodologies with semantic taxonomy, offering a robust foundation for future multimodal interaction systems.