Polysemous Visual-Semantic Embedding for Cross-Modal Retrieval (1906.04402v2)

Abstract: Visual-semantic embedding aims to find a shared latent space where related visual and textual instances are close to each other. Most current methods learn injective embedding functions that map an instance to a single point in the shared space. Unfortunately, injective embedding cannot effectively handle polysemous instances with multiple possible meanings; at best, it would find an average representation of different meanings. This hinders its use in real-world scenarios where individual instances and their cross-modal associations are often ambiguous. In this work, we introduce Polysemous Instance Embedding Networks (PIE-Nets) that compute multiple and diverse representations of an instance by combining global context with locally-guided features via multi-head self-attention and residual learning. To learn visual-semantic embedding, we tie-up two PIE-Nets and optimize them jointly in the multiple instance learning framework. Most existing work on cross-modal retrieval focuses on image-text data. Here, we also tackle a more challenging case of video-text retrieval. To facilitate further research in video-text retrieval, we release a new dataset of 50K video-sentence pairs collected from social media, dubbed MRW (my reaction when). We demonstrate our approach on both image-text and video-text retrieval scenarios using MS-COCO, TGIF, and our new MRW dataset.

Polysemous Visual-Semantic Embedding for Cross-Modal Retrieval: An Analytical Overview

This paper introduces Polysemous Visual-Semantic Embedding Networks (PIE-Nets), aimed at addressing challenges in cross-modal retrieval due to polysemy and partial cross-domain associations in data. The traditional method of mapping instances to a single point in a visual-semantic embedding space often fails when dealing with ambiguity, as seen in real-world data. By contrast, the authors propose a one-to-many mapping through PIE-Nets, facilitating diverse representations of visual and textual data.

The core innovation of this paper is the formulation of the PIE-Nets, which employ multi-head self-attention and residual learning to generate multiple embeddings per instance. By integrating both global and locally-guided features, PIE-Nets provide a richer, context-sensitive representation of instances. This contrasts with injective embeddings, which compress these complex representations into a single point, often overlooking subtle, yet critical, nuances inherent in polysemous data.

The linkage of two PIE-Nets, one dedicated to each modality, enables the simultaneous optimization of visual-semantic embeddings using a multiple instance learning (MIL) framework. This integration maximizes the usage of diverse instance representations, allowing for more robust retrieval, particularly in the presence of partial associations where not every aspect of the pair is directly linked.

The paper showcases its approach using the MS-COCO dataset for image-text retrieval tasks and introduces a new dataset named MRW for exploring video-text retrieval. The MRW dataset, consisting of 50,000 video-sentence pairs curated from social media, provides a fertile ground for testing the efficacy of the PIE-Nets in handling ambiguity and partial associations endemic to real-world scenarios. Through extensive experiments, the paper highlights the superior retrieval capabilities of the proposed architecture over established baselines, notably achieving impressive results in image-to-text tasks.

Empirically, the research asserts significant improvement through quantitative measures such as the Recall@$k$ and median rank metrics across MS-COCO, TGIF, and MRW datasets. These findings reinforce the adaptable nature of PIE-Nets and suggest broader applicability to various cross-modal retrieval tasks beyond initially presented datasets.

Additionally, the paper's comparative analysis with conventional methods such as DeViSE and VSE++ illustrates a marked improvement not only in retrieval accuracy but also in handling instances with weak or implicit concept associations. This positions PIE-Net as a potentially valuable tool for applications needing nuanced interpretability of multimedia content, augmenting tasks like automated video captioning and image tagging with enhanced precision and context-awareness.

The theoretical implications of this work suggest a reevaluation of the current paradigms in cross-modal retrieval. By demonstrating the utility of polysemous embeddings within a MIL framework, it questions the long-standing reliance on one-to-one mappings in visual-semantic tasks.

Looking forward, there are several intriguing research directions prompted by this paper. The extension of multi-head self-attention mechanisms to diverse representations opens the door to advancing neural architectures that better mimic human-like understanding of polysemous language and visuals. Furthermore, the introduction and further development of datasets such as MRW will likely fuel more tailored approaches to cross-modal retrieval challenges, emphasizing the need for addressing both explicit and implicit associations.

Overall, this paper makes a substantial contribution to the field of visual-semantic embedding by proposing a novel framework to tackle ambiguity in cross-modal retrieval tasks, yielding practical approaches and sparking further research opportunities in the field of artificial intelligence.