Visual-Semantic Decomposition and Partial Alignment for Document-based Zero-Shot Learning
This work addresses a bottleneck in document-based zero-shot learning for computer vision applications, offering an incremental improvement over existing methods.
The paper tackles the problem of suboptimal alignment between documents and images in zero-shot learning by proposing a network that extracts multi-view semantic concepts and aligns only matching parts, achieving state-of-the-art performance on three standard benchmarks with consistent improvements.
Recent work shows that documents from encyclopedias serve as helpful auxiliary information for zero-shot learning. Existing methods align the entire semantics of a document with corresponding images to transfer knowledge. However, they disregard that semantic information is not equivalent between them, resulting in a suboptimal alignment. In this work, we propose a novel network to extract multi-view semantic concepts from documents and images and align the matching rather than entire concepts. Specifically, we propose a semantic decomposition module to generate multi-view semantic embeddings from visual and textual sides, providing the basic concepts for partial alignment. To alleviate the issue of information redundancy among embeddings, we propose the local-to-semantic variance loss to capture distinct local details and multiple semantic diversity loss to enforce orthogonality among embeddings. Subsequently, two losses are introduced to partially align visual-semantic embedding pairs according to their semantic relevance at the view and word-to-patch levels. Consequently, we consistently outperform state-of-the-art methods under two document sources in three standard benchmarks for document-based zero-shot learning. Qualitatively, we show that our model learns the interpretable partial association.