📝 SummarXiv

Abstract

Audio grounding, or speech-driven open-set object detection, aims to localize and identify objects directly from speech, enabling generalization beyond predefined categories. This task is crucial for applications like human-robot interaction where textual input is impractical. However, progress in this domain faces a fundamental bottleneck from the scarcity of large-scale, paired audio-image data, and is further constrained by previous methods that rely on indirect, text-mediated pipelines. In this paper, we introduce Speech-to-See (Speech2See), an end-to-end approach built on a pre-training and fine-tuning paradigm. Specifically, in the pre-training stage, we design a Query-Guided Semantic Aggregation module that employs learnable queries to condense redundant speech embeddings into compact semantic representations. During fine-tuning, we incorporate a parameter-efficient Mixture-of-LoRA-Experts (MoLE) architecture to achieve deeper and more nuanced cross-modal adaptation. Extensive experiments show that Speech2See achieves robust and adaptable performance across multiple benchmarks, demonstrating its strong generalization ability and broad applicability.