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arXiv · 2606.10887

Listen, Look, and Learn: Learning Without Forgetting through SAM-Audio

Avi Gupta·Nilotpal Sinha·Vishnu Raj·Sambuddha Saha·Pratik Joshi·Koteswar Rao Jerripothula·Tammam Tillo
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Abstract

Class-Incremental Learning (CIL) aims to continuously learn new classes without forgetting previously acquired knowledge. While recent CIL advances have spurred significant interest across various modalities, the audio-visual setting remains underexplored. Furthermore, although foundational multimodal models like SAM-Audio encapsulate rich static priors, our empirical analysis reveals that these representations struggle in incremental settings. This work bridges this gap by integrating SAM-Audio's audio-visual priors into the CIL setting. Specifically, we leverage its dense audio and visual representations and employ a novel guided attention strategy where the audio features contextually guide the visual representations. To further mitigate catastrophic forgetting, we introduce dual-level distillation objectives at both the feature and logit levels. Extensive evaluations on audio-visual CIL benchmarks demonstrate that our approach consistently outperforms state-of-the-art methods.

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Avi Gupta, Nilotpal Sinha, Vishnu Raj, Sambuddha Saha, Pratik Joshi, Koteswar Rao Jerripothula, Tammam Tillo. 2026-06-09. Listen, Look, and Learn: Learning Without Forgetting through SAM-Audio. https://arxiv.org/abs/2606.10887

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