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ViLT: Vision-and-Language Transformer Without Convolution or Region Supervision
A key function of auditory cognition is the association of characteristic
sounds with their corresponding semantics over time. Humans attempting to
discriminate between fine-grained audio categories, often replay the same
discriminative sounds to increase their prediction confidence. We propose an
end-to-end attention-based architecture that through selective repetition
attends over the most discriminative sounds across the audio sequence. Our
model initially uses the full audio sequence and iteratively refines the
temporal segments replayed based on slot attention. At each playback, the
selected segments are replayed using a smaller hop length which represents
higher resolution features within these segments. We show that our method can
consistently achieve state-of-the-art performance across three
audio-classification benchmarks: AudioSet, VGG-Sound, and EPIC-KITCHENS-100.
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