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In this paper, we introduce audio-visual class-incremental learning, a class-incremental learning scenario for audio-visual video recognition.
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Dual-modality seq2seq network for audio-visual event localization
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Watch, listen and tell: Multi-modal weakly supervised dense event captioning
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Audio-visual event localization in the wild
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Large scale incremental learning
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Soundspaces: Audio-visual navigation in 3d environments
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Der: Dynamically expandable representation for class incremental learning
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Class-incremental learning via dual augmentation
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Prototype augmentation and self-supervision for incremental learning
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Sound localization by self-supervised time delay estimation
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Overcoming catastrophic forgetting in incremental object detection via elastic response distillation
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Self-supervised models are continual learners
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Masked autoencoders are scalable vision learners
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Temporal and cross-modal attention for audio-visual zero-shot learning
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Localizing visual sounds the easy way
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Multi-modal grouping network for weakly-supervised audio-visual video parsing
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ALIFE: Adaptive logit regularizer and feature replay for incremental semantic segmentation
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The challenges of continuous self-supervised learning
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FOSTER: feature boosting and compression for class-incremental learning
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Learning in audio-visual context: A review, analysis, and new perspective
Yake Wei, Di Hu, Yapeng Tian, and Xuelong Li · 2022
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Generative negative text replay for continual vision-language pretraining
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Contrastive audio-visual masked autoencoder
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Attribution-aware weight transfer: A warm-start initialization for class-incremental semantic segmentation
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