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Found 9 papers, 2 papers with code
Dual Transformer Decoder based Features Fusion Network for Automated Audio Captioning
Experimental results show that LHDFF outperforms existing audio captioning models.
Towards Generating Diverse Audio Captions via Adversarial Training
Captions generated by existing models are generally faithful to the content of audio clips, however, these machine-generated captions are often deterministic (e. g., generating a fixed caption for a given audio clip), simple (e. g., using common words and simple grammar), and generic (e. g., generating the same caption for similar audio clips).
Visually-Aware Audio Captioning With Adaptive Audio-Visual Attention
Audio captioning aims to generate text descriptions of audio clips.
Automated Audio Captioning via Fusion of Low- and High- Dimensional Features
Moreover, in LHDFF, a new PANNs encoder is proposed called Residual PANNs (RPANNs) by fusing the low-dimensional feature from the intermediate convolution layer output and the high-dimensional feature from the final layer output of PANNs.
On Metric Learning for Audio-Text Cross-Modal Retrieval
We present an extensive evaluation of popular metric learning objectives on the AudioCaps and Clotho datasets.
Deep Neural Decision Forest for Acoustic Scene Classification
In this paper, we propose a novel approach for ASC using deep neural decision forest (DNDF).
Leveraging Pre-trained BERT for Audio Captioning
BERT is a pre-trained language model that has been extensively used in Natural Language Processing (NLP) tasks.
Diverse Audio Captioning via Adversarial Training
As different people may describe an audio clip from different aspects using distinct words and grammars, we argue that an audio captioning system should have the ability to generate diverse captions for a fixed audio clip and across similar audio clips.
Banzhaf Random Forests
Random forests are a type of ensemble method which makes predictions by combining the results of several independent trees.
