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Found 7 papers, 3 papers with code
LEMON: Learning 3D Human-Object Interaction Relation from 2D Images
Which underexploit certain correlations between the interaction counterparts (human and object), and struggle to address the uncertainty in interactions.
Adapting OpenAI's Whisper for Speech Recognition on Code-Switch Mandarin-English SEAME and ASRU2019 Datasets
The Mixed Error Rate results show that the amount of adaptation data may be as low as $1\sim10$ hours to achieve saturation in performance gain (SEAME) while the ASRU task continued to show performance with more adaptation data ($>$100 hours).
Grounding 3D Object Affordance from 2D Interactions in Images
Comprehensive experiments on PIAD demonstrate the reliability of the proposed task and the superiority of our method.
Speech-text based multi-modal training with bidirectional attention for improved speech recognition
To let the state-of-the-art end-to-end ASR model enjoy data efficiency, as well as much more unpaired text data by multi-modal training, one needs to address two problems: 1) the synchronicity of feature sampling rates between speech and language (aka text data); 2) the homogeneity of the learned representations from two encoders.
Hierarchical Softmax for End-to-End Low-resource Multilingual Speech Recognition
Low-resource speech recognition has been long-suffering from insufficient training data.
Self-supervision of Feature Transformation for Further Improving Supervised Learning
In this paper we find that features in CNNs can be also used for self-supervision.
Self-supervised Feature Enhancement: Applying Internal Pretext Task to Supervised Learning
In this paper, we show that feature transformations within CNNs can also be regarded as supervisory signals to construct the self-supervised task, called \emph{internal pretext task}.
