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Found 12 papers, 4 papers with code
Toward Unsupervised Realistic Visual Question Answering
To resolve the first drawback, we propose a new testing dataset, RGQA, which combines AQs from an existing VQA dataset with around 29K human-annotated UQs.
Chatbots for Mental Health Support: Exploring the Impact of Emohaa on Reducing Mental Distress in China
In this study, we analyze the effectiveness of Emohaa in reducing symptoms of mental distress.
Measurement of carbon finance level and exploration of its influencing factors
Faced with increasingly severe environmental problems, carbon trading markets and related financial activities aiming at limiting carbon dioxide emissions are booming.
New Intent Discovery with Pre-training and Contrastive Learning
Existing approaches typically rely on a large amount of labeled utterances and employ pseudo-labeling methods for representation learning and clustering, which are label-intensive, inefficient, and inaccurate.
Fine-tuning Pre-trained Language Models for Few-shot Intent Detection: Supervised Pre-training and Isotropization
It is challenging to train a good intent classifier for a task-oriented dialogue system with only a few annotations.
Effectiveness of Pre-training for Few-shot Intent Classification
This paper investigates the effectiveness of pre-training for few-shot intent classification.
Low-Dose CT Denoising Using a Structure-Preserving Kernel Prediction Network
Low-dose CT has been a key diagnostic imaging modality to reduce the potential risk of radiation overdose to patient health.
Fast and Accurate Quantized Camera Scene Detection on Smartphones, Mobile AI 2021 Challenge: Report
To address this problem, we introduce the first Mobile AI challenge, where the target is to develop quantized deep learning-based camera scene classification solutions that can demonstrate a real-time performance on smartphones and IoT platforms.
KuraNet: Systems of Coupled Oscillators that Learn to Synchronize
Here, we present a single approach to both of these problems in the form of "KuraNet", a deep-learning-based system of coupled oscillators that can learn to synchronize across a distribution of disordered network conditions.
Texture and Shape Biased Two-Stream Networks for Clothing Classification and Attribute Recognition
We also find that texture features have an impelling effect on these tasks and that the pre-trained ImageNet model has good performance in extracting texture features.
Dataset of Segmented Nuclei in Hematoxylin and Eosin Stained Histopathology Images of 10 Cancer Types
To address this, we developed an analysis pipeline that segments nuclei in whole slide tissue images from multiple cancer types with a quality control process.
Predict Future Sales using Ensembled Random Forests
This is a method report for the Kaggle data competition 'Predict future sales'.
