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Found 8 papers, 4 papers with code
Segue: Side-information Guided Generative Unlearnable Examples for Facial Privacy Protection in Real World
To address these concerns, researchers are actively exploring the concept of ``unlearnable examples", by adding imperceptible perturbation to data in the model training stage, which aims to prevent the model from learning discriminate features of the target face.
LLM-empowered Chatbots for Psychiatrist and Patient Simulation: Application and Evaluation
Empowering chatbots in the field of mental health is receiving increasing amount of attention, while there still lacks exploration in developing and evaluating chatbots in psychiatric outpatient scenarios.
Semantic Space Grounded Weighted Decoding for Multi-Attribute Controllable Dialogue Generation
Controlling chatbot utterance generation with multiple attributes such as personalities, emotions and dialogue acts is a practically useful but under-studied problem.
Symptom Identification for Interpretable Detection of Multiple Mental Disorders
Mental disease detection (MDD) from social media has suffered from poor generalizability and interpretability, due to lack of symptom modeling.
Psychiatric Scale Guided Risky Post Screening for Early Detection of Depression
Depression is a prominent health challenge to the world, and early risk detection (ERD) of depression from online posts can be a promising technique for combating the threat.
Can Audio Captions Be Evaluated with Image Caption Metrics?
Current metrics are found in poor correlation with human annotations on these datasets.
Diverse and Specific Clarification Question Generation with Keywords
Due to the variety of possible user backgrounds and use cases, the information need can be quite diverse but also specific to a detailed topic, while previous works assume generating one CQ per context and the results tend to be generic.
A unified sequence-to-sequence front-end model for Mandarin text-to-speech synthesis
In Mandarin text-to-speech (TTS) system, the front-end text processing module significantly influences the intelligibility and naturalness of synthesized speech.
