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Found 8 papers, 1 papers with code
VISREAS: Complex Visual Reasoning with Unanswerable Questions
The unique feature of this task, validating question answerability with respect to an image before answering, and the poor performance of state-of-the-art models inspired the design of a new modular baseline, LOGIC2VISION that reasons by producing and executing pseudocode without any external modules to generate the answer.
Self-Imagine: Effective Unimodal Reasoning with Multimodal Models using Self-Imagination
The potential of Vision-Language Models (VLMs) often remains underutilized in handling complex text-based problems, particularly when these problems could benefit from visual representation.
PARK: Parkinson's Analysis with Remote Kinetic-tasks
We present a web-based framework to screen for Parkinson's disease (PD) by allowing users to perform neurological tests in their homes.
Unmasking Parkinson's Disease with Smile: An AI-enabled Screening Framework
Parkinson's disease (PD) diagnosis remains challenging due to lacking a reliable biomarker and limited access to clinical care.
Using AI to Measure Parkinson's Disease Severity at Home
We present an artificial intelligence system to remotely assess the motor performance of individuals with Parkinson's disease (PD).
TextMI: Textualize Multimodal Information for Integrating Non-verbal Cues in Pre-trained Language Models
Our approach, TextMI, significantly reduces model complexity, adds interpretability to the model's decision, and can be applied for a diverse set of tasks while achieving superior (multimodal sarcasm detection) or near SOTA (multimodal sentiment analysis and multimodal humor detection) performance.
Detecting Parkinson's Disease From an Online Speech-task
In this paper, we envision a web-based framework that can help anyone, anywhere around the world record a short speech task, and analyze the recorded data to screen for Parkinson's disease (PD).
Integrating Multimodal Information in Large Pretrained Transformers
It does so by generating a shift to internal representation of BERT and XLNet; a shift that is conditioned on the visual and acoustic modalities.
