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Found 8 papers, 4 papers with code
OSCaR: Object State Captioning and State Change Representation
To address these challenges, in this paper, we introduce the Object State Captioning and State Change Representation (OSCaR) dataset and benchmark.
Learning Audio Concepts from Counterfactual Natural Language
Conventional audio classification relied on predefined classes, lacking the ability to learn from free-form text.
Video Understanding with Large Language Models: A Survey
With the burgeoning growth of online video platforms and the escalating volume of video content, the demand for proficient video understanding tools has intensified markedly.
Separating Invisible Sounds Toward Universal Audiovisual Scene-Aware Sound Separation
The audio-visual sound separation field assumes visible sources in videos, but this excludes invisible sounds beyond the camera's view.
MISAR: A Multimodal Instructional System with Augmented Reality
Augmented reality (AR) requires the seamless integration of visual, auditory, and linguistic channels for optimized human-computer interaction.
Unveiling Cross Modality Bias in Visual Question Answering: A Causal View with Possible Worlds VQA
In this paper, we first model a confounding effect that causes language and vision bias simultaneously, then propose a counterfactual inference to remove the influence of this effect.
Cross Modal Global Local Representation Learning from Radiology Reports and X-Ray Chest Images
Extensive experiments confirm consistent results for classifying lung pathologies using the multimodal global local representations of language and vision information.
Re-defining Radiology Quality Assurance (QA) -- Artificial Intelligence (AI)-Based QA by Restricted Investigation of Unequal Scores (AQUARIUS)
Using AQUARIUS with NLP on final radiology reports and targeted expert neuroradiology review of only 29 discordantly classified cases reduced the human QA effort by 98. 5%, where we found a total of six non-reported true ICH+ cases, with radiologists' missed ICH detection rates of 0. 52% and 2. 5% for flagged and non-flagged cases, respectively.
