VQA

The paper highlights the importance of detecting dirty samples in datasets used for Data-centric AI, such as poisoned samples or noisy labels, which compromise the reliability of DNNs. To address this, we propose the Versatile Data Cleanser (VDC), a novel approach leveraging multimodal large language models to identify inconsistencies between images and their labels, demonstrating its effectiveness across various types of dirty samples.

In order to achieve a general visual question answering (VQA) system, it is essential to learn to answer deeper questions that require …

Knowledge-based Visual Question Answering (KVQA) requires external knowledge beyond the visible content to answer questions about an …

The paper introduces a multi-modal heterogeneous graph-based approach with a modality-aware graph convolutional network for Fact-based Visual Question Answering, enhancing the selection and aggregation of relevant evidence across modalities, thereby achieving state-of-the-art performance and interpretability.

Visual Dialogue task requires an agent to be engaged in a conversation with human about an image. The ability of generating detailed …