Visual Dialog

Visual dialog research focuses on building AI agents capable of engaging in multi-round conversations about images, requiring them to understand both visual and textual information and maintain contextual coherence. Current research emphasizes improving model architectures, such as transformers and large language models, to better handle complex dialog history, resolve coreferences (pronouns and other references), and generate more accurate and natural-sounding responses. This field is significant for advancing multimodal understanding in AI, with potential applications in human-robot interaction, image captioning, and question answering systems. A key challenge remains developing robust and generalizable models that can handle the ambiguities and complexities inherent in natural language conversations grounded in visual contexts.