Visual Disambiguation

Visual disambiguation focuses on resolving uncertainties in interpreting visual data, particularly when faced with ambiguous or similar-looking objects or scenes. Current research emphasizes developing robust models that integrate visual and linguistic information, employing techniques like hybrid architectures combining large language models with transformers and graph neural networks, to improve accuracy and handle complex scenarios. This work is driven by the need for more reliable and adaptable systems in human-robot interaction and 3D reconstruction, where resolving visual ambiguities is crucial for successful task completion. The development of large-scale datasets for benchmarking and interactive disambiguation methods further highlights the field's growing importance.