Object Grounding

Object grounding aims to connect textual descriptions or commands to corresponding objects or regions within images or videos, bridging the gap between language and vision. Current research focuses on improving grounding accuracy and robustness across diverse modalities (audio, visual, 3D), employing techniques like contrastive learning, transformer networks, and graph-based reasoning to enhance model performance. This work is crucial for advancing applications such as robotics, autonomous driving, and multimodal AI, enabling more sophisticated interaction between humans and machines through natural language. Furthermore, ongoing efforts address biases in existing models and evaluate the effectiveness of grounding in reducing hallucinations in large language models.