OCC-MLLM:Empowering Multimodal Large Language Model For the Understanding of Occluded Objects
<|reference_start|>OCC-MLLM:Empowering Multimodal Large Language Model For the Understanding of Occluded Objects: There is a gap in the understanding of occluded objects in existing large-scale visual language multi-modal models. Current state-of-the-art multimodal models fail to provide satisfactory results in describing occluded objects for visual-language multimodal models through universal visual encoders. Another challenge is the limited number of datasets containing image-text pairs with a large number of occluded objects. Therefore, we introduce a novel multimodal model that applies a newly designed visual encoder to understand occluded objects in RGB images. We also introduce a large-scale visual-language pair dataset for training large-scale visual-language multimodal models and understanding occluded objects. We start our experiments comparing with the state-of-the-art models.<|reference_end|>
@article{qiu2024occ-mllm:empowering,
    title={OCC-MLLM:Empowering Multimodal Large Language Model For the
  Understanding of Occluded Objects},
    author={Wenmo Qiu, Xinhan Di},
    journal={arXiv preprint arXiv:2410.01261},
    year={2024},
    archivePrefix={arXiv},
    eprint={2410.01261},
    primaryClass={cs.CV}
}