Giant Network AI Lab, Shanghai, China
Research
OCC-MLLM-Alpha:Empowering Multi-modal Large Language Model for the Understanding of Occluded Objects with Self-Supervised Test-Time Learning
Author(s): Shuxin Yang* and Xinhan Di
There is a gap in the understanding of occluded objects in existing large-scale visual language multi-modal models. Current state of the art multi modal models fail to provide satisfactory results in describing occluded objects through universal visual encoders and supervised learning strategies. Therefore, we introduce a multi-modal large language framework and corresponding self-supervised learning strategy with support of 3D generation. We start our experiments comparing with the state of the art models in the evaluation of a large scale dataset SOM Video. The initial results demonstrate the improvement of 16.92% in comparison with the state of the art VLM models.details summary { cursor: pointer; color: #f8f9fa; } a { font-size: 11px; opacity: 0.5; }References
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