MLLM Based Vision Task

Multimodal large language models (MLLMs) are being increasingly applied to vision tasks, aiming to improve the understanding and processing of visual information, particularly in videos. Current research focuses on enhancing MLLM performance on video understanding by addressing challenges like long video processing and improving visual perception, often through techniques like causal cross-attention mechanisms and parameter-efficient fine-tuning of pre-trained models. These advancements are significant because they enable more robust and efficient solutions for various applications, including video question answering, geometric reasoning from images, and 3D perception from 2D inputs. The development of comprehensive benchmarks is also crucial for evaluating and comparing the performance of these models.