VideoMind: A Role-Based Agent for Temporal-Grounded Video Understanding

LLMS showed great capabilities in thinking tasks such as the Cot Series (COT), which enhances accuracy and interpretation in solving complex problems. While the researchers expand these capabilities to the multimedia fields, videos represent unique challenges due to the temporal dimension. Unlike fixed images, videos require understanding dynamic reactions over time. The current COT methods excel with fixed inputs, but they are struggling with the video content because they cannot localize specific moments or openly review the sequences. Humans overcome these challenges by breaking up complex problems, identifying and reconsidering the main moments, and synthesizing notes in coherent answers. This approach highlights the need for artificial intelligence systems to manage multiple thinking capabilities.

Developments of modern video understanding tasks such as illustrations and answering questions have improved, but models often lack visual correspondence and the ability to explain, especially for long videos. This video timeline deals with this by requesting accurate resettlement. Large multimedia models are trained in the struggle of instructions subject to supervision with complex thinking tasks. Two main Nesjan has emerged to address these restrictions: the agent -based facades and thinking models based on the pure text embodied by COT operations. Moreover, the techniques of searching for inference time are valuable in fields such as robots, games and navigation by allowing the processes to improve the outputs frequently without changing the basic weights.

Researchers from the University of Hong Kong Politchk and the exhibition laboratory, Singapore National University, Video, a video language agent designed to understand the time video. Video offers major innovations to face video thinking challenges. First, it determines the basic capabilities of the time thinking of the video and implement a working on roles with specialized components: scheme, founder, verified, and answering. Second, the Laura series strategy, which allows the replacement of non -welded roles through lightweight Lora transformers, and avoids general expenses of multiple models with efficiency and flexibility balance. Experiments through 14 general standards show performance on the latest model in various video understanding tasks.

Video depends on QWEN2-VL, as it combines the LLM spine with visible VIT-based encryption capable of dealing with dynamic resolution inputs. Its primary innovation is its strategy in the Lora series, which is dynamic in a dizziness transformer while inference by self -equation. Moreover, it contains four specialized components: (a) The planned, which coordinates all other roles and determines the function that must be called after that based on the query, (b) Gronder, which determines the relevant moments by identifying the answers resulting from starting and ending the responses, (either, answers, answering answers, parts set by GORONER or the entire video when the direct response is more Relationship.

In the grounding measures, the light-weight Video 2B is outperforming most comparative models, including Internet2-78B and Claude-3.5-Sonnet, with only GPT-4O width. However, the Video 7B version exceeds the GPT-4O, and achieves competitive general performance. In the following GQA standard, the 2B Modern 7B models match both of the agents based on the agent and the end, compared to the text -based solutions, such as Llovi, Langrepo and Sevila. Video shows an exceptional zero zero possibilities, as it outperforms all LLM ground grounding methods and achieving competitive results compared to the timelines who have been seized. Moreover, Videomind generally excels in QA via video tasks (Long), MLVU and LVBENCH, which indicates effective localization of the signal sectors before answering questions.

In this paper, the researchers presented Videomind, a great progress in time video thinking. It addresses the complex challenges of understanding the video through the progress of parents, combining a plan, founder, institution, answering, and an effective strategy for the Laura series to switch roles. Experiments confirm through three main areas, prepare for video questions, video timeline, and public video questions, Video Long Video Thinking Tasks for a long time, as accurate answers based on evidence. This work determines the basis for future developments in multimedia video agents and thinking capabilities, which opens new paths for the most complex video understanding systems.

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Sajjad Ansari is in the last year of the first university stage of Iit khargpur. As enthusiastic about technology, it turns into the practical applications of Amnesty International with a focus on understanding the impact of artificial intelligence techniques and their effects in the real world. It aims to clarify the concepts of complex artificial intelligence in a clear and accessible way.