Complex video question-answering (VQA) requires in-depth understanding of video contents including object and action recognition as well as video classification and summarization, which exhibits great potential in emerging applications in education and entertainment, etc. Multimodal large language models (MLLMs) may accomplish this task by grasping the intention of a question and decomposing it to a series of visual recognition sub-tasks to find out the answer with the help of an agent. To tackle this task, we first collect a new dedicated Complex VQA dataset named CVQA and then propose VQAGuider, an innovative framework planning a few atomic visual recognition tools by video-related API matching. VQAGuider facilitates a deep engagement with video content and precise responses to complex video-related questions by MLLMs, which is beyond aligning visual and language features for simple VQA tasks. Our experiments demonstrate VQAGuider is capable of navigating the complex VQA tasks by MLLMs and improves the accuracy by 29.6% and 17.2% on CVQA and the existing VQA datasets, respectively, highlighting its potential in advancing MLLMs’s capabilities in video understanding.

In this paper, we address the challenge of adapting vision-language models (VLMs) to few-shot image recognition in a training-free manner. We observe that existing methods are not able to effectively characterize the semantic relationship between support and query samples in a training-free setting. We recognize that, in the semantic feature space, the feature of the query image is a linear and sparse combination of support image features since support-query pairs are from the class and share the same small set of distinctive visual attributes. Motivated by this interesting observation, we propose a novel method called Training-free Feature ReConstruction with Sparse optimization (TaCo), which formulates the few-shot image recognition task as a feature reconstruction and sparse optimization problem. Specifically, we exploit the VLM to encode the query and support images into features. We utilize sparse optimization to reconstruct the query feature from the corresponding support features. The feature reconstruction error is then used to define the reconstruction similarity. Coupled with the text-image similarity provided by the VLM, our reconstruction similarity analysis accurately characterizes the relationship between support and query images. This results in significantly improved performance in few-shot image recognition. Our extensive experimental results on few-shot recognition demonstrate that our method outperforms existing state-of-the-art approaches by substantial margins.