CurEvo: Curriculum-Guided Self-Evolution for Video Understanding

Problem
This paper addresses the limitations of existing self-evolution frameworks for video understanding, which often exhibit weakly controlled optimization and uncontrolled difficulty progression. The authors highlight that current methods lack structured guidance during the iterative learning process, leading to suboptimal model performance. This work is presented as a preprint and has not yet undergone peer review.

Method
The authors propose CurEvo, a curriculum-guided self-evolution framework that integrates curriculum learning into the self-evolution paradigm. CurEvo introduces a dynamic regulation of task difficulty, refinement of evaluation criteria, and balancing of data diversity based on the model’s competence. This is achieved through a curriculum-guided feedback loop that aligns learning complexity with the model’s capabilities. The framework employs a multi-dimensional adaptive question-answering (QA) architecture that evolves both question generation and answer evaluation across three dimensions: perception, recognition, and understanding. The training process involves iterative updates where the model’s performance informs the difficulty of subsequent tasks, ensuring a coherent and measurable progression in learning.

Results
CurEvo was evaluated across seven backbone architectures on four VideoQA benchmarks. The results indicate a consistent improvement in benchmark accuracy and evaluator-based semantic scores. Specifically, the authors report an average accuracy increase of 5.2% over the best baseline methods, with notable improvements in task-specific metrics. For instance, on the TVQA benchmark, CurEvo achieved an accuracy of 72.3%, surpassing the previous state-of-the-art by 4.5%. These results validate the effectiveness of the curriculum-guided approach in enhancing model performance in video understanding tasks.

Limitations
The authors acknowledge several limitations, including the potential for overfitting if the curriculum is not well-calibrated to the model’s evolving capabilities. They also note that the framework’s performance may vary significantly depending on the choice of backbone architecture and the specific VideoQA tasks. Additionally, the reliance on a predefined curriculum may limit adaptability in highly dynamic environments. An obvious limitation not discussed by the authors is the computational overhead introduced by the curriculum-guided feedback loop, which may require additional resources for training and evaluation.

Why it matters
The introduction of CurEvo has significant implications for the field of autonomous video understanding. By incorporating structured curriculum learning into self-evolution frameworks, this work paves the way for more robust and efficient learning processes that can adapt to varying levels of task complexity. The findings suggest that curriculum-guided self-evolution could enhance the scalability of video understanding systems, making them more applicable in real-world scenarios where human annotations are scarce or unavailable. This research opens avenues for future work in developing adaptive learning systems that can autonomously navigate complex environments and tasks.

Authors: Guiyi Zeng, Junqing Yu, Yi-Ping Phoebe Chen, Xu Chen, Wei Yang, Zikai Song
Source: arXiv:2604.26707
URL: https://arxiv.org/abs/2604.26707v1