In this regard, despite the recent developments in Vid-LLMs, the analysis of videos through frame-based processing using image-focused MLLMs remains a compelling alternative to end-to-end video understanding (Huang, et al., 2024). Although Vid-LLMs theoretically provide more complete temporal analyses for short videos, they are computationally expensive for long videos and have problems with long-range dependencies (Tang et al., 2023, pp. 13–14). In this regard, Meinardus et al. demonstrated the efficacy of image-text MLLM for video moment retrieval (Mr. BLIB), emphasising the benefits of a frame-based approach without the need for resource-intensive pretraining, precise localisation of relevant moments, and flexible processing of longer videos through the use of key frames (Meinardus, et al., 2024).