MLLM Frame Subset Ensembling for Audio-Visual Video QA and MLLM-based Reranking for Ad-hoc Video Search in TRECVID 2025

This paper presents the overview of the runs related to the Ad-hoc Video Search (AVS) and Video Question Answering (VQA) tracks of TRECVID 2025 on behalf of the CERTH-ITI team. For the AVS track, we introduce a two-stage framework built on foundation models. In the first stage, multiple vision-language models (VLMs) encode both the input query, augmented through LLM-generated rephrasings, and the candidate video shots, producing weighted similarity scores for initial retrieval. In the second stage, we utilize a Multimodal-LLM(MLLM)-based reranking module that evaluates the semantic alignment between each shot among the top-N highest-ranked ones and the original query, generating updated relevance scores for reordering these shots. This MLLM-driven reranking significantly improves contextual matching and produces more accurate final rankings without requiring any model training. Regarding the VQA track, we fine-tune an audio-visual MLLM model on the provided TRECVID training dataset and we implement an inference-time scaling technique to enhance the multimodal understanding capabilities of the MLLM. For the open-ended Answer Generation (AG) task, we aggregate multiple model responses per question via a majority vote. The responses are generated with greedy sampling from different random frame subsets of the video and they are ranked based on the number of votes. For the Multiple-Choice (MC) task, instead of voting, we use mean pooling on the logits assigned by the fine-tuned model to each candidate response. Through the combination of fine-tuning and frame subset ensembling we achieve the highest score across 3 metrics in the VQA AG task and the second highest in the VQA MC task.