Papers by Sandro Pollastrini
Detecting and Mitigating Challenges in Zero-Shot Video Summarization with Video LLMs (2025.findings-acl)
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Luca Cagliero, Lorenzo Vaiani, Eliana Pastor, Alkis Koudounas, Elena Baralis, Vittorio Mazzia, Sandro Pollastrini, Thomas Gueudre, Manuel Giollo, Daniele Amberti, Yue Wu
| Challenge: | Video Large Language Models (VLLMs) exhibit impressive zero-shot capabilities in video analysis, but their performance varies significantly depending on the LLM prompt, the characteristics of the video, and the properties of the training data and LLM architecture. |
| Approach: | They propose to use Chain-of-Thought prompting to inject knowledge extracted by external, lightweight models into video summarization benchmarks to evaluate their performance. |
| Outcome: | The proposed solutions improve summarization performance by injecting knowledge extracted by external, lightweight models. |