Abstract:We propose Compressed Video Aggregator (CVA), a lightweight micro-video recommendation module that decouples video information from preference learning. It aggregates frozen VFM embeddings, and uses latent reasoning without cross-attention projection, producing compact video embeddings for recommenders. Due to the redundancy in the frame count of the original benchmark and its overly coarse sampling, we used titles to re-select key frames based on CLIP. Experiments on MicroLens and Short-Video show consistent gains with orders-of-magnitude reductions in training time and GPU memory, and re-selected frames can further enhance the performance of all methods, including CVA. Furthermore, we also discussed the impact of several scenarios involving erroneous titles on our method. Code will be released soon.
| Comments: | 18 pages |
| Subjects: | Machine Learning (cs.LG); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2605.08810 [cs.LG] |
| (or arXiv:2605.08810v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2605.08810 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Yang Xiao [view email]
[v1]
Sat, 9 May 2026 09:02:20 UTC (573 KB)