4 accepted papers
Recently, vision-language models (e.g., CLIP) are increasingly adopted for continual learning to mitigate catastrophic forgetting. However, existing CLIP-based methods typically freeze the backbone to preserve pre-trained knowledge, which limits the model's ability to learn discriminative features f…
Continual learning aims to learn multiple tasks sequentially while preserving prior knowledge, but faces the challenge of catastrophic forgetting when adapting to new tasks. Recently, approaches leveraging pre-trained models have gained increasing popularity in mitigating this issue, due to the stro…
With the increasing amount of multimedia data, cross-modality hashing has made great progress as it achieves sub-linear search time and low memory space. However, due to the huge discrepancy between different modalities, most existing cross-modality hashing methods cannot learn unified hash codes an…
The Adam algorithm has become extremely popular for large-scale machine learning. Under convexity condition, it has been proved to enjoy a data-dependent $O(\sqrt{T})$ regret bound where $T$ is the time horizon. However, whether strong convexity can be utilized to further improve the performance rem…