TiC-CLIP: Continual Training of CLIP Models 2024 ICLR Hierarchical Prompts for Rehearsal-free Continual Learning 2024 Arxiv KOPPA: Improving Prompt-based Continual Learning with Key-Query Orthogonal Projection and Prototype-based One-Versus-All 2023 Arxiv RanPAC: Random Projections and Pre-trained Mode...
Continual learning可以帮助预训练视觉模型不需要训练地有效泛化到下游任务上,然而Clip的zero-shot能力在灾难性遗忘后有很明显的下降,现在已有的Continual learning方法可以通过replay 之前的数据达到阻止遗忘,但是由于Clip的数据集是私密的,这种方法行不通。除此之外,尽管repaly可以增强表现,但是也会损害zero-shot的能力。
Following cammand lines are examples of training and evaluating the model. #train from clip modelpython -m src.main \ --train-mode=whole \ --train-dataset=DTD \ --lr=1e-5\ --ls 0.2 \ --iterations 1000 \ --method ZSCL \ --image_loss \ --text_loss \ --we \ --avg_freq 100...
Recently, pre-trained vision-language models such as CLIP, with powerful generalization ability, have been gaining traction as practical CL candidates. However, the domain mismatch between the pre-training and the downstream CL tasks calls for finetuning of the CLIP on the latter. The deterministic...
Notably and following the core ideas of the vCLIMB [47] benchmark, PIVOT does not rely on any in-distribution pre-training (a common feature of prompting methods for CL [45,50]). Rather, it leverages the vast and general visual knowledge contained in the CLIP visual encoder (trained on ...
Vision-Language Models for Downstream Tasks. Many works propose different training strategies of vision-language models for better performance on down- stream tasks, such as CoOp [64], CLIP-Adapter [15] and WiSE-FT [58]. However, very few attemp...
Our approach maintains constant memory complexity to the number of models, minimizes interference between tasks through orthogonal projections, and retains the performance of previously merged models through adaptive task vector scaling. Extensive experiments on CLIP-ViT models demonstrate that our method ...
The class alignment is achieved by performing a self-training of the target domain where a pseudo-labelling process is committed to highly confident samples of the target domain. As with Li et al. and Zheng et al. [11], [12], the meta-learning-based regularization approach is applied to ...
Fig. 2. Overview of proposed CavRL. We use a Siamese network to handle the multimodal data, in which two separate encoders extract the audio and visual representations. In Task Tt, the memory buffer M stores a small amount of data from previous tasks as additions of new training data Dt...
Continual Learning of Foundation Models (CLFM) This project includes the PyTorch implementations of the following papers✨: Embracing Language Inclusivity and Diversity in CLIP Through Continual Language Learning Bang Yang, Yong Dai, Xuxin Cheng, Yaowei Li, Asif Raza, Yuexian Zou ...