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。
Owing to their powerful generalizability, pre-trained vision-language models such as Contrastive Language-Image Pre-training (CLIP) have lately gained traction as practical CL candidates. However, the domain mismatch between the pre-training and the downstream CL tasks often calls for finetuning of ...
Continual Generative training for Incremental prompt-Learning (CGIL):cgil Contrastive Language-Image Pre-Training (CLIP):clip(staticmethod with no learning). CODA-Prompt: COntinual Decomposed Attention-based Prompting for Rehearsal-Free Continual Learning (CODA-Prompt) -Requirespip install timm==0.9.8:co...
kNN-CLIP: Retrieval Enables Training-Free Segmentation on Continually Expanding Large Vocabularies [TMLR 2024] [paper] Background Adaptation with Residual Modeling for Exemplar-Free Class-Incremental Semantic Segmentation. [ECCV 2024] [paper] [code] Mitigating Background Shift in Class-Incremental Sem...
A: 这篇论文提出了一种名为CLAP4CLIP的方法,旨在解决持续学习(Continual Learning, CL)中的一些问题,特别是针对视觉-语言模型(Vision-Language Models, VLMs)。具体来说,它试图解决以下几个问题: 领域不匹配问题:预训练的视觉-语言模型(如CLIP)在进行下游任务(如持续学习任务)时,可能会遇到领域不匹配的问题。这...
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...
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 ...
Initially, before learning on any tasks, a model parameter is free to update, which corresponds to all bits of the parameter being free-to-flip. Shannon entropy, which is equivalent to the number of free- to-flip bits, is 20 at this point. Next, after training on the fir...
Bridging pre-trained models to continual learning: A hypernetwork based framework with parameter-efficient fine-tuning techniques Modern techniques of pre-training and fine-tuning have significantly improved the performance of models on downstream tasks. However, this improvement face... F Ding,C Xu,...
Zero-shot action recognition requires a strong ability to generalize from pre-training and seen classes to novel unseen classes. Similarly, continual learning aims to develop models that can generalize effectively and learn new tasks without forgetting the ones previously learned. The generalization goals...