Test-time Distribution Learning Adapter for Cross-modal Visual Reasoning

arXiv:2403.06059v1 Announce Type: new
Abstract: Vision-Language Pre-Trained (VLP) models, such as CLIP, have demonstrated remarkable effectiveness in learning generic visual representations. Several approaches aim to efficiently adapt VLP models to downstream tasks with limited supervision, aiming to leverage the acquired knowledge from VLP models. However, these methods suffer from either introducing biased representations or requiring high computational complexity, which hinders their effectiveness in fine-tuning the CLIP model. Moreover, when a model is trained on data specific to a particular domain, …

abstract acquired adapt adapter aim arxiv clip cs.cv distribution however knowledge language modal reasoning supervision tasks test type vision visual