TRIPS: Efficient Vision-and-Language Pre-training with Text-Relevant Image Patch Selection
Chaoya Jiang, Haiyang Xu, Chenliang Li, Ming Yan, Wei Ye, Shikun Zhang, Bin Bi, Songfang Huang · 2022
Vision Transformers (ViTs) have been widely used in large-scale Vision and Language Pretraining (VLP) models.Though previous VLP works have proved the effectiveness of ViTs, they still suffer from computational efficiency brought by the long visual sequence.To tackle this problem, in this paper, we propose an efficient vision-and-language pre-training model with Text-Relevant Image Patch Selection, namely TRIPS, which reduces the visual sequence progressively with a text-guided patchselection layer in the visual backbone for efficient training and inference.The patchselection layer can dynamically compute textdependent visual attention to identify the attentive image tokens with text guidance and fuse inattentive ones in an end-to-end manner.Meanwhile, TRIPS does not introduce extra parameters to ViTs.Experimental results on a variety of popular benchmark datasets demonstrate that TRIPS gain a speedup of 40% over previous similar VLP models, yet with competitive or better downstream task performance.