Year: 2026
Type: article
Abstract: The visual pathway of human brain includes two sub-pathways,i.e., the ventral pathway and the dorsal pathway, which focus on object identification and dynamic information modeling, respectively. Both pathways comprise multi-layer structures, with each layer responsible for processing different aspects of visual information. Inspired by the human visual information processing mechanism, we propose the Brain Inspired Masked Modeling (BIMM) framework, aiming to learn comprehensive representations from videos. Specifically, our approach consists of ventral and dorsal branches, which learn image and video representations, respectively. Both branches employ the Vision Transformer (ViT) as their backbone and are trained through a masked modeling method. To emulate the distinct functions of the visual cortices, we segment the encoder of each branch into three intermediate blocks and reconstruct progressive prediction targets with light weight decoders. Furthermore, drawing inspiration from the information-sharing mechanism in the brain’s visual pathways, we introduce a partial parameter sharing strategy between the branches during training. Extensive experiments demonstrate that BIMM achieves superior performance compared to the state-of-the-art methods.
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
Funder: Beijing Nova Program
Award: G1689576601
OpenAlex ID: W7154203313