There are two main challenges in learning visual representations: the computational inefficiency of Vision Transformers (ViTs) and the limited ability of convolutional neural networks (CNNs) to capture global contextual information. ViTs suffer from quadratic computational complexity while excelling in international receptive field and tuning capabilities. On the other hand, CNNs offer scalability and linear complexity in terms of image resolution, but lack the dynamic weighting and global perspective of ViTs. These issues highlight the need for a model that brings together the strengths of CNNs and ViTs without inheriting their respective computational and representational limitations.
There is important research on the evolution of visual perception of machines. CNNs and ViT have become dominant graphical basic models with unique strengths in visual information processing. State space models (SSM) have gained prominence for their efficiency in modeling long sequences, influencing both NLP and computer vision domains.
A team of UCAS researchers, in collaboration with Huawei Inc. and Pengcheng Lab, introduced the Visual Space State Model (VMamba), a novel architecture for visual representation learning. VMamba is inspired by the state space model and aims to address the computational inefficiencies of ViTs while retaining their advantages such as global receptive fields and dynamic weights. The research emphasizes VMamba's innovative approach to address the direction-sensitive problem in visual data processing, proposing the cross scan module (CSM) for efficient spatial traversal.
CSM is used to transform visual images into sequences of patches and uses a 2D state space model as its core. VMamba's selective scanning mechanism and discretization process enhance its capabilities. The effectiveness of the model is validated through extensive experiments, comparing its effective receptive fields with models such as ResNet50 and ConvNeXt-T and its performance in semantic segmentation on the ADE20K dataset.
Looking at the details of VMamba's remarkable performance on various benchmarks, it achieved 48.5-49.7 mAP in object detection and 43.2-44.0 mIoU in instance segmentation on the COCO dataset, outperforming the established models. On the ADE20K dataset, the VMamba-T model achieved 47.3 mIoU and 48.3 mIoU with multi-scale inputs in semantic segmentation, outperforming competitors such as ResNet, DeiT, Swin, and ConvNeXt, as mentioned above. It also showed superior accuracy in semantic segmentation at various input resolutions. The comparative analysis highlighted the global effective receptive fields of VMamba, distinguishing it from other models with local ERFs.
The research on VMamba marks a significant leap in visual representation learning. It successfully integrates the strengths of CNN and ViT, offering a solution to their limitations. The novel CSM improves the efficiency of VMamba, making it adept at handling various visual tasks with improved computational effectiveness. This model demonstrates robustness across multiple benchmarks and suggests a new direction for future developments in basic graphical models. VMamba's approach to maintaining global receptive fields while ensuring linear complexity underscores its potential as an innovative tool in computer vision.
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Nikhil is an internal consultant at Marktechpost. He is pursuing an integrated double degree in Materials at the Indian Institute of technology Kharagpur. Nikhil is an ai/ML enthusiast who is always researching applications in fields like biomaterials and biomedical science. With a strong background in materials science, he is exploring new advances and creating opportunities to contribute.
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