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NVIDIA’s Global Context ViT Achieves SOTA Performance on CV Tasks Without Expensive Computation
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In the new paper Global Context Vision Transformers, an NVIDIA research team proposes the Global Context Vision Transformer, a novel yet simple hierarchical ViT architecture comprising global self-attention and token generation modules that enables the efficient modelling of both short- and long-range dependencies without costly compute operations while achieving SOTA results across various computer vision tasks.
The post NVIDIA’s Global Context ViT Achieves SOTA Performance on CV Tasks Without Expensive Computation first appeared on Synced.
ai artificial intelligence computation computer vision & graphics context cv deep-neural-networks global machine learning machine learning & data science ml nvidia performance research sota technology transformer vision-transformer