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We introduce a novel architecture design that enhances expressiveness byincorporating multiple head classifiers (\ie, classification heads) instead ofrelying on channel expansion or additional building blocks. Our approachemploys attention-based aggregation, utilizing pairwise feature similarity toenhance multiple lightweight heads with minimal resource overhead. We computethe Gramian matrices to reinforce class tokens in an attention layer for eachhead. This enables the heads to learn more discriminative representations,enhancing their aggregation capabilities. Furthermore, we propose a learningalgorithm that encourages heads to complement each other by reducingcorrelation for aggregation. Our models eventually surpass state-of-the-artCNNs and ViTs regarding the accuracy-throughput trade-off on ImageNet-1K anddeliver remarkable performance across various downstream tasks, such as COCOobject instance segmentation, ADE20k semantic segmentation, and fine-grainedvisual classification datasets. The effectiveness of our framework issubstantiated by practical experimental results and further underpinned bygeneralization error bound. We release the code publicly at:https://github.com/Lab-LVM/imagenet-models.

Gramian Attention Heads are Strong yet Efficient Vision Learners