VECA learns effective visual representations using core-periphery attention where patches interact exclusively via a resolution-invariant set of learned core embeddings, achieving linear O(N) complexity while maintaining competitive performance.
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DASCN uses a unified primal-dual GAN architecture to generate semantics-consistent visual features for generalized zero-shot learning, claiming state-of-the-art gains.
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Elastic Attention Cores for Scalable Vision Transformers
VECA learns effective visual representations using core-periphery attention where patches interact exclusively via a resolution-invariant set of learned core embeddings, achieving linear O(N) complexity while maintaining competitive performance.
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Dual Adversarial Semantics-Consistent Network for Generalized Zero-Shot Learning
DASCN uses a unified primal-dual GAN architecture to generate semantics-consistent visual features for generalized zero-shot learning, claiming state-of-the-art gains.