HAMSA achieves 85.7% ImageNet-1K top-1 accuracy as a spectral-domain SSM with 2.2x faster inference and lower memory than transformers or scanning-based SSMs.
Focal loss for dense object detection
2 Pith papers cite this work. Polarity classification is still indexing.
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cs.CV 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
FruitEnsemble uses a weighted ensemble of backbones for top-3 candidates followed by MLLM arbitration on low-confidence samples to reach 70.49% accuracy on a new 306-class fruit dataset.
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HAMSA: Scanning-Free Vision State Space Models via SpectralPulseNet
HAMSA achieves 85.7% ImageNet-1K top-1 accuracy as a spectral-domain SSM with 2.2x faster inference and lower memory than transformers or scanning-based SSMs.
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FruitEnsemble: MLLM-Guided Arbitration for Heterogeneous ensemble in Fine-Grained Fruit Recognition
FruitEnsemble uses a weighted ensemble of backbones for top-3 candidates followed by MLLM arbitration on low-confidence samples to reach 70.49% accuracy on a new 306-class fruit dataset.