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School of Computer Science and Technology, Soochow University
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Low-light crowd counting accuracy is revolutionized by a novel multi-modal approach that leverages depth and edge information, outperforming existing methods.
RT-Counter achieves 7.4x faster performance in object counting while maintaining competitive accuracy, breaking the traditional accuracy-speed trade-off.
MambaCount achieves state-of-the-art object counting accuracy with linear complexity, challenging the dominance of Transformer-based methods in dense visual environments.
Ling-2.6 and Ring-2.6 achieve unprecedented efficiency in agentic intelligence, enabling instant responses and deep reasoning at trillion-parameter scale.
