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cs.LG2024
MoE++: Accelerating Mixture-of-Experts Methods with Zero-Computation Experts
Peng Jin, Bo Zhu, Li Yuan +1
In this work, we aim to simultaneously enhance the effectiveness and efficiency of Mixture-of-Experts (MoE) methods. To achieve this, we propose MoE++, a general and heterogeneous…
cs.CV2024★ 3 cited
MoH: Multi-Head Attention as Mixture-of-Head Attention
Peng Jin, Bo Zhu, Li Yuan +1
In this work, we upgrade the multi-head attention mechanism, the core of the Transformer model, to improve efficiency while maintaining or surpassing the previous accuracy level. W…
cs.CV2024
Regressor-Segmenter Mutual Prompt Learning for Crowd Counting
Mingyue Guo, Li Yuan, Zhaoyi Yan +3
Crowd counting has achieved significant progress by training regressors to predict instance positions. In heavily crowded scenarios, however, regressors are challenged by uncontrol…