3 citations · 3 across the 4 of their papers we have counts for
5 papers
MiniMax-M1: Scaling Test-Time Compute Efficiently with Lightning Attention
MiniMax, :, Aili Chen +125
We introduce MiniMax-M1, the world's first open-weight, large-scale hybrid-attention reasoning model. MiniMax-M1 is powered by a hybrid Mixture-of-Experts (MoE) architecture combin…
DBDH: A Dual-Branch Dual-Head Neural Network for Invisible Embedded Regions Localization
Chengxin Zhao, Hefei Ling, Sijing Xie +4
Embedding invisible hyperlinks or hidden codes in images to replace QR codes has become a hot topic recently. This technology requires first localizing the embedded region in the c…
NLHD: A Pixel-Level Non-Local Retinex Model for Low-Light Image Enhancement
Hao Hou, Yingkun Hou, Yuxuan Shi +2
Retinex model has been applied to low-light image enhancement in many existing methods. More appropriate decomposition of a low-light image can help achieve better image enhancemen…
Hands-on Guidance for Distilling Object Detectors
Yangyang Qin, Hefei Ling, Zhenghai He +2
Knowledge distillation can lead to deploy-friendly networks against the plagued computational complexity problem, but previous methods neglect the feature hierarchy in detectors. M…
Selective Convolutional Network: An Efficient Object Detector with Ignoring Background
Hefei Ling, Yangyang Qin, Li Zhang +2
It is well known that attention mechanisms can effectively improve the performance of many CNNs including object detectors. Instead of refining feature maps prevalently, we reduce…