8 citations · 15 across the 11 of their papers we have counts for
11 papers
The MiniMax-M2 Series: Mini Activations Unleashing Max Real-World Intelligence
Aili Chen, Aonian Li, Baichuan Zhou +215
We introduce the MiniMax-M2 series, a family of Mixture-of-Experts language models built around the principle that mini activations can unleash maximum real-world intelligence. The…
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…
Reducing Fine-Tuning Memory Overhead by Approximate and Memory-Sharing Backpropagation
Yuchen Yang, Yingdong Shi, Cheems Wang +3
Fine-tuning pretrained large models to downstream tasks is an important problem, which however suffers from huge memory overhead due to large-scale parameters. This work strives to…
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…
MARformer: An Efficient Metal Artifact Reduction Transformer for Dental CBCT Images
Yuxuan Shi, Jun Xu, Dinggang Shen
Cone Beam Computed Tomography (CBCT) plays a key role in dental diagnosis and surgery. However, the metal teeth implants could bring annoying metal artifacts during the CBCT imagin…
Parameter-Free Channel Attention for Image Classification and Super-Resolution
Yuxuan Shi, Lingxiao Yang, Wangpeng An +2
The channel attention mechanism is a useful technique widely employed in deep convolutional neural networks to boost the performance for image processing tasks, eg, image classific…
Improving the Transferability of Adversarial Attacks on Face Recognition with Beneficial Perturbation Feature Augmentation
Fengfan Zhou, Hefei Ling, Yuxuan Shi +3
Face recognition (FR) models can be easily fooled by adversarial examples, which are crafted by adding imperceptible perturbations on benign face images. The existence of adversari…
Semi-Supervised Contrastive Learning for Remote Sensing: Identifying Ancient Urbanization in the South Central Andes
Jiachen Xu, Junlin Guo, James Zimmer-Dauphinee +7
Archaeology has long faced fundamental issues of sampling and scalar representation. Traditionally, the local-to-regional-scale views of settlement patterns are produced through sy…
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…