17 citations · 29 across the 10 of their papers we have counts for
10 papers
Kimi K3: Open Frontier Intelligence
Kimi Team, Tongtong Bai, Yifan Bai +398
We introduce Kimi K3, a 2.8T parameter Mixture-of-Experts model with 104 billion activated parameters, native vision capabilities, and a 1-million-token context window. Kimi K3 is…
Towards Seamless Borders: A Method for Mitigating Inconsistencies in Image Inpainting and Outpainting
Xingzhong Hou, Jie Wu, Boxiao Liu +5
Image inpainting is the task of reconstructing missing or damaged parts of an image in a way that seamlessly blends with the surrounding content. With the advent of advanced genera…
See Further When Clear: Curriculum Consistency Model
Yunpeng Liu, Boxiao Liu, Yi Zhang +4
Significant advances have been made in the sampling efficiency of diffusion models and flow matching models, driven by Consistency Distillation (CD), which trains a student model t…
Brain-Inspired Efficient Pruning: Exploiting Criticality in Spiking Neural Networks
Shuo Chen, Boxiao Liu, Zeshi Liu +1
Spiking Neural Networks (SNNs) have gained significant attention due to the energy-efficient and multiplication-free characteristics. Despite these advantages, deploying large-scal…
Rethinking Robust Representation Learning Under Fine-grained Noisy Faces
Bingqi Ma, Guanglu Song, Boxiao Liu +1
Learning robust feature representation from large-scale noisy faces stands out as one of the key challenges in high-performance face recognition. Recent attempts have been made to…
TokenMix: Rethinking Image Mixing for Data Augmentation in Vision Transformers
Jihao Liu, Boxiao Liu, Hang Zhou +2
CutMix is a popular augmentation technique commonly used for training modern convolutional and transformer vision networks. It was originally designed to encourage Convolution Neur…
An Efficient Pruning Process with Locality Aware Exploration and Dynamic Graph Editing for Subgraph Matching
Zite Jiang, Boxiao Liu, Shuai Zhang +3
Subgraph matching is a NP-complete problem that extracts isomorphic embeddings of a query graph in a data graph . In this paper, we present a framework with three components…
FNAS: Uncertainty-Aware Fast Neural Architecture Search
Jihao Liu, Ming Zhang, Yangting Sun +4
Reinforcement learning (RL)-based neural architecture search (NAS) generally guarantees better convergence yet suffers from the requirement of huge computational resources compared…
Fast and Efficient Parallel Breadth-First Search with Power-law Graph Transformation
Zite Jiang, Tao Liu, Shuai Zhang +3
In the big data era, graph computing is widely used to exploit the hidden value in real-world graphs in various scenarios such as social networks, knowledge graphs, web searching,…
Utilizing the Instability in Weakly Supervised Object Detection
Yan Gao, Boxiao Liu, Nan Guo +4
Weakly supervised object detection (WSOD) focuses on training object detector with only image-level annotations, and is challenging due to the gap between the supervision and the o…