Zobrazeno 1 - 10
of 823
pro vyhledávání: '"Li, Chenghao"'
In industrial anomaly detection, model efficiency and mobile-friendliness become the primary concerns in real-world applications. Simultaneously, the impressive generalization capabilities of Segment Anything (SAM) have garnered broad academic attent
Externí odkaz:
http://arxiv.org/abs/2402.19145
Unrestricted Error-Type Codebook Generation for Error Correction Code in DNA Storage Inspired by NLP
Recently, DNA storage has surfaced as a promising alternative for data storage, presenting notable benefits in terms of storage capacity, cost-effectiveness in maintenance, and the capability for parallel replication. Mathematically, the DNA storage
Externí odkaz:
http://arxiv.org/abs/2401.15915
We revisit the relationship between attention mechanisms and large kernel ConvNets in visual transformers and propose a new spatial attention named Large Kernel Convolutional Attention (LKCA). It simplifies the attention operation by replacing it wit
Externí odkaz:
http://arxiv.org/abs/2401.05738
Traditional clustering algorithms often struggle to capture the complex relationships within graphs and generalise to arbitrary clustering criteria. The emergence of graph neural networks (GNNs) as a powerful framework for learning representations of
Externí odkaz:
http://arxiv.org/abs/2312.14847
Autor:
Li, Chenghao, Wu, Yifei, Shen, Wenbo, Zhao, Zichen, Chang, Rui, Liu, Chengwei, Liu, Yang, Ren, Kui
Rust programming language is gaining popularity rapidly in building reliable and secure systems due to its security guarantees and outstanding performance. To provide extra functionalities, the Rust compiler introduces Rust unstable features (RUF) to
Externí odkaz:
http://arxiv.org/abs/2310.17186
While diffusion models demonstrate a remarkable capability for generating high-quality images, their tendency to `replicate' training data raises privacy concerns. Although recent research suggests that this replication may stem from the insufficient
Externí odkaz:
http://arxiv.org/abs/2309.07254
Autor:
Li, Chenghao, Zhang, Chaoning
The success of Vision Transformer (ViT) has been widely reported on a wide range of image recognition tasks. ViT can learn global dependencies superior to CNN, yet CNN's inherent locality can substitute for expensive training resources. Recently, the
Externí odkaz:
http://arxiv.org/abs/2309.05375
In the realm of multi-agent reinforcement learning, intrinsic motivations have emerged as a pivotal tool for exploration. While the computation of many intrinsic rewards relies on estimating variational posteriors using neural network approximators,
Externí odkaz:
http://arxiv.org/abs/2308.09909
Autor:
Zhang, Chaoning, Qiao, Yu, Tariq, Shehbaz, Zheng, Sheng, Zhang, Chenshuang, Li, Chenghao, Shin, Hyundong, Hong, Choong Seon
In contrast to the human vision that mainly depends on the shape for recognizing the objects, deep image recognition models are widely known to be biased toward texture. Recently, Meta research team has released the first foundation model for image s
Externí odkaz:
http://arxiv.org/abs/2311.11465
Autor:
Jiang, Yuhua, Liu, Qihan, Ma, Xiaoteng, Li, Chenghao, Yang, Yiqin, Yang, Jun, Liang, Bin, Zhao, Qianchuan
Among the great successes of Reinforcement Learning (RL), self-play algorithms play an essential role in solving competitive games. Current self-play algorithms optimize the agent to maximize expected win-rates against its current or historical copie
Externí odkaz:
http://arxiv.org/abs/2305.11476