Kangkang Wei - IEEE Xplore Author Profile
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Kangkang Wei

Publications
2
Citations
27
Publications by Year
20202020
Co-Authors:
Show All Co-Authors (11)
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Kangkang Wei

Affiliation

Wuhan National Laboratory for Optoelectronics
Huazhong University of Science and Technology
Wuhan, China

Publication Topics

Biography

Kangkang Wei is currently working toward the master's degree with the in Huazhong University of Science and Technology, Wuhan, China. His current research direction is heterogeneous integration of silicon photonics and two-dimensional materials.(Based on document published on 30 March 2020).
Publications
2
Citations
27
Publications by Year
20202020
Co-Authors:
Show All Co-Authors (11)
Author's Published Works

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TokenDoc: Source Authentication With a Hybrid Approach of Smart Contract and RNN-Based Models With AES Encryption

Apoorv Saxena;Hung-Min Sun

IEEE Transactions on Engineering Management
Year: 2024 | Volume: 71 | Journal Article |
This study proposes a hybrid architecture in which duplicates in the dataset are identified using a deep-learning neural network to authenticate the source. The blockchain provides distinct metadata to the information source to allow the sender and the recipient to verify that the information is accurate and cannot be modified or altered. The system also uses RNN-based LSTM and GRU model neural ne...Show More

TokenDoc: Source Authentication With a Hybrid Approach of Smart Contract and RNN-Based Models With AES Encryption

Apoorv Saxena;Hung-Min Sun

Year: 2024 | Volume: 71 | Journal Article |

A-STAR: Test-time Attention Segregation and Retention for Text-to-image Synthesis

Aishwarya Agarwal;Srikrishna Karanam;K J Joseph;Apoorv Saxena;Koustava Goswami;Balaji Vasan Srinivasan

2023 IEEE/CVF International Conference on Computer Vision (ICCV)
Year: 2023 | Conference Paper |
Cited by: Papers (7)
While recent developments in text-to-image generative models have led to a suite of high-performing methods capable of producing creative imagery from free-form text, there are several limitations. By analyzing the cross-attention representations of these models, we notice two key issues. First, for text prompts that contain multiple concepts, there is a significant amount of pixel-space overlap (...Show More

A-STAR: Test-time Attention Segregation and Retention for Text-to-image Synthesis

Aishwarya Agarwal;Srikrishna Karanam;K J Joseph;Apoorv Saxena;Koustava Goswami;Balaji Vasan Srinivasan

Year: 2023 | Conference Paper |

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