College of Computing and Data Science (CCDS)

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101 to 110 of 235 Results

Replication Data for: Towards Explainable In-the-Wild Video Quality Assessment: A Database and a Language-Prompted Approach Jun 20, 2024 - S-Lab for Advanced Intelligence Wu, Haoning; Zhang, Erli; Liao, Liang; Chen, Chaofeng; Hou, Jingwen; Wang, Annan; Sun, Wenxiu; Yan, Qiong; Lin, Weisi, 2024, "Replication Data for: Towards Explainable In-the-Wild Video Quality Assessment: A Database and a Language-Prompted Approach", https://doi.org/10.21979/N9/ELWDPE, DR-NTU (Data), V1 A large-scale in-the-wild VQA database, named Maxwell, created to gather more than two million human opinions across 13 specific quality-related factors, including technical distortions e.g. noise, flicker and aesthetic factors e.g. contents.
Replication Data for: Q-Bench: A Benchmark for General-Purpose Foundation Models on Low-level Vision Jun 20, 2024 - S-Lab for Advanced Intelligence Wu, Haoning; Zhang, Zicheng; Zhang, Erli; Chen, Chaofeng; Liao, Liang; Wang, Annan; Li, Chunyi; Sun, Wenxiu; Yan, Qiong; Zhai, Guangtao; Lin, Weisi, 2024, "Replication Data for: Q-Bench: A Benchmark for General-Purpose Foundation Models on Low-level Vision", https://doi.org/10.21979/N9/M41ERD, DR-NTU (Data), V1 We present Q-Bench, a holistic benchmark crafted to systematically evaluate potential abilities of MLLMs on three realms: low-level visual perception, low-level visual description, and overall visual quality assessment.
Replication Data for: Q-Instruct: Improving Low-level Visual Abilities for Multi-modality Foundation Models Jun 20, 2024 - S-Lab for Advanced Intelligence Wu, Haoning; Zhang, Zicheng; Zhang, Erli; Chen, Chaofeng; Liao, Liang; Wang, Annan; Xu, Kaixin; Li, Chunyi; Hou, Jingwen; Zhai, Guangtao; Xue, Geng; Sun, Wenxiu; Yan, Qiong; Lin, Weisi, 2024, "Replication Data for: Q-Instruct: Improving Low-level Visual Abilities for Multi-modality Foundation Models", https://doi.org/10.21979/N9/GPLPNI, DR-NTU (Data), V1 The dataset consisting of human natural language feedback on low-level vision.
Related Data for: Adversarial Attack for Robust Watermark Protection Against Inpainting-based and Blind Watermark Removers Jan 23, 2024 - LYU Mingzhi Lyu, Mingzhi; Huang, Yi; Kong, Adams Wai-Kin, 2024, "Related Data for: Adversarial Attack for Robust Watermark Protection Against Inpainting-based and Blind Watermark Removers", https://doi.org/10.21979/N9/JNH3P4, DR-NTU (Data), V1 This dataset contains the main codes for the paper "Adversarial Attack for Robust Watermark Protection Against Inpainting-based and Blind Watermark Removers", which proposes a method to protect visible watermark on images against deep-learning-based watermark removers.
Basal Cell Carcinoma by Reflectance Confocal Microscopy images Jan 23, 2024 - XU Shifeng Xu, Shifeng; Kong, Adams Wai Kin, 2024, "Basal Cell Carcinoma by Reflectance Confocal Microscopy images", https://doi.org/10.21979/N9/U4FPB1, DR-NTU (Data), V1 Classification on reflectance confocal microscopy images of basal cell carcinoma.
Related Data for: Pearls Hide Behind Linearity: Simplifying Deep Convolutional Networks for Embedded Hardware Systems via Linearity Grafting Dec 18, 2023 - Luo Xiangzhong Luo, Xiangzhong; Liu, Weichen, 2023, "Related Data for: Pearls Hide Behind Linearity: Simplifying Deep Convolutional Networks for Embedded Hardware Systems via Linearity Grafting", https://doi.org/10.21979/N9/SJX3HR, DR-NTU (Data), V1 The related data for "Pearls Hide Behind Linearity: Simplifying Deep Convolutional Networks for Embedded Hardware Systems via Linearity Grafting".
Replication Data for: iMAT: Energy-Efficient In-Memory Acceleration of Ternary Neural Networks With Sparse Dot Product Dec 18, 2023 - Zhu Shien Zhu, Shien; Huai, Shuo; Xiong, Guochu; Liu, Weichen, 2023, "Replication Data for: iMAT: Energy-Efficient In-Memory Acceleration of Ternary Neural Networks With Sparse Dot Product", https://doi.org/10.21979/N9/B4SIIN, DR-NTU (Data), V1, UNF:6:KboaxJ9ZX4FgcAqcXn8SqQ== [fileUNF] This dataset contains the simulation environment, simulation results, and post-processing data for the iMAT paper published in ISLPED 2023.
Related data for: Contention Minimized Bypassing in SMART NoC Dec 15, 2023 - Peng Chen Chen, Peng, 2023, "Related data for: Contention Minimized Bypassing in SMART NoC", https://doi.org/10.21979/N9/BPBOYK, DR-NTU (Data), V1 This dataset contains keycodes for the proposed algorithms in the paper.
Replication Data for: FAT: An In-Memory Accelerator with Fast Addition for Ternary Weight Neural Networks Dec 14, 2023 - Zhu Shien Zhu, Shien; Duong, H. K. Luan; Chen, Hui; Di, Liu; Liu, Weichen, 2023, "Replication Data for: FAT: An In-Memory Accelerator with Fast Addition for Ternary Weight Neural Networks", https://doi.org/10.21979/N9/DYKUPV, DR-NTU (Data), V1, UNF:6:UCCl9ySs+IN5trVkl2AGQQ== [fileUNF] This dataset contains the codes, figures, and tables for the TCAD 2022 paper: FAT: An In-Memory Accelerator with Fast Addition for Ternary Weight Neural Networks.
Related data for: An efficient sparse LSTM accelerator on embedded FPGAs with bandwidth-oriented pruning Dec 14, 2023 - LI Shiqing Li, Shiqing, 2023, "Related data for: An efficient sparse LSTM accelerator on embedded FPGAs with bandwidth-oriented pruning", https://doi.org/10.21979/N9/MTHKVG, DR-NTU (Data), V1 Training procedure of the paper

Replication Data for: Towards Explainable In-the-Wild Video Quality Assessment: A Database and a Language-Prompted Approach

Jun 20, 2024 - S-Lab for Advanced Intelligence

Wu, Haoning; Zhang, Erli; Liao, Liang; Chen, Chaofeng; Hou, Jingwen; Wang, Annan; Sun, Wenxiu; Yan, Qiong; Lin, Weisi, 2024, "Replication Data for: Towards Explainable In-the-Wild Video Quality Assessment: A Database and a Language-Prompted Approach", https://doi.org/10.21979/N9/ELWDPE, DR-NTU (Data), V1

A large-scale in-the-wild VQA database, named Maxwell, created to gather more than two million human opinions across 13 specific quality-related factors, including technical distortions e.g. noise, flicker and aesthetic factors e.g. contents.

Replication Data for: Q-Bench: A Benchmark for General-Purpose Foundation Models on Low-level Vision

Jun 20, 2024 - S-Lab for Advanced Intelligence

Wu, Haoning; Zhang, Zicheng; Zhang, Erli; Chen, Chaofeng; Liao, Liang; Wang, Annan; Li, Chunyi; Sun, Wenxiu; Yan, Qiong; Zhai, Guangtao; Lin, Weisi, 2024, "Replication Data for: Q-Bench: A Benchmark for General-Purpose Foundation Models on Low-level Vision", https://doi.org/10.21979/N9/M41ERD, DR-NTU (Data), V1

We present Q-Bench, a holistic benchmark crafted to systematically evaluate potential abilities of MLLMs on three realms: low-level visual perception, low-level visual description, and overall visual quality assessment.

Replication Data for: Q-Instruct: Improving Low-level Visual Abilities for Multi-modality Foundation Models

Jun 20, 2024 - S-Lab for Advanced Intelligence

Wu, Haoning; Zhang, Zicheng; Zhang, Erli; Chen, Chaofeng; Liao, Liang; Wang, Annan; Xu, Kaixin; Li, Chunyi; Hou, Jingwen; Zhai, Guangtao; Xue, Geng; Sun, Wenxiu; Yan, Qiong; Lin, Weisi, 2024, "Replication Data for: Q-Instruct: Improving Low-level Visual Abilities for Multi-modality Foundation Models", https://doi.org/10.21979/N9/GPLPNI, DR-NTU (Data), V1

The dataset consisting of human natural language feedback on low-level vision.

Related Data for: Adversarial Attack for Robust Watermark Protection Against Inpainting-based and Blind Watermark Removers

Jan 23, 2024 - LYU Mingzhi

Lyu, Mingzhi; Huang, Yi; Kong, Adams Wai-Kin, 2024, "Related Data for: Adversarial Attack for Robust Watermark Protection Against Inpainting-based and Blind Watermark Removers", https://doi.org/10.21979/N9/JNH3P4, DR-NTU (Data), V1

This dataset contains the main codes for the paper "Adversarial Attack for Robust Watermark Protection Against Inpainting-based and Blind Watermark Removers", which proposes a method to protect visible watermark on images against deep-learning-based watermark removers.

Basal Cell Carcinoma by Reflectance Confocal Microscopy images

Jan 23, 2024 - XU Shifeng

Xu, Shifeng; Kong, Adams Wai Kin, 2024, "Basal Cell Carcinoma by Reflectance Confocal Microscopy images", https://doi.org/10.21979/N9/U4FPB1, DR-NTU (Data), V1

Classification on reflectance confocal microscopy images of basal cell carcinoma.

Related Data for: Pearls Hide Behind Linearity: Simplifying Deep Convolutional Networks for Embedded Hardware Systems via Linearity Grafting

Dec 18, 2023 - Luo Xiangzhong

Luo, Xiangzhong; Liu, Weichen, 2023, "Related Data for: Pearls Hide Behind Linearity: Simplifying Deep Convolutional Networks for Embedded Hardware Systems via Linearity Grafting", https://doi.org/10.21979/N9/SJX3HR, DR-NTU (Data), V1

The related data for "Pearls Hide Behind Linearity: Simplifying Deep Convolutional Networks for Embedded Hardware Systems via Linearity Grafting".

Replication Data for: iMAT: Energy-Efficient In-Memory Acceleration of Ternary Neural Networks With Sparse Dot Product

Dec 18, 2023 - Zhu Shien

Zhu, Shien; Huai, Shuo; Xiong, Guochu; Liu, Weichen, 2023, "Replication Data for: iMAT: Energy-Efficient In-Memory Acceleration of Ternary Neural Networks With Sparse Dot Product", https://doi.org/10.21979/N9/B4SIIN, DR-NTU (Data), V1, UNF:6:KboaxJ9ZX4FgcAqcXn8SqQ== [fileUNF]

This dataset contains the simulation environment, simulation results, and post-processing data for the iMAT paper published in ISLPED 2023.

Related data for: Contention Minimized Bypassing in SMART NoC

Dec 15, 2023 - Peng Chen

Chen, Peng, 2023, "Related data for: Contention Minimized Bypassing in SMART NoC", https://doi.org/10.21979/N9/BPBOYK, DR-NTU (Data), V1

This dataset contains keycodes for the proposed algorithms in the paper.

Replication Data for: FAT: An In-Memory Accelerator with Fast Addition for Ternary Weight Neural Networks

Dec 14, 2023 - Zhu Shien

Zhu, Shien; Duong, H. K. Luan; Chen, Hui; Di, Liu; Liu, Weichen, 2023, "Replication Data for: FAT: An In-Memory Accelerator with Fast Addition for Ternary Weight Neural Networks", https://doi.org/10.21979/N9/DYKUPV, DR-NTU (Data), V1, UNF:6:UCCl9ySs+IN5trVkl2AGQQ== [fileUNF]

This dataset contains the codes, figures, and tables for the TCAD 2022 paper: FAT: An In-Memory Accelerator with Fast Addition for Ternary Weight Neural Networks.

Related data for: An efficient sparse LSTM accelerator on embedded FPGAs with bandwidth-oriented pruning

Dec 14, 2023 - LI Shiqing

Li, Shiqing, 2023, "Related data for: An efficient sparse LSTM accelerator on embedded FPGAs with bandwidth-oriented pruning", https://doi.org/10.21979/N9/MTHKVG, DR-NTU (Data), V1

Training procedure of the paper

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