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101 to 110 of 306 Results

Replication Data for: Energy-Efficient Real-Time Job Mapping and Resource Management in Mobile-Edge Computing Aug 21, 2024 - Resource Allocation for Edge-Cloud System Gao, Chuanchao; Kumar, Niraj; Easwaran, Arvind, 2024, "Replication Data for: Energy-Efficient Real-Time Job Mapping and Resource Management in Mobile-Edge Computing", https://doi.org/10.21979/N9/VJTMBM, DR-NTU (Data), V2, UNF:6:ujvYZ07RwxVvj5sepdEDNw== [fileUNF] Experiment data for paper "Energy-Efficient Real-Time Job Mapping and Resource Management in Mobile-Edge Computing".
Replication Data for: Co-Design of Out-of-Distribution Detectors for Autonomous Emergency Braking Systems Jul 3, 2024 - Safe ML Yuhas, Michael John; Easwaran, Arvind, 2024, "Replication Data for: Co-Design of Out-of-Distribution Detectors for Autonomous Emergency Braking Systems", https://doi.org/10.21979/N9/YIOFK8, DR-NTU (Data), V2 Replication Data for: Co-Design of Out-of-Distribution Detectors for Autonomous Emergency Braking Systems
Replication Data for: Design Methodology for Deep Out-of-Distribution Detectors in Real-Time Cyber-Physical Systems Jul 2, 2024 - Safe ML Yuhas, Michael; Ng, Daniel Jun Xian; Easwaran, Arvind, 2024, "Replication Data for: Design Methodology for Deep Out-of-Distribution Detectors in Real-Time Cyber-Physical Systems", https://doi.org/10.21979/N9/UZY54Q, DR-NTU (Data), V1 Replication Data for: Design Methodology for Deep Out-of-Distribution Detectors in Real-Time Cyber-Physical Systems
Replication Data for: Out of Distribution Reasoning by Weakly-Supervised Disentangled Logic Variational Autoencoder Jul 1, 2024 - Safe ML Yuhas, Michael; Rahiminasab, Zahra; Easwaran, Arvind, 2024, "Replication Data for: Out of Distribution Reasoning by Weakly-Supervised Disentangled Logic Variational Autoencoder", https://doi.org/10.21979/N9/0YI4HT, DR-NTU (Data), V1 Replication Data for: Out of Distribution Reasoning by Weakly-Supervised Disentangled Logic Variational Autoencoder
Coronavirus Neutralizing Antibodies(Nanyang Technological University) Jun 20, 2024KWOH Chee Keong
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.
LIN Weisi(Nanyang Technological University) Jun 12, 2024 Research topics: • Image processing • Perceptual modeling • Video compression • Multimedia communication and computer vision • Computer 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.

Replication Data for: Energy-Efficient Real-Time Job Mapping and Resource Management in Mobile-Edge Computing

Aug 21, 2024 - Resource Allocation for Edge-Cloud System

Gao, Chuanchao; Kumar, Niraj; Easwaran, Arvind, 2024, "Replication Data for: Energy-Efficient Real-Time Job Mapping and Resource Management in Mobile-Edge Computing", https://doi.org/10.21979/N9/VJTMBM, DR-NTU (Data), V2, UNF:6:ujvYZ07RwxVvj5sepdEDNw== [fileUNF]

Experiment data for paper "Energy-Efficient Real-Time Job Mapping and Resource Management in Mobile-Edge Computing".

Replication Data for: Co-Design of Out-of-Distribution Detectors for Autonomous Emergency Braking Systems

Jul 3, 2024 - Safe ML

Yuhas, Michael John; Easwaran, Arvind, 2024, "Replication Data for: Co-Design of Out-of-Distribution Detectors for Autonomous Emergency Braking Systems", https://doi.org/10.21979/N9/YIOFK8, DR-NTU (Data), V2

Replication Data for: Co-Design of Out-of-Distribution Detectors for Autonomous Emergency Braking Systems

Replication Data for: Design Methodology for Deep Out-of-Distribution Detectors in Real-Time Cyber-Physical Systems

Jul 2, 2024 - Safe ML

Yuhas, Michael; Ng, Daniel Jun Xian; Easwaran, Arvind, 2024, "Replication Data for: Design Methodology for Deep Out-of-Distribution Detectors in Real-Time Cyber-Physical Systems", https://doi.org/10.21979/N9/UZY54Q, DR-NTU (Data), V1

Replication Data for: Design Methodology for Deep Out-of-Distribution Detectors in Real-Time Cyber-Physical Systems

Replication Data for: Out of Distribution Reasoning by Weakly-Supervised Disentangled Logic Variational Autoencoder

Jul 1, 2024 - Safe ML

Yuhas, Michael; Rahiminasab, Zahra; Easwaran, Arvind, 2024, "Replication Data for: Out of Distribution Reasoning by Weakly-Supervised Disentangled Logic Variational Autoencoder", https://doi.org/10.21979/N9/0YI4HT, DR-NTU (Data), V1

Replication Data for: Out of Distribution Reasoning by Weakly-Supervised Disentangled Logic Variational Autoencoder

Coronavirus Neutralizing Antibodies(Nanyang Technological University)

Jun 20, 2024KWOH Chee Keong

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.

LIN Weisi(Nanyang Technological University)

Jun 12, 2024

Research topics: • Image processing • Perceptual modeling • Video compression • Multimedia communication and computer vision • Computer 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.

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