College of Computing and Data Science (CCDS)

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81 to 90 of 223 Results

EdgeSAM Sep 12, 2024 - Chen Change LOY Loy, Chen Change, 2024, "EdgeSAM", https://doi.org/10.21979/N9/KF8798, DR-NTU (Data), V2 We present EdgeSAM, an accelerated variant of the Segment Anything Model (SAM), optimized for efficient execution on edge devices with minimal compromise in performance. Our approach involves distilling the original ViT-based SAM image encoder into a purely CNN-based architecture...
CodeFormer Sep 12, 2024 - Chen Change LOY Loy, Chen Change, 2024, "CodeFormer", https://doi.org/10.21979/N9/X3IBKH, DR-NTU (Data), V4 Blind face restoration is a highly ill-posed problem that often requires auxiliary guidance to 1) improve the mapping from degraded inputs to desired outputs, or 2) complement high-quality details lost in the inputs. In this paper, we demonstrate that the learned discrete codeboo...
MMDetection3D Sep 9, 2024 - Chen Change LOY Loy, Chen Change, 2024, "MMDetection3D", https://doi.org/10.21979/N9/15XUKI, DR-NTU (Data), V1 MMDetection3D is an open source object detection toolbox based on PyTorch, towards the next-generation platform for general 3D detection. It is a part of the OpenMMLab project.
Related Data for: Intelligent-reflecting-surfaces-assisted hybrid FSO/RF communication with diversity combining: a performance analysis Aug 30, 2024 - Narendra VISHWAKARMA Vishwakarma, Narendra, 2024, "Related Data for: Intelligent-reflecting-surfaces-assisted hybrid FSO/RF communication with diversity combining: a performance analysis", https://doi.org/10.21979/N9/XRDOTT, DR-NTU (Data), V1 MATLAB source code the publication title: "Intelligent-reflecting-surfaces-assisted hybrid FSO/RF communication with diversity combining: a performance analysis" These code will produce the outage probability and Bit error rate plots for the above paper
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
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.

EdgeSAM

Sep 12, 2024 - Chen Change LOY

Loy, Chen Change, 2024, "EdgeSAM", https://doi.org/10.21979/N9/KF8798, DR-NTU (Data), V2

We present EdgeSAM, an accelerated variant of the Segment Anything Model (SAM), optimized for efficient execution on edge devices with minimal compromise in performance. Our approach involves distilling the original ViT-based SAM image encoder into a purely CNN-based architecture...

CodeFormer

Sep 12, 2024 - Chen Change LOY

Loy, Chen Change, 2024, "CodeFormer", https://doi.org/10.21979/N9/X3IBKH, DR-NTU (Data), V4

Blind face restoration is a highly ill-posed problem that often requires auxiliary guidance to 1) improve the mapping from degraded inputs to desired outputs, or 2) complement high-quality details lost in the inputs. In this paper, we demonstrate that the learned discrete codeboo...

MMDetection3D

Sep 9, 2024 - Chen Change LOY

Loy, Chen Change, 2024, "MMDetection3D", https://doi.org/10.21979/N9/15XUKI, DR-NTU (Data), V1

MMDetection3D is an open source object detection toolbox based on PyTorch, towards the next-generation platform for general 3D detection. It is a part of the OpenMMLab project.

Related Data for: Intelligent-reflecting-surfaces-assisted hybrid FSO/RF communication with diversity combining: a performance analysis

Aug 30, 2024 - Narendra VISHWAKARMA

Vishwakarma, Narendra, 2024, "Related Data for: Intelligent-reflecting-surfaces-assisted hybrid FSO/RF communication with diversity combining: a performance analysis", https://doi.org/10.21979/N9/XRDOTT, DR-NTU (Data), V1

MATLAB source code the publication title: "Intelligent-reflecting-surfaces-assisted hybrid FSO/RF communication with diversity combining: a performance analysis" These code will produce the outage probability and Bit error rate plots for the above paper

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

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.

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