91 to 100 of 235 Results
Sep 25, 2024 - S-Lab for Advanced Intelligence
Feng, Ruicheng; Li, Chongyi; Loy, Chen Change, 2024, "Kalman-Inspired Feature Propagation for Video Face Super-Resolution", https://doi.org/10.21979/N9/FMVNYY, DR-NTU (Data), V1
Despite the promising progress of face image super-resolution, video face super-resolution remains relatively under-explored. Existing approaches either adapt general video super-resolution networks to face datasets or apply established face image super-resolution models independ... |
Sep 20, 2024 - S-Lab for Advanced Intelligence
Hu, Tao; Hong, Fangzhou; Liu, Ziwei, 2024, "StructLDM: Structured Latent Diffusion for 3D Human Generation", https://doi.org/10.21979/N9/BXUEXV, DR-NTU (Data), V1
Recent 3D human generative models have achieved remarkable progress by learning 3D-aware GANs from 2D images. However, existing 3D human generative methods model humans in a compact 1D latent space, ignoring the articulated structure and semantics of human body topology. In this... |
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... |
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... |
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. |
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 |
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". |
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 |
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 |
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 |
