81 to 90 of 298 Results
Sep 25, 2024 - S-Lab for Advanced Intelligence
Wu, Tianhao; Zheng, Chuanxia; Wu, Qianyi; Cham, Tat-Jen, 2024, "ClusteringSDF: Self-Organized Neural Implicit Surfaces for 3D Decomposition", https://doi.org/10.21979/N9/RJUHMC, DR-NTU (Data), V1
3D decomposition/segmentation remains a challenge as large-scale 3D annotated data is not readily available. Existing approaches typically leverage 2D machine-generated segments, integrating them to achieve 3D consistency. In this paper, we propose ClusteringSDF, a novel approach... |
Sep 25, 2024 - S-Lab for Advanced Intelligence
Xu, Baixin; Hu, Jiangbei; Hou, Fei; Lin, Kwan-Yee; Wu, Wayne; Qian, Chen; He, Ying, 2024, "Parameterization-driven Neural Surface Reconstruction for Object-oriented Editing in Neural Rendering", https://doi.org/10.21979/N9/0C9BU9, DR-NTU (Data), V1
The advancements in neural rendering have increased the need for techniques that enable intuitive editing of 3D objects represented as neural implicit surfaces. This paper introduces a novel neural algorithm for parameterizing neural implicit surfaces to simple parametric domains... |
Sep 25, 2024 - S-Lab for Advanced Intelligence
Xu, Qianxiong; Lin, Guosheng; Loy, Chen Change; Long, Cheng; Li, Ziyue; Zhao, Rui, 2024, "Eliminating Feature Ambiguity for Few-Shot Segmentation", https://doi.org/10.21979/N9/CIOE8Y, DR-NTU (Data), V1
Recent advancements in few-shot segmentation (FSS) have exploited pixel-by-pixel matching between query and support features, typically based on cross attention, which selectively activate query foreground (FG) features that correspond to the same-class support FG features. Howev... |
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... |
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... |
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... |
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. |
Appointment: Professor Research topics: • Dr Loy's research interests include computer vision and deep learning with a focus on image/video restoration and enhancement, creative content generation, and representation learning. For more information, visit this webpage. |
Sep 9, 2024
S-Lab for Advanced Intelligence, established in 2020, is a university laboratory at NTU focusing on research and development of cutting-edge AI technologies in computer vision, natural language processing, reinforcement learning, deep learning, and distributed computing. We aim t... |
