College of Computing and Data Science (CCDS)

Featured Dataverses

In order to use this feature you must have at least one published dataverse.

Publish Dataverse

Are you sure you want to publish your dataverse? Once you do so it must remain published.

Publish Dataverse

This dataverse cannot be published because the dataverse it is in has not been published.

Delete Dataverse

Are you sure you want to delete your dataverse? You cannot undelete this dataverse.

1,581 to 1,590 of 5,121 Results

ClusteringSDF Self-Organized Neural Implicit Surfaces for 3D Decomposition.pdf Sep 25, 2024 - ClusteringSDF: Self-Organized Neural Implicit Surfaces for 3D Decomposition Adobe PDF - 5.7 MB - MD5: b796cd399fb8241b429ce3943ca1a23b
Parameterization-driven Neural Surface Reconstruction for Object-oriented Editing in Neural Rendering 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...
Neuparam_ECCV24.pdf Sep 25, 2024 - Parameterization-driven Neural Surface Reconstruction for Object-oriented Editing in Neural Rendering Adobe PDF - 9.4 MB - MD5: a85b602783d50cc3f5c9166ddd818e79
Eliminating Feature Ambiguity for Few-Shot Segmentation 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...
Paper_ECCV2024.pdf Sep 25, 2024 - Eliminating Feature Ambiguity for Few-Shot Segmentation Adobe PDF - 1.4 MB - MD5: c46baf03e693a12c36592bb3f0ac214a
Kalman-Inspired Feature Propagation for Video Face Super-Resolution 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...
KEEP_ECCV.pdf Sep 25, 2024 - Kalman-Inspired Feature Propagation for Video Face Super-Resolution Adobe PDF - 7.8 MB - MD5: d6b53c6b690e3cf6f3c5e34757ea7c8e
StructLDM: Structured Latent Diffusion for 3D Human Generation 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...
Paper_ECCV2024.pdf Sep 20, 2024 - StructLDM: Structured Latent Diffusion for 3D Human Generation Adobe PDF - 4.5 MB - MD5: b293401ab6fd2e9cc01588dd1b04879d
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...

ClusteringSDF Self-Organized Neural Implicit Surfaces for 3D Decomposition.pdf

Sep 25, 2024 - ClusteringSDF: Self-Organized Neural Implicit Surfaces for 3D Decomposition

Adobe PDF - 5.7 MB -

Parameterization-driven Neural Surface Reconstruction for Object-oriented Editing in Neural Rendering

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...

Neuparam_ECCV24.pdf

Sep 25, 2024 - Parameterization-driven Neural Surface Reconstruction for Object-oriented Editing in Neural Rendering

Adobe PDF - 9.4 MB -

Eliminating Feature Ambiguity for Few-Shot Segmentation

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...

Paper_ECCV2024.pdf

Sep 25, 2024 - Eliminating Feature Ambiguity for Few-Shot Segmentation

Adobe PDF - 1.4 MB -

Kalman-Inspired Feature Propagation for Video Face Super-Resolution

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...

KEEP_ECCV.pdf

Sep 25, 2024 - Kalman-Inspired Feature Propagation for Video Face Super-Resolution

Adobe PDF - 7.8 MB -

StructLDM: Structured Latent Diffusion for 3D Human Generation

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...

Paper_ECCV2024.pdf

Sep 20, 2024 - StructLDM: Structured Latent Diffusion for 3D Human Generation

Adobe PDF - 4.5 MB -

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...

Add Data

Share Dataverse

Link Dataverse

Reset Modifications