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61 to 70 of 84 Results

ClearCLIP: Decomposing CLIP Representations for Dense Vision-Language Inference Sep 25, 2024 Lan, Mengcheng; Chen, Chaofeng; Ke, Yiping; Wang, Xinjiang; Feng, Litong; Zhang, Wayne, 2024, "ClearCLIP: Decomposing CLIP Representations for Dense Vision-Language Inference", https://doi.org/10.21979/N9/S6NTDJ, DR-NTU (Data), V1 Despite the success of large-scale pretrained Vision-Language Models (VLMs) especially CLIP in various open-vocabulary tasks, their application to semantic segmentation remains challenging, producing noisy segmentation maps with mis-segmented regions. In this paper, we carefully...
ClearCLIP.pdf Sep 25, 2024 - ClearCLIP: Decomposing CLIP Representations for Dense Vision-Language Inference Adobe PDF - 4.7 MB - MD5: ef5b43ea1ec6f6fa4a3204222101b526
Open-Vocabulary SAM: Segment and Recognize Twenty-thousand Classes Interactively Sep 25, 2024 Yuan, Haobo; Li, Xiangtai; Zhou, Chong; Li, Yining; Chen, Kai; Loy, Chen Change, 2024, "Open-Vocabulary SAM: Segment and Recognize Twenty-thousand Classes Interactively", https://doi.org/10.21979/N9/L05ULT, DR-NTU (Data), V1 The CLIP and Segment Anything Model (SAM) are remarkable vision foundation models (VFMs). SAM excels in segmentation tasks across diverse domains, whereas CLIP is renowned for its zero-shot recognition capabilities. This paper presents an in-depth exploration of integrating these...
OpenVoc_SAM.pdf Sep 25, 2024 - Open-Vocabulary SAM: Segment and Recognize Twenty-thousand Classes Interactively Adobe PDF - 3.4 MB - MD5: fa2b0303cf17c6b247314d2a9252040d
ClusteringSDF: Self-Organized Neural Implicit Surfaces for 3D Decomposition Sep 25, 2024 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...
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 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 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

ClearCLIP: Decomposing CLIP Representations for Dense Vision-Language Inference

Sep 25, 2024

Lan, Mengcheng; Chen, Chaofeng; Ke, Yiping; Wang, Xinjiang; Feng, Litong; Zhang, Wayne, 2024, "ClearCLIP: Decomposing CLIP Representations for Dense Vision-Language Inference", https://doi.org/10.21979/N9/S6NTDJ, DR-NTU (Data), V1

Despite the success of large-scale pretrained Vision-Language Models (VLMs) especially CLIP in various open-vocabulary tasks, their application to semantic segmentation remains challenging, producing noisy segmentation maps with mis-segmented regions. In this paper, we carefully...

ClearCLIP.pdf

Sep 25, 2024 - ClearCLIP: Decomposing CLIP Representations for Dense Vision-Language Inference

Adobe PDF - 4.7 MB -

Open-Vocabulary SAM: Segment and Recognize Twenty-thousand Classes Interactively

Sep 25, 2024

Yuan, Haobo; Li, Xiangtai; Zhou, Chong; Li, Yining; Chen, Kai; Loy, Chen Change, 2024, "Open-Vocabulary SAM: Segment and Recognize Twenty-thousand Classes Interactively", https://doi.org/10.21979/N9/L05ULT, DR-NTU (Data), V1

The CLIP and Segment Anything Model (SAM) are remarkable vision foundation models (VFMs). SAM excels in segmentation tasks across diverse domains, whereas CLIP is renowned for its zero-shot recognition capabilities. This paper presents an in-depth exploration of integrating these...

OpenVoc_SAM.pdf

Sep 25, 2024 - Open-Vocabulary SAM: Segment and Recognize Twenty-thousand Classes Interactively

Adobe PDF - 3.4 MB -

ClusteringSDF: Self-Organized Neural Implicit Surfaces for 3D Decomposition

Sep 25, 2024

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...

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

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

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 -

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