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1,391 to 1,400 of 5,008 Results

ProxyCLIP: Proxy Attention Improves CLIP for Open-Vocabulary Segmentation Sep 25, 2024 - S-Lab for Advanced Intelligence Lan, Mengcheng; Chen, Chaofeng; Ke, Yiping; Wang, Xinjiang; Feng, Litong; Zhang, Wayne, 2024, "ProxyCLIP: Proxy Attention Improves CLIP for Open-Vocabulary Segmentation", https://doi.org/10.21979/N9/YY8L5O, DR-NTU (Data), V1 Open-vocabulary semantic segmentation requires models to effectively integrate visual representations with open-vocabulary semantic labels. While Contrastive Language-Image Pre-training (CLIP) models shine in recognizing visual concepts from text, they often struggle with segment...
ProxyCLIP.pdf Sep 25, 2024 - ProxyCLIP: Proxy Attention Improves CLIP for Open-Vocabulary Segmentation Adobe PDF - 3.5 MB - MD5: 396eed51abcc20b34e2a977c570d33ee
ClearCLIP: Decomposing CLIP Representations for Dense Vision-Language Inference Sep 25, 2024 - S-Lab for Advanced Intelligence 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 - S-Lab for Advanced Intelligence 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 - 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...
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

ProxyCLIP: Proxy Attention Improves CLIP for Open-Vocabulary Segmentation

Sep 25, 2024 - S-Lab for Advanced Intelligence

Lan, Mengcheng; Chen, Chaofeng; Ke, Yiping; Wang, Xinjiang; Feng, Litong; Zhang, Wayne, 2024, "ProxyCLIP: Proxy Attention Improves CLIP for Open-Vocabulary Segmentation", https://doi.org/10.21979/N9/YY8L5O, DR-NTU (Data), V1

Open-vocabulary semantic segmentation requires models to effectively integrate visual representations with open-vocabulary semantic labels. While Contrastive Language-Image Pre-training (CLIP) models shine in recognizing visual concepts from text, they often struggle with segment...

ProxyCLIP.pdf

Sep 25, 2024 - ProxyCLIP: Proxy Attention Improves CLIP for Open-Vocabulary Segmentation

Adobe PDF - 3.5 MB -

ClearCLIP: Decomposing CLIP Representations for Dense Vision-Language Inference

Sep 25, 2024 - S-Lab for Advanced Intelligence

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 - S-Lab for Advanced Intelligence

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 - 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...

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 -

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