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1,711 to 1,720 of 5,121 Results

val.tar.xz Jul 1, 2024 - Replication Data for: Out of Distribution Reasoning by Weakly-Supervised Disentangled Logic Variational Autoencoder XZ Archive - 103.4 MB - MD5: 5c8d1a2d7108ece4b5ea5f3c94c4508a
Replication Data for: Towards Explainable In-the-Wild Video Quality Assessment: A Database and a Language-Prompted Approach Jun 20, 2024 - S-Lab for Advanced Intelligence Wu, Haoning; Zhang, Erli; Liao, Liang; Chen, Chaofeng; Hou, Jingwen; Wang, Annan; Sun, Wenxiu; Yan, Qiong; Lin, Weisi, 2024, "Replication Data for: Towards Explainable In-the-Wild Video Quality Assessment: A Database and a Language-Prompted Approach", https://doi.org/10.21979/N9/ELWDPE, DR-NTU (Data), V1 A large-scale in-the-wild VQA database, named Maxwell, created to gather more than two million human opinions across 13 specific quality-related factors, including technical distortions e.g. noise, flicker and aesthetic factors e.g. contents.
Replication Data for: Q-Bench: A Benchmark for General-Purpose Foundation Models on Low-level Vision Jun 20, 2024 - S-Lab for Advanced Intelligence Wu, Haoning; Zhang, Zicheng; Zhang, Erli; Chen, Chaofeng; Liao, Liang; Wang, Annan; Li, Chunyi; Sun, Wenxiu; Yan, Qiong; Zhai, Guangtao; Lin, Weisi, 2024, "Replication Data for: Q-Bench: A Benchmark for General-Purpose Foundation Models on Low-level Vision", https://doi.org/10.21979/N9/M41ERD, DR-NTU (Data), V1 We present Q-Bench, a holistic benchmark crafted to systematically evaluate potential abilities of MLLMs on three realms: low-level visual perception, low-level visual description, and overall visual quality assessment.
Replication Data for: Q-Instruct: Improving Low-level Visual Abilities for Multi-modality Foundation Models Jun 20, 2024 - S-Lab for Advanced Intelligence Wu, Haoning; Zhang, Zicheng; Zhang, Erli; Chen, Chaofeng; Liao, Liang; Wang, Annan; Xu, Kaixin; Li, Chunyi; Hou, Jingwen; Zhai, Guangtao; Xue, Geng; Sun, Wenxiu; Yan, Qiong; Lin, Weisi, 2024, "Replication Data for: Q-Instruct: Improving Low-level Visual Abilities for Multi-modality Foundation Models", https://doi.org/10.21979/N9/GPLPNI, DR-NTU (Data), V1 The dataset consisting of human natural language feedback on low-level vision.
Related Data for: Adversarial Attack for Robust Watermark Protection Against Inpainting-based and Blind Watermark Removers Jan 23, 2024 - LYU Mingzhi Lyu, Mingzhi; Huang, Yi; Kong, Adams Wai-Kin, 2024, "Related Data for: Adversarial Attack for Robust Watermark Protection Against Inpainting-based and Blind Watermark Removers", https://doi.org/10.21979/N9/JNH3P4, DR-NTU (Data), V1 This dataset contains the main codes for the paper "Adversarial Attack for Robust Watermark Protection Against Inpainting-based and Blind Watermark Removers", which proposes a method to protect visible watermark on images against deep-learning-based watermark removers.
blind_watermark_removal.jpg Jan 23, 2024 - Related Data for: Adversarial Attack for Robust Watermark Protection Against Inpainting-based and Blind Watermark Removers JPEG Image - 78.9 KB - MD5: 38421b45b03d549ffe23d59dd27e1811 Documentation
Crfill.cpython-36.pyc Jan 23, 2024 - Related Data for: Adversarial Attack for Robust Watermark Protection Against Inpainting-based and Blind Watermark Removers Unknown - 3.9 KB - MD5: b8a8e1900a27bf07276ddcd2e405f314
Crfill.cpython-38.pyc Jan 23, 2024 - Related Data for: Adversarial Attack for Robust Watermark Protection Against Inpainting-based and Blind Watermark Removers Unknown - 3.9 KB - MD5: a0a9e1e391631d61b2a73e97a62b9fa9
Crfill.py Jan 23, 2024 - Related Data for: Adversarial Attack for Robust Watermark Protection Against Inpainting-based and Blind Watermark Removers Python Source Code - 5.1 KB - MD5: a8a0281bfde0d2a1e870d117c9334ed6
DBWEModel.cpython-36.pyc Jan 23, 2024 - Related Data for: Adversarial Attack for Robust Watermark Protection Against Inpainting-based and Blind Watermark Removers Unknown - 2.7 KB - MD5: 7582b957b2858c5847b027137b984f45

val.tar.xz

Jul 1, 2024 - Replication Data for: Out of Distribution Reasoning by Weakly-Supervised Disentangled Logic Variational Autoencoder

XZ Archive - 103.4 MB -

Replication Data for: Towards Explainable In-the-Wild Video Quality Assessment: A Database and a Language-Prompted Approach

Jun 20, 2024 - S-Lab for Advanced Intelligence

Wu, Haoning; Zhang, Erli; Liao, Liang; Chen, Chaofeng; Hou, Jingwen; Wang, Annan; Sun, Wenxiu; Yan, Qiong; Lin, Weisi, 2024, "Replication Data for: Towards Explainable In-the-Wild Video Quality Assessment: A Database and a Language-Prompted Approach", https://doi.org/10.21979/N9/ELWDPE, DR-NTU (Data), V1

A large-scale in-the-wild VQA database, named Maxwell, created to gather more than two million human opinions across 13 specific quality-related factors, including technical distortions e.g. noise, flicker and aesthetic factors e.g. contents.

Replication Data for: Q-Bench: A Benchmark for General-Purpose Foundation Models on Low-level Vision

Jun 20, 2024 - S-Lab for Advanced Intelligence

Wu, Haoning; Zhang, Zicheng; Zhang, Erli; Chen, Chaofeng; Liao, Liang; Wang, Annan; Li, Chunyi; Sun, Wenxiu; Yan, Qiong; Zhai, Guangtao; Lin, Weisi, 2024, "Replication Data for: Q-Bench: A Benchmark for General-Purpose Foundation Models on Low-level Vision", https://doi.org/10.21979/N9/M41ERD, DR-NTU (Data), V1

We present Q-Bench, a holistic benchmark crafted to systematically evaluate potential abilities of MLLMs on three realms: low-level visual perception, low-level visual description, and overall visual quality assessment.

Replication Data for: Q-Instruct: Improving Low-level Visual Abilities for Multi-modality Foundation Models

Jun 20, 2024 - S-Lab for Advanced Intelligence

Wu, Haoning; Zhang, Zicheng; Zhang, Erli; Chen, Chaofeng; Liao, Liang; Wang, Annan; Xu, Kaixin; Li, Chunyi; Hou, Jingwen; Zhai, Guangtao; Xue, Geng; Sun, Wenxiu; Yan, Qiong; Lin, Weisi, 2024, "Replication Data for: Q-Instruct: Improving Low-level Visual Abilities for Multi-modality Foundation Models", https://doi.org/10.21979/N9/GPLPNI, DR-NTU (Data), V1

The dataset consisting of human natural language feedback on low-level vision.

Related Data for: Adversarial Attack for Robust Watermark Protection Against Inpainting-based and Blind Watermark Removers

Jan 23, 2024 - LYU Mingzhi

Lyu, Mingzhi; Huang, Yi; Kong, Adams Wai-Kin, 2024, "Related Data for: Adversarial Attack for Robust Watermark Protection Against Inpainting-based and Blind Watermark Removers", https://doi.org/10.21979/N9/JNH3P4, DR-NTU (Data), V1

This dataset contains the main codes for the paper "Adversarial Attack for Robust Watermark Protection Against Inpainting-based and Blind Watermark Removers", which proposes a method to protect visible watermark on images against deep-learning-based watermark removers.

blind_watermark_removal.jpg

Jan 23, 2024 - Related Data for: Adversarial Attack for Robust Watermark Protection Against Inpainting-based and Blind Watermark Removers

JPEG Image - 78.9 KB -

Documentation

Crfill.cpython-36.pyc

Jan 23, 2024 - Related Data for: Adversarial Attack for Robust Watermark Protection Against Inpainting-based and Blind Watermark Removers

Unknown - 3.9 KB -

Crfill.cpython-38.pyc

Jan 23, 2024 - Related Data for: Adversarial Attack for Robust Watermark Protection Against Inpainting-based and Blind Watermark Removers

Unknown - 3.9 KB -

Crfill.py

Jan 23, 2024 - Related Data for: Adversarial Attack for Robust Watermark Protection Against Inpainting-based and Blind Watermark Removers