cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.21979/n9/l05ult
title: Open-Vocabulary SAM: Segment and Recognize Twenty-thousand Classes Interactively

description: 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 two models into a unified framework. Specifically, we introduce the Open-Vocabulary SAM, a SAM-inspired model designed for simultaneous interactive segmentation and recognition, leveraging two unique knowledge transfer modules: SAM2CLIP and CLIP2SAM. The former adapts SAM’s knowledge into the CLIP via distillation and learnable transformer adapters, while the latter transfers CLIP knowledge into SAM, enhancing its recognition capabilities. Extensive experiments on various datasets and detectors show the effectiveness of Open-Vocabulary SAM in both segmentation and recognition tasks, significantly outperforming the naïve baselines of simply combining SAM and CLIP. Furthermore, aided with image classification data training, our method can segment and recognize approximately 22,000 classes.
DOI: 10.21979/n9/elwdpe
title: Replication Data for: Towards Explainable In-the-Wild Video Quality Assessment: A Database and a Language-Prompted Approach

description: 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.
DOI: 10.21979/n9/m41erd
title: Replication Data for: Q-Bench: A Benchmark for General-Purpose Foundation Models on Low-level Vision

description: 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.
DOI: 10.21979/n9/gplpni
title: Replication Data for: Q-Instruct: Improving Low-level Visual Abilities for Multi-modality Foundation Models

description: The dataset consisting of human natural language feedback on low-level vision.
DOI: 10.21979/n9/rjuhmc
title: ClusteringSDF: Self-Organized Neural Implicit Surfaces for 3D Decomposition

description: 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 achieving both segmentation and reconstruction in 3D via the neural implicit surface representation, specifically the Signed Distance Function (SDF), where the segmentation rendering is directly integrated with the volume rendering of neural implicit surfaces. Although based on ObjectSDF++, ClusteringSDF no longer requires ground-truth segments for supervision while maintaining the capability of reconstructing individual object surfaces, relying purely on the noisy and inconsistent labels from pre-trained models. As the core of ClusteringSDF, we introduce a highly efficient clustering mechanism for lifting 2D labels to 3D. Experimental results on the challenging scenes from ScanNet and Replica datasets show that ClusteringSDF can achieve competitive performance compared to the state-of-the-art with significantly reduced training time.
DOI: 10.21979/n9/bxuexv
title: StructLDM: Structured Latent Diffusion for 3D Human Generation

description: 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, we explore more expressive and higher-dimensional latent space for 3D human modeling and propose StructLDM, a diffusion-based unconditional 3D human generative model that is learned from 2D images. StructLDM solves the challenges imposed due to the high-dimensional growth of latent space with three key designs: 1) A semantic structured latent space defined on the dense surface manifold of a statistical human body template. 2) A structured 3D-aware auto-decoder that factorizes the global latent space into several semantic body parts parameterized by a set of conditional structured local NeRFs anchored to the body template, which embeds the properties learned from the 2D training data and can be decoded to render view-consistent humans under different poses and clothing styles. 3) A structured latent diffusion model for generative human appearance sampling. Extensive experiments validate StructLDM's state-of-the-art generation performance and illustrate the expressiveness of the structured latent space over the well-adopted 1D latent space. Notably, StructLDM enables different levels of controllable 3D human generation and editing, including pose/view/shape control, and high-level tasks including compositional generations, part-aware clothing editing, 3D virtual try-on, etc.
DOI: 10.21979/n9/x3ibkh
title: CodeFormer

description: 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 codebook prior in a small proxy space largely reduces the uncertainty and ambiguity of restoration mapping by casting face restoration as a code prediction task, it meanwhile provides rich visual atoms for generating high-quality faces. Under this paradigm, we propose a Transformer-based prediction network, named CodeFormer, to model global composition and context of the low-quality faces for code prediction, enabling the discovery of natural faces that closely approximate the target faces even when the inputs are severely degraded. To enhance the adaptiveness for different degradation, we also propose a controllable feature transformation module that allows a flexible trade-off between fidelity and quality. Thanks to the expressive codebook prior and global modeling, CodeFormer outperforms the state of the arts in both quality and fidelity, showing superior robustness to degradation. Extensive experimental results on synthetic and real-world datasets verify the effectiveness of our method.
DOI: 10.5281/zenodo.17309780
title: SARS-CoV-2 Infektionen in Deutschland

description: Im Datensatz "SARS-CoV-2 Infektionen in Deutschland" des Robert Koch-Instituts werden die Daten basierend auf Meldungen zu SARS-CoV-2 Infektionen nach dem Infektionsschutzgesetz (IfSG) bereitgestellt. Darin enthalten sind Informationen auf Landkreisebene zu bestätigten Infektionen, Todesfällen und Genesungen, differenziert nach Altersgruppen und Geschlecht. Diese Daten dienen als Grundlage für epidemiologische Bewertungen und gesundheitspolitische Maßnahmen.
DOI: 10.21979/n9/cioe8y
title: Eliminating Feature Ambiguity for Few-Shot Segmentation

description: 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. However, due to the large receptive fields in deep layers of the backbone, the extracted query and support FG features are inevitably mingled with background (BG) features, impeding the FG-FG matching in cross attention. Hence, the query FG features are fused with less support FG features, i.e., the support information is not well utilized. This paper presents a novel plug-in termed ambiguity elimination network (AENet), which can be plugged into any existing cross attention-based FSS methods. The main idea is to mine discriminative query FG regions to rectify the ambiguous FG features, increasing the proportion of FG information, so as to suppress the negative impacts of the doped BG features. In this way, the FG-FG matching is naturally enhanced. We plug AENet into three baselines CyCTR, SCCAN and HDMNet for evaluation, and their scores are improved by large margins, e.g., the 1-shot performance of SCCAN can be improved by 3.0%+ on both PASCAL-5i and COCO-20i.
DOI: 10.21979/n9/fmvnyy
title: Kalman-Inspired Feature Propagation for Video Face Super-Resolution

description: 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 independently on individual video frames. These paradigms encounter challenges either in reconstructing facial details or maintaining temporal consistency. To address these issues, we introduce a novel framework called Kalman-inspired Feature Propagation (KEEP), designed to maintain a stable face prior over time. The Kalman filtering principles offer our method a recurrent ability to use the information from previously restored frames to guide and regulate the restoration process of the current frame. Extensive experiments demonstrate the effectiveness of our method in capturing facial details consistently across video frames.
DOI: 10.21979/n9/zppmt8
title: MOWA: Multiple-in-One Image Warping Model

description: While recent image warping approaches achieved remarkable success on existing benchmarks, they still require training separate models for each specific task and cannot generalize well to different camera models or customized manipulations. To address diverse types of warping in practice, we propose a Multiple-in-One image WArping model (named MOWA) in this work. Specifically, we mitigate the difficulty of multi-task learning by disentangling the motion estimation at both the region level and pixel level. To further enable dynamic task-aware image warping, we introduce a lightweight point-based classifier that predicts the task type, serving as prompts to modulate the feature maps for more accurate estimation. To our knowledge, this is the first work that solves multiple practical warping tasks in one single model. Extensive experiments demonstrate that our MOWA, which is trained on six tasks for multiple-in-one image warping, outperforms state-of-the-art task-specific models across most tasks. Moreover, MOWA also exhibits promising potential to generalize into unseen scenes, as evidenced by cross-domain and zero-shot evaluations.
DOI: 10.6084/m9.figshare.30325252
title: IDH-mutant ATRX loss/intact GL261 RNA-seq and IDH-mutant human gliomas RNA-seq

description: ATRX (alpha-thalassemia/mental retardation syndrome X-linked) is a critical chromatin remodeling factor widely implicated in glioma pathobiology, especially in IDH-mutant low-grade gliomas where ATRX loss frequently occurs. ATRX deficiency synergizes with mutant IDH1 to promote genome instability, modify gene expression networks, and alter tumor phenotypes. Importantly, ATRX loss has been associated with regulation of cellular senescence, the senescence-associated secretory phenotype (SASP), and immune cell recruitment within the glioma microenvironment. Mouse glioma GL261 cells were cultured in DMEM (high glucose) supplemented with 10% fetal bovine serum and 1% penicillin/streptomycin. To generate IDH-mutant GL261 cells with or without ATRX deficiency, mut-IDH1R132H and shRNA targeting murine Atrx (CCCACGGATGAGAATGTAAAT; both from Genechem, China) were stably introduced to establish IDH-mutant GL261 cells with intact or loss of ATRX function.
DOI: 10.6084/m9.figshare.30325252.v1
title: IDH-mutant ATRX loss/intact GL261 RNA-seq and IDH-mutant human gliomas RNA-seq

description: ATRX (alpha-thalassemia/mental retardation syndrome X-linked) is a critical chromatin remodeling factor widely implicated in glioma pathobiology, especially in IDH-mutant low-grade gliomas where ATRX loss frequently occurs. ATRX deficiency synergizes with mutant IDH1 to promote genome instability, modify gene expression networks, and alter tumor phenotypes. Importantly, ATRX loss has been associated with regulation of cellular senescence, the senescence-associated secretory phenotype (SASP), and immune cell recruitment within the glioma microenvironment. Mouse glioma GL261 cells were cultured in DMEM (high glucose) supplemented with 10% fetal bovine serum and 1% penicillin/streptomycin. To generate IDH-mutant GL261 cells with or without ATRX deficiency, mut-IDH1R132H and shRNA targeting murine Atrx (CCCACGGATGAGAATGTAAAT; both from Genechem, China) were stably introduced to establish IDH-mutant GL261 cells with intact or loss of ATRX function.
DOI: 10.5281/zenodo.15427988
title: Dataset for "Unlocking early fault-tolerant quantum computing with mitigated magic dilution"
DOI: 10.5281/zenodo.16783316
title: Dataset for "Unlocking early fault-tolerant quantum computing with mitigated magic dilution"
DOI: 10.5517/ccdc.csd.cc54ymv
title: CCDC 153717: Experimental Crystal Structure Determination

description: Related Article: Khalid Doudin, Karl W. Törnroos|2017|J.Mol.Struct.|1137|506|doi:10.1016/j.molstruc.2017.02.069
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.ccpj4z9
title: CCDC 641141: Experimental Crystal Structure Determination

description: Related Article: Stefan Mebs, Simon Grabowsky, Diana Förster, Rainer Kickbusch, Monika Hart, Luke L. Daemen, Wolfgang Morgenroth, Peter Luger, Beate Paulus, Dieter Lentz|2010|J.Phys.Chem.A|114|10185|doi:10.1021/jp100995n
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.ccpj50c
title: CCDC 641142: Experimental Crystal Structure Determination

description: Related Article: Stefan Mebs, Simon Grabowsky, Diana Förster, Rainer Kickbusch, Monika Hart, Luke L. Daemen, Wolfgang Morgenroth, Peter Luger, Beate Paulus, Dieter Lentz|2010|J.Phys.Chem.A|114|10185|doi:10.1021/jp100995n
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.ccplysz
title: CCDC 643832: Experimental Crystal Structure Determination

description: Related Article: Xiao-Hang Zhang, Seon-Ho KIm, In-Su Lee, Chun-Ji Gao, Sung-Ik Yang, Kwang-Hyun Ahn|2007|Bull.Korean Chem.Soc.|28|1389|doi:10.5012/bkcs.2007.28.8.1389
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.ccpvbb5
title: CCDC 650917: Experimental Crystal Structure Determination

description: Related Article: Aman Bhalla, Garima Modi, Shamsher S. Bari, Anu Kumari, Shiwani Berry, Geeta Hundal|2017|Tetrahedron Lett.|58|1160|doi:10.1016/j.tetlet.2017.02.011
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.ccr6tc3
title: CCDC 691745: Experimental Crystal Structure Determination

description: Related Article: Shadia A. Galal, Ahmed S. Abdelsamie, Harukuni Tokuda, Nobutaka Suzuki, Akira Lida, Mahmoud M. ElHefnawi, Raghda A. Ramadan, Mona H.E. Atta, Hoda I. El Diwani|2011|Eur.J.Med.Chem.|46|327|doi:10.1016/j.ejmech.2010.11.022
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.ccs0chm
title: CCDC 715340: Experimental Crystal Structure Determination

description: Related Article: Shadia A. Galal, Ahmed S. Abdelsamie, Harukuni Tokuda, Nobutaka Suzuki, Akira Lida, Mahmoud M. ElHefnawi, Raghda A. Ramadan, Mona H.E. Atta, Hoda I. El Diwani|2011|Eur.J.Med.Chem.|46|327|doi:10.1016/j.ejmech.2010.11.022
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.ccs2ln2
title: CCDC 717484: Experimental Crystal Structure Determination

description: Related Article: Shadia A. Galal, Ahmed S. Abdelsamie, Harukuni Tokuda, Nobutaka Suzuki, Akira Lida, Mahmoud M. ElHefnawi, Raghda A. Ramadan, Mona H.E. Atta, Hoda I. El Diwani|2011|Eur.J.Med.Chem.|46|327|doi:10.1016/j.ejmech.2010.11.022
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.ccs6lyh
title: CCDC 721337: Experimental Crystal Structure Determination

description: Related Article: Shadia A. Galal, Ahmed S. Abdelsamie, Harukuni Tokuda, Nobutaka Suzuki, Akira Lida, Mahmoud M. ElHefnawi, Raghda A. Ramadan, Mona H.E. Atta, Hoda I. El Diwani|2011|Eur.J.Med.Chem.|46|327|doi:10.1016/j.ejmech.2010.11.022
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.ccs6lzj
title: CCDC 721338: Experimental Crystal Structure Determination

description: Related Article: Shadia A. Galal, Ahmed S. Abdelsamie, Harukuni Tokuda, Nobutaka Suzuki, Akira Lida, Mahmoud M. ElHefnawi, Raghda A. Ramadan, Mona H.E. Atta, Hoda I. El Diwani|2011|Eur.J.Med.Chem.|46|327|doi:10.1016/j.ejmech.2010.11.022
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.ccsff6t
title: CCDC 727886: Experimental Crystal Structure Determination

description: Related Article: Nasrin Oroujzadeh, Khodayar Gholivand|2016|J.Iran.Chem.Soc.|13|847|doi:10.1007/s13738-015-0800-z
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.ccsff7v
title: CCDC 727887: Experimental Crystal Structure Determination

description: Related Article: Nasrin Oroujzadeh, Khodayar Gholivand|2016|J.Iran.Chem.Soc.|13|847|doi:10.1007/s13738-015-0800-z
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.ccstjz2
title: CCDC 739535: Experimental Crystal Structure Determination

description: Related Article: Lei Wu, Ming Xue, Gerald Chaplais, Guang-Shan Zhu, Shi-Lun Qiu, Angelique Simon-Masseron, Joel Patarin|2014|Adv.Mater.Res.|834/836|543|doi:10.4028/www.scientific.net/AMR.834-836.543
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.ccwcbvd
title: CCDC 815264: Experimental Crystal Structure Determination

description: Related Article: N. Burcu Arslan, Canan Kazak, Fatma Aydın|2015|Mol.Phys.|113|608|doi:10.1080/00268976.2014.961986
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccv7w20
title: CCDC 782132: Experimental Crystal Structure Determination

description: Related Article: Muhammad Shabbir, Zareen Akhter, Iqbal Ahmad, Safeer Ahmed, Michael Bolte, Vickie McKee|2017|Bioorg.Chem.|75|224|doi:10.1016/j.bioorg.2017.10.002
description: Related Article: Z.Akhter, M.Bolte|2010|CSD Communication|||
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccv7w42
title: CCDC 782134: Experimental Crystal Structure Determination

description: Related Article: Muhammad Shabbir, Zareen Akhter, Iqbal Ahmad, Safeer Ahmed, Michael Bolte, Vickie McKee|2017|Bioorg.Chem.|75|224|doi:10.1016/j.bioorg.2017.10.002
description: Related Article: Z.Akhter, M.Bolte|2010|CSD Communication|||
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.