cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.57702/bw3hjwag
title: VCTK Corpus

description: The VCTK corpus is an English multi-speaker dataset, with 44 hours of audio spoken by 109 native English speakers.
DOI: 10.57702/08et5ajj
title: Walker-2D

description: The dataset used in the paper is a set of demonstrations of a bipedal robot performing different moving behaviors.
DOI: 10.57702/s6vfefww
title: FaceForensics and Free-form Video Inpainting

description: Free-form video inpainting with 3d gated convolution and temporal patchgan
DOI: 10.57702/eenzh502
title: Visualizing MuZero Models

description: MuZero, a model-based reinforcement learning algorithm that uses a value equivalent dynamics model.
DOI: 10.57702/gepvlxqx
title: ANTNets: Mobile Convolutional Neural Networks for Resource Efﬁcient Image Classiﬁcation

description: Deep convolutional neural networks have achieved remarkable success in computer vision. However, deep neural networks require large computing resources to achieve high performance. Although depthwise separable convolution can be an efficient module to approximate a standard convolution, it often leads to reduced representational power of networks. In this paper, under budget constraints such as computational cost (MAdds) and the parameter count, we propose a novel basic architectural block, ANTBlock. It boosts the representational power by modeling, in a high dimensional space, interdependency of channels between a depthwise convolution layer and a projection layer in the ANTBlocks. Our experiments show that ANTNet built by a sequence of ANTBlocks, consistently outperforms state-of-the-art low-cost mobile convolutional neural networks across multiple datasets. On CIFAR100, our model achieves 75.7% top-1 accuracy, which is 1.5% higher than MobileNetV2 with 8.3% fewer parameters and 19.6% less computational cost. On ImageNet, our model achieves 72.8% top-1 accuracy, which is 0.8% improvement, with 157.7ms (20% faster) on iPhone 5s over MobileNetV2.
DOI: 10.57702/tqdrbm84
title: Wild Motion-Caption (WMC)

description: A large-scale Wild Motion-Caption dataset (WMC) is constructed, which comprises 141k motion-caption pairs.
DOI: 10.57702/ruct1whq
title: Test1200

description: The dataset used in the paper for image deraining task
DOI: 10.57702/kkgp8ib0
title: S1000

description: The dataset used in the paper for underwater image enhancement task
DOI: 10.57702/lgsl0np4
title: Fact-checking article dataset

description: The dataset provides non-anonymized pairs of an original tweet and its reply in which FC-articles, from two major fact-checking sites snopes.com and politifact.com, are embedded.
DOI: 10.57702/cl4g6sef
title: Pipeline Repair Problem

description: The dataset used in the pipeline repair problem, where a robot inspects and repairs a linear pipeline with potentially several rupture sites under partial information about the state of the pipeline.
DOI: 10.57702/xmga0rgh
title: R-CNN minus R

description: Deep convolutional neural networks (CNNs) have had a major impact in most areas of image understanding, including object category detection.
DOI: 10.57702/ic0viytn
title: NeRF-Det++

description: NeRF-Det++: Incorporating Semantic Cues and Perspective-aware Depth Supervision for Indoor Multi-View 3D Detection
DOI: 10.57702/7yonuw2g
title: LivDet-Iris 2020

description: LivDet-Iris 2020 competition database, Iris-CL1, iris-printed-CL1, Warsaw-BioBase-Post-Mortem-Iris v3.0
DOI: 10.57702/6pqg6wfz
title: Iris-CL1

description: Iris-CL1, iris-printed-CL1, Warsaw-BioBase-Post-Mortem-Iris v3.0
DOI: 10.57702/6k3fyanj
title: Iris-printed-CL1

description: Iris-printed-CL1, Warsaw-BioBase-Post-Mortem-Iris v3.0
DOI: 10.57702/vdr2pkhc
title: Warsaw-BioBase-Post-Mortem-Iris v3.0

description: Warsaw-BioBase-Post-Mortem-Iris v3.0
DOI: 10.57702/k0mk9vhq
title: SVHN to MNIST

description: The dataset used for image-to-image transformation with auxiliary condition
DOI: 10.57702/hq388yar
title: TownCentre

description: The dataset used for image-to-image transformation with auxiliary condition
DOI: 10.57702/n4lecjfj
title: CocoChorales

description: The CocoChorales dataset consists of 240,000 pieces, and each of them is a standard four-part chorale (Soprano, Alto, Tenor, Bass) in 4/4 time of 8 bars.
DOI: 10.57702/fkbfmx93
title: Unsupervised Clustered Federated Learning in Complex Multi-source Acoustic Environments

description: The dataset is a complex multi-source acoustic environment and an improved algorithm for the estimation of source-dominated microphone clusters in acoustic sensor networks.
DOI: 10.57702/wv0ae8wd
title: Human3.6m: Large scale datasets and predictive methods for 3D human sensing in natural environments

description: A large-scale dataset for 3D human sensing in natural environments.
DOI: 10.57702/1clwm3af
title: ESIM

description: The dataset used in the paper is a simulation of five sequences with different motion trajectories and background textures using the event camera simulator ESIM.
DOI: 10.57702/rds7gvgt
title: TUM-VIE

description: The dataset used in the paper is the TUM-VIE dataset, which contains a stereo visual-inertial event dataset.
DOI: 10.57702/kra6ecdq
title: Degenerate Swin to Win: Plain Window-based Transformer without Sophisticated Operations

description: The proposed Win Transformer achieves consistently superior performance than Swin Transformer on multiple computer vision tasks, including image recognition, semantic segmentation, and object detection.
DOI: 10.57702/s7a2n92j
title: Depth Over RGB: Automatic Evaluation of Open Surgery Skills Using Depth Camera

description: A dataset recorded with Azure Kinect for evaluating open surgery skills using depth camera.
DOI: 10.57702/or7xrf8c
title: Authors' Own Dataset

description: The authors' own dataset is used for object detection in thermal images.
DOI: 10.57702/muoux04m
title: Open-CLIP

description: Open-CLIP dataset contains pre-trained CLIP models.
DOI: 10.57702/5im9c751
title: 3DeformRS: Certifying Spatial Deformations on Point Clouds

description: 3D computer vision models are commonly used in security-critical applications such as autonomous driving and surgical robotics. Emerging concerns over the robustness of these models against real-world deformations must be addressed practically and reliably.
DOI: 10.57702/epmadm14
title: Two-level Group Convolution

description: The proposed two-level group convolution is suitable for distributed memory computing and robust with respect to the large number of groups.
DOI: 10.57702/wydudoyx
title: WideResNet-28-10 and WideResNet-34-2

description: The WideResNet-28-10 and WideResNet-34-2 datasets are used to evaluate the performance of the proposed two-level group convolution.
DOI: 10.57702/79a9zi1f
title: Stock Prices Dataset

description: Hewlett Packard, Yahoo, and Starbucks stock prices dataset.
DOI: 10.57702/hv8ecfds
title: Dynamic Task and Weight Prioritization Curriculum

description: This paper explores post-disaster analytics using multimodal deep learning models trained with curriculum learning method.
DOI: 10.57702/hhcm4qm2
title: inﬁMNIST Dataset

description: The inﬁMNIST dataset is a large collection of images of handwritten digits with infinite transformations.
DOI: 10.57702/ae001wyn
title: LFW Dataset

description: The LFW dataset is a large collection of images of faces with various expressions and poses.
DOI: 10.57702/6o6yanhg
title: VLP

description: The Vehicle License Plate (VLP) dataset is a dataset for image recognition and image processing tasks.
DOI: 10.57702/gvw4iqau
title: arXiv:2012.15827 - Osco et al. (2021)

description: A dataset of RGB images from corn and citrus fields with plantation-rows and plants detected and counted.
DOI: 10.57702/ggzvdc1o
title: Sparse Resnet50 model

description: The dataset used in this paper is a sparse Resnet50 model, which is a variant of the Resnet50 model with 80% sparsity.
DOI: 10.57702/1lru8uz6
title: Realistic US Simulation from CT Volume

description: In silico bone US simulation framework that synthesizes realistic US images from diagnostic CT volume.
DOI: 10.57702/32kixxv8
title: Lightweight Vision Transformer for US Segmentation

description: Lightweight vision transformer for US segmentation.
DOI: 10.57702/zzs4376q
title: SynText

description: The dataset used in the paper for scene text manipulation with diffusion models.
DOI: 10.57702/l70mofsw
title: ICDAR 2013 Robust Reading Competition

description: The ICDAR 2013 robust reading competition dataset.
DOI: 10.57702/jpug155o
title: COVID-19 Open Data

description: The dataset is used to demonstrate the application of the Computational Hypergraph Discovery (CHD) framework to recover functional dependencies from COVID-19 open data.
DOI: 10.57702/d6gn2fg4
title: Cell Signaling Network

description: The dataset is a flow cytometry data set for estimating a cell signaling network. The data was processed to be approximately Gaussian using a log transform after shifting the data to be non-negative.
DOI: 10.57702/h3j4plms
title: CIFAR-10, CIFAR-100, and CelebA

description: The dataset used in the paper is CIFAR-10 and CIFAR-100 for image classification, and CelebA for image-to-image translation.
DOI: 10.57702/8q045ihj
title: ResNet-20, ResNet-32, ResNet-56, and ResNet-110

description: The dataset used in the paper is ResNet-20, ResNet-32, ResNet-56, and ResNet-110 for image classification.
DOI: 10.57702/ccyk9e39
title: StarGAN

description: A dataset for image-to-image translation using generative adversarial networks.
DOI: 10.57702/relvufed
title: ACN-Data

description: ACN-Data: Analysis and applications of an open EV charging dataset
DOI: 10.57702/d843g4ck
title: Neuro-Causal Factor Analysis

description: NCFA models used in this paper are a subfamily of the models known as MeDIL causal models, originally introduced in (Markham and Grosse-Wentrup, 2020).
DOI: 10.57702/w0fq0hhn
title: MobileSAMv2: Faster Segment Anything to Everything

description: Segment anything model (SAM) addresses two practical yet challenging segmentation tasks: segment anything (SegAny), which utilizes a certain point to predict the mask for a single object of interest, and segment everything (SegEvery), which predicts the masks for all objects on the image.
DOI: 10.57702/bc95d7b0
title: LVIS: A dataset for large vocabulary instance segmentation

description: LVIS: A dataset for large vocabulary instance segmentation.
DOI: 10.57702/1zjw8in4
title: LVIS: A dataset for segment anything model (SAM)

description: A dataset for segment anything model (SAM) to evaluate its performance.
DOI: 10.57702/j4j2a4uq
title: EclipSE: Efficient Long-range Video Retrieval using Sight and Sound

description: EclipSE: Efficient Long-range Video Retrieval using Sight and Sound
DOI: 10.57702/7u6uo9vy
title: QVHighlights: Detecting moments and highlights in videos via natural language queries

description: QVHighlights: Detecting moments and highlights in videos via natural language queries
DOI: 10.57702/y5vw7fyj
title: Hollywood in homes: Crowdsourcing data collection for activity understanding

description: Hollywood in homes: Crowdsourcing data collection for activity understanding.
DOI: 10.57702/9ygqpwrq
title: Vidu

description: Vidu is a high-definition text-to-video generator that demonstrates strong abilities in various aspects, including duration, coherence, and dynamism of the generated videos, on par with Sora.
DOI: 10.57702/xhjxn22l
title: PASCAL-Context Dataset

description: The PASCAL-Context dataset comprises 4,998 images for training and 5,105 images for testing. This dataset offers dense labels for four tasks including semantic segmentation, human parsing, surface normal estimation, and saliency detection.
DOI: 10.57702/87gojwd0
title: StochDiff

description: The dataset used in this paper for stochastic time series forecasting.
DOI: 10.57702/lpttv1o1
title: MMG

description: The dataset used in this paper for stochastic time series forecasting.
DOI: 10.57702/ud7dwaef
title: Weather

description: The dataset used in this paper for stochastic time series forecasting.
DOI: 10.57702/zwm8htab
title: Exchange

description: The dataset used in this paper for stochastic time series forecasting.
DOI: 10.57702/zf4ahd45
title: Solar

description: The dataset used in this paper for stochastic time series forecasting.
DOI: 10.57702/1njn76sz
title: Cambridge Landmarks

description: The Cambridge Landmarks dataset contains 5 different large outdoor scenes of landmarks in the city of Cambridge.
DOI: 10.57702/z6omoed1
title: Tufano dataset

description: The Tufano dataset is used for automatic program repair. It contains buggy programs and their corresponding correct versions.
DOI: 10.57702/cfubnrkp
title: PEMSD7(M)

description: Urban traffic speed prediction aims to estimate the future traffic speed for improving urban transportation services. The proposed framework is a generic wrapper-style solution to enhance the current GNN-based methods by integrating cross-scale topology-free patterns.
DOI: 10.57702/3c8dixfu
title: Landslide4Sense

description: The Landslide4Sense competition provides a landslide benchmark data set with globally distributed multi-source satellite imagery.
DOI: 10.57702/gg8pi33i
title: Landslide Detection Dataset

description: The dataset contains images of landslides and their corresponding labels.
DOI: 10.57702/ezbg5ooo
title: Graphics

description: Graphics is a well-balanced collection of images containing 14,248 human faces from different ethnic groups and rich in a wide range of learnable attributes.
DOI: 10.57702/495p3iv9
title: BeNeRF

description: A dataset for recovering the neural radiance fields and camera motion trajectory from a single blurry image and its corresponding event stream.
DOI: 10.57702/khfitbqr
title: Imagenette and Imagewoof

description: The dataset used in this paper is the Imagenette and Imagewoof datasets.
DOI: 10.57702/43opd9jn
title: CloudBrain-ReconAI

description: CloudBrain-ReconAI is an online platform for MRI reconstruction and image quality evaluation.
DOI: 10.57702/0pzii9bm
title: FastMRI

description: Fast and accurate reconstruction of magnetic resonance (MR) images from under-sampled data is important in many clinical applications. In recent years, deep learning-based methods have been shown to produce superior performance on MR image reconstruction. However, these methods require large amounts of data which is difficult to collect and share due to the high cost of acquisition and medical data privacy regulations.
DOI: 10.57702/d8qk7qtm
title: PAIR

description: A paired phase and magnitude reconstruction for advanced diffusion-weighted imaging.
DOI: 10.57702/r2j8wfdg
title: Generalised Structural CNNs (SCNNs) for time series data with arbitrary graph topology

description: Human kinematics data, captured during daily life activities, with a tree-like structure.
DOI: 10.57702/c1w5v3ns
title: LatteGAN: Visually Guided Language Attention for Multi-Turn Text-Conditioned Image Manipulation

description: Text-guided image manipulation tasks have recently gained attention in the vision-and-language community. The GeNeVA task is a multi-turn text-conditioned image generation (MTIM) task. It involves two participants: a Teller that instructs how to modify the image, and a Drawer that draws the image according to the Teller’s instructions.
DOI: 10.57702/j2sr20dv
title: i-CLEVR

description: i-CLEVR is a synthetic dataset generated using the CLEVR engine. Each scene contains five image-instruction pairs. Starting from a background image, new objects are added sequentially in a scene.
DOI: 10.57702/2vqxexco
title: Airplanes Dataset

description: The dataset used for video generation and evaluation of the proposed iVGAN model.
DOI: 10.57702/xno66u67
title: SERGIO: A Single-Cell Expression Simulator Guided by Gene Regulatory Networks

description: SERGIO is a technique for simulating single-cell RNA sequencing dynamics using stochastic differential equations.
DOI: 10.57702/zffh2wak
title: Mapping Information-Rich Genotype-Phenotype Landscapes with Genome-Scale Perturb-Seq

description: Replogle dataset is a collection of single-cell RNA sequencing data from cancer-related genes.
DOI: 10.57702/hlpz5mfl
title: Wide-Slice Residual Networks for Food Recognition

description: The proposed WISeR architecture combines features extracted from two main network branches. The residual learning branch provides a deep hierarchy which is able to capture the food traits of the majority of the existing food categories. The slice convolution branch captures the vertical layers of the food dishes which present such a peculiarity.
DOI: 10.57702/5cefz9hc
title: UECFood256

description: The UECFood256 dataset contains 256 different food dishes which are represented in about 32,000 images.
DOI: 10.57702/25aibzlv
title: Coarse-Fine Granularity Prompts dataset (CFP)

description: Coarse-Fine Granularity Prompts dataset (CFP) is a collection of 81,910 data instances from popular text-to-image community.
DOI: 10.57702/g6zd97up
title: NIMA, TReS, and DB-

description: NIMA, TReS, and DB- are datasets used for image quality and aesthetic assessment.
DOI: 10.57702/tzoyjnlo
title: MUSIQ and KonIQ

description: MUSIQ and KonIQ are datasets used for image quality and aesthetic assessment.
DOI: 10.57702/ykfhti3n
title: SARS-CoV-2 phylogenetic tree

description: The SARS-CoV-2 phylogenetic tree contains the full phylogeny of SARS-CoV-2.
DOI: 10.57702/npptf2ww
title: EPA-ng dataset

description: The EPA-ng dataset contains evolutionary placement of genetic sequences.
DOI: 10.57702/kph4w0ad
title: RealNet

description: RealNet: Combining Optimized Object Detection with Information Fusion Depth Estimation Co-Design Method on IoT
DOI: 10.6084/m9.figshare.27936756
title: GSE185381 29_Nov_2024 sc_adult_AML

description: GSE185381 sc_adult_AML
DOI: 10.6084/m9.figshare.27936756.v7
title: GSE185381 29_Nov_2024 sc_adult_AML

description: GSE185381 sc_adult_AML
DOI: 10.6084/m9.figshare.27936756.v8
title: GSE185381 29_Nov_2024 sc_adult_AML

description: GSE185381 sc_adult_AML
DOI: 10.57702/l403ubyc
title: Nerf dataset

description: The Nerf dataset is a collection of 3D scenes rendered using neural radiance fields.
DOI: 10.57702/6c9kyys0
title: TE-YOLOF: Tiny and Efficient YOLOF for Blood Cell Detection

description: Blood cell detection in microscopic images is an essential branch of medical image processing research.
DOI: 10.57702/lu6n3v30
title: Cell counting algorithm based on YOLOv3 and image density estimation

description: Cell counting algorithm based on YOLOv3 and image density estimation.
DOI: 10.57702/qab853f6
title: A fast and yet efficient YOLOv3 for blood cell detection

description: A fast and yet efficient YOLOv3 for blood cell detection.
DOI: 10.57702/ta5r3qvx
title: LIVEC

description: The dataset used in the paper for blind image quality assessment, consisting of images with various distortions.
DOI: 10.57702/yist42in
title: KADID

description: The dataset used in the paper for blind image quality assessment, consisting of images with various distortions.
DOI: 10.57702/71ho18dj
title: AIGC2023

description: The dataset used in the paper for image quality assessment, consisting of AI-generated images.
DOI: 10.57702/axw7nckw
title: Videomamba

description: The dataset used in the paper for video quality assessment, consisting of videos with various distortions.
DOI: 10.57702/uktgq371
title: MNIST, CIFAR-10, CIFAR-100, Tiny-ImageNet, VGG-like

description: The dataset used in the paper is MNIST, CIFAR-10, CIFAR-100, Tiny-ImageNet, and VGG-like.
DOI: 10.57702/x8cu58rg
title: Event-based Eye-Tracking-AIS2024 dataset

description: Eye-tracking technology is integral to numerous consumer electronics applications, particularly in the realm of virtual and augmented reality (VR/AR). These applications demand solutions that excel in three crucial aspects: low-latency, low-power consumption, and precision.
DOI: 10.57702/cp4gy07h
title: Menet: A memory-based network with dual-branch for efficient event stream processing

description: Menet: A memory-based network with dual-branch for efficient event stream processing.
DOI: 10.57702/hma59gwm
title: Next Day Wildﬁre Spread

description: A curated, large-scale, multivariate data set of historical wildﬁres aggregating nearly a decade of remote-sensing data across the United States.
DOI: 10.57702/7na1djtq
title: Oxford RobotCar Dataset

description: The Oxford RobotCar Dataset is a collection of images and videos of a car driving on various roads and conditions.
DOI: 10.57702/gz2gq5j5
title: GFDL-ESM4

description: GFDL-ESM4 climate model output
DOI: 10.57702/icimi6d6
title: ResNet-20 and ResNet-32

description: The dataset used in this paper is the ResNet-20 and ResNet-32 datasets, which are variants of the ResNet-50 model.
DOI: 10.57702/3v6l8ojc
title: ConvNeXt-T and ConvNeXt-S

description: The dataset used in this paper is the ConvNeXt-T and ConvNeXt-S datasets, which are variants of the ConvNeXt model.
DOI: 10.57702/zqbh1hd7
title: ImageNet∗

description: A dataset interface for the ImageNet dataset, allowing for the generation of counterfactual examples that exhibit a specified shift.
DOI: 10.57702/k0m8qnxu
title: Oxford-IIIT Pet

description: A dataset of images of pets, used for fine-grained visual classification.
DOI: 10.57702/055z53bg
title: SqueezeSegV3: Spatially-Adaptive Convolution for Efficient Point-Cloud Segmentation

description: LiDAR point-cloud segmentation is an important problem for many applications. For large-scale point cloud segmentation, the de facto method is to project a 3D point cloud to get a 2D LiDAR image and use convolutions to process it.
DOI: 10.57702/pqeb0x5y
title: Diffusion Hyperfeatures: Searching Through Time and Space for Semantic Correspondence

description: Diffusion Hyperfeatures: Searching Through Time and Space for Semantic Correspondence
DOI: 10.57702/uv76t6mb
title: SPair-71k

description: The proposed method, dubbed Dynamic Hyperpixel Flow, learns to compose hypercolumn features on the fly by selecting a small number of relevant layers from a deep convolutional neural network.
DOI: 10.5281/zenodo.14258630
title: Dataset of the publication: Interplay between optical emission and magnetism in the van der Waals magnetic semiconductor CrSBr in the two-dimensional limit

description: Dataset of the publication: Interplay between optical emission and magnetism in the van der Waals magnetic semiconductor CrSBr in the two-dimensional limit

DOI: 10.1021/acsnano.3c00375

F. Marques-Moros, C. Boix-Constant, S. Mañas-Valero, J. Canet-Ferrer, E. Coronado

ACS Nano, 17, 14, 13224-13231 (2023)
DOI: 10.5281/zenodo.14258631
title: Dataset of the publication: Interplay between optical emission and magnetism in the van der Waals magnetic semiconductor CrSBr in the two-dimensional limit

description: Dataset of the publication: Interplay between optical emission and magnetism in the van der Waals magnetic semiconductor CrSBr in the two-dimensional limit

DOI: 10.1021/acsnano.3c00375

F. Marques-Moros, C. Boix-Constant, S. Mañas-Valero, J. Canet-Ferrer, E. Coronado

ACS Nano, 17, 14, 13224-13231 (2023)
DOI: 10.57702/y2thy9t3
title: Tube-Link: A Flexible Cross Tube Framework for Universal Video Segmentation

description: Video segmentation aims to segment and track every pixel in diverse scenarios accurately. This paper presents Tube-Link, a versatile framework that addresses multiple core tasks of video segmentation with a unified architecture.
DOI: 10.57702/p88hl4xr
title: VIPSeg: A Large-Scale Benchmark for Video Panoptic Segmentation

description: Large-scale video panoptic segmentation in the wild: A benchmark.
DOI: 10.57702/39xbbxmu
title: Panoptic-partformer: Learning a Unified Model for Panoptic Part Segmentation

description: Panoptic-partformer: Learning a unified model for panoptic part segmentation.
DOI: 10.57702/dvuaj1d9
title: Video k-net: A Simple, Strong, and Unified Baseline for Video Segmentation

description: Video k-net: A simple, strong, and unified baseline for video segmentation.
DOI: 10.57702/cjb0q89q
title: Human3.6m

description: Human3.6m: Large scale datasets and predictive methods for 3D human sensing in natural environments.