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Efficient 3D Brain Tumor Segmentation with Axial-Coronal-Sagittal
Embedding | 2506.00434v1 | ronneberger_unet_miccai_2015 | \cite{ronneberger_unet_miccai_2015} | U-net: Convolutional networks for biomedical image segmentation | null | null | true | false | Ronneberger, Olaf and Fischer, Philipp and Brox, Thomas | 2,015 | null | null | null | null | U-net: Convolutional networks for biomedical image segmentation | U-Net: Convolutional Networks for Biomedical Image Segmentation | http://arxiv.org/pdf/1505.04597v1 | There is large consent that successful training of deep networks requires
many thousand annotated training samples. In this paper, we present a network
and training strategy that relies on the strong use of data augmentation to use
the available annotated samples more efficiently. The architecture consists of
a contrac... |
Efficient 3D Brain Tumor Segmentation with Axial-Coronal-Sagittal
Embedding | 2506.00434v1 | menze_tmi_2015 | \cite{menze_tmi_2015} | The {Multimodal} {Brain} {Tumor} {Image} {Segmentation} {Benchmark} ({BRATS}) | null | null | true | false | Menze, Bjoern H and Jakab, Andras and Bauer, Stefan and Kalpathy-Cramer, Jayashree and Farahani, Keyvan and Kirby, Justin and Burren, Yuliya and Porz, Nicole and Slotboom, Johannes and Wiest, Roland and others | 2,015 | null | null | null | IEEE TMI | The {Multimodal} {Brain} {Tumor} {Image} {Segmentation} {Benchmark} ({BRATS}) | The Multimodal Brain Tumor Image Segmentation Benchmark ... | https://pmc.ncbi.nlm.nih.gov/articles/PMC4833122/ | The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS) - PMC The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS) Find articles by Thomas J Taylor Find articles by Nicholas J Tustison [DOI00671-8)] [PMC free article] [PubMed] [Google Scholar00671-8&)] [DOI] [PMC free article] [PubMed] [Google Schol... |
Efficient 3D Brain Tumor Segmentation with Axial-Coronal-Sagittal
Embedding | 2506.00434v1 | bakas_arxiv_2019 | \cite{bakas_arxiv_2019} | Identifying the Best Machine Learning Algorithms for Brain Tumor
Segmentation, Progression Assessment, and Overall Survival Prediction in the
BRATS Challenge | http://arxiv.org/abs/1811.02629v3 | Gliomas are the most common primary brain malignancies, with different
degrees of aggressiveness, variable prognosis and various heterogeneous
histologic sub-regions, i.e., peritumoral edematous/invaded tissue, necrotic
core, active and non-enhancing core. This intrinsic heterogeneity is also
portrayed in their radio-p... | true | true | Bakas, Spyridon and Reyes, Mauricio and Jakab, Andras and Bauer, Stefan and Rempfler, Markus and Crimi, Alessandro and Shinohara, Russell Takeshi and Berger, Christoph and Ha, Sung Min and Rozycki, Martin and others | 2,018 | null | null | null | arXiv preprint arXiv:1811.02629 | Identifying the Best Machine Learning Algorithms for Brain Tumor
Segmentation, Progression Assessment, and Overall Survival Prediction in the
BRATS Challenge | Identifying the Best Machine Learning Algorithms for Brain Tumor ... | https://arxiv.org/abs/1811.02629 | View a PDF of the paper titled Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge, by Spyridon Bakas and 426 other authors View a PDF of the paper titled Identifying the Best Machine Learning Algorithms for Brain ... |
Efficient 3D Brain Tumor Segmentation with Axial-Coronal-Sagittal
Embedding | 2506.00434v1 | baid_arxiv_2021 | \cite{baid_arxiv_2021} | The RSNA-ASNR-MICCAI BraTS 2021 Benchmark on Brain Tumor Segmentation
and Radiogenomic Classification | http://arxiv.org/abs/2107.02314v2 | The BraTS 2021 challenge celebrates its 10th anniversary and is jointly
organized by the Radiological Society of North America (RSNA), the American
Society of Neuroradiology (ASNR), and the Medical Image Computing and Computer
Assisted Interventions (MICCAI) society. Since its inception, BraTS has been
focusing on bein... | true | true | Baid, Ujjwal and Ghodasara, Satyam and Mohan, Suyash and Bilello, Michel and Calabrese, Evan and Colak, Errol and Farahani, Keyvan and Kalpathy-Cramer, Jayashree and Kitamura, Felipe C and Pati, Sarthak and others | 2,021 | null | null | null | arXiv preprint arXiv:2107.02314 | The RSNA-ASNR-MICCAI BraTS 2021 Benchmark on Brain Tumor Segmentation
and Radiogenomic Classification | BraTS-Lighthouse 2025 Challenge - syn64153130 - Wiki | https://www.synapse.org/Synapse:syn64153130/wiki/631064 | [1] U.Baid, et al., The RSNA-ASNR-MICCAI BraTS 2021 Benchmark on Brain Tumor Segmentation and Radiogenomic Classification, arXiv:2107.02314, 2021. |
Efficient 3D Brain Tumor Segmentation with Axial-Coronal-Sagittal
Embedding | 2506.00434v1 | myronenko_miccai_2019 | \cite{myronenko_miccai_2019} | 3D MRI brain tumor segmentation using autoencoder regularization | http://arxiv.org/abs/1810.11654v3 | Automated segmentation of brain tumors from 3D magnetic resonance images
(MRIs) is necessary for the diagnosis, monitoring, and treatment planning of
the disease. Manual delineation practices require anatomical knowledge, are
expensive, time consuming and can be inaccurate due to human error. Here, we
describe a semant... | true | true | Myronenko, Andriy | 2,019 | null | null | null | null | 3D MRI brain tumor segmentation using autoencoder regularization | 3D MRI brain tumor segmentation using autoencoder regularization | http://arxiv.org/pdf/1810.11654v3 | Automated segmentation of brain tumors from 3D magnetic resonance images
(MRIs) is necessary for the diagnosis, monitoring, and treatment planning of
the disease. Manual delineation practices require anatomical knowledge, are
expensive, time consuming and can be inaccurate due to human error. Here, we
describe a semant... |
Efficient 3D Brain Tumor Segmentation with Axial-Coronal-Sagittal
Embedding | 2506.00434v1 | jiang_cascaded_unet_miccai_2020 | \cite{jiang_cascaded_unet_miccai_2020} | Two-stage cascaded u-net: 1st place solution to brats challenge 2019 segmentation task | null | null | true | false | Jiang, Zeyu and Ding, Changxing and Liu, Minfeng and Tao, Dacheng | 2,020 | null | null | null | null | Two-stage cascaded u-net: 1st place solution to brats challenge 2019 segmentation task | Two-Stage Cascaded U-Net: 1st Place Solution to BraTS Challenge ... | https://www.semanticscholar.org/paper/Two-Stage-Cascaded-U-Net%3A-1st-Place-Solution-to-Jiang-Ding/6eead90d63cc679263ef608121db075b78e03960 | A novel two-stage cascaded U-Net to segment the substructures of brain tumors from coarse to fine is devised and won the 1st place in the BraTS 2019 |
Efficient 3D Brain Tumor Segmentation with Axial-Coronal-Sagittal
Embedding | 2506.00434v1 | isensee_nnunet_miccai_2021 | \cite{isensee_nnunet_miccai_2021} | nnU-Net for Brain Tumor Segmentation | http://arxiv.org/abs/2011.00848v1 | We apply nnU-Net to the segmentation task of the BraTS 2020 challenge. The
unmodified nnU-Net baseline configuration already achieves a respectable
result. By incorporating BraTS-specific modifications regarding postprocessing,
region-based training, a more aggressive data augmentation as well as several
minor modifica... | true | true | Isensee, Fabian and J{\"a}ger, Paul F and Full, Peter M and Vollmuth, Philipp and Maier-Hein, Klaus H | 2,021 | null | null | null | null | nnU-Net for Brain Tumor Segmentation | Brain tumor segmentation with advanced nnU-Net - ScienceDirect.com | https://www.sciencedirect.com/science/article/pii/S2772528624000013 | This paper introduces an extended version of the nnU-Net architecture for brain tumor segmentation, addressing both adult (Glioma) and pediatric tumors. |
Efficient 3D Brain Tumor Segmentation with Axial-Coronal-Sagittal
Embedding | 2506.00434v1 | luu_miccai_2022 | \cite{luu_miccai_2022} | Extending nn-UNet for brain tumor segmentation | http://arxiv.org/abs/2112.04653v1 | Brain tumor segmentation is essential for the diagnosis and prognosis of
patients with gliomas. The brain tumor segmentation challenge has continued to
provide a great source of data to develop automatic algorithms to perform the
task. This paper describes our contribution to the 2021 competition. We
developed our meth... | true | true | Luu, Huan Minh and Park, Sung-Hong | 2,021 | null | null | null | null | Extending nn-UNet for brain tumor segmentation | Extending nn-UNet for Brain Tumor Segmentation | https://link.springer.com/chapter/10.1007/978-3-031-09002-8_16 | by HM Luu · 2021 · Cited by 185 — We extended the nn-UNet framework by using a larger network, replacing batch normalization with group normalization, and using axial attention |
Efficient 3D Brain Tumor Segmentation with Axial-Coronal-Sagittal
Embedding | 2506.00434v1 | zeineldin_miccai_2022 | \cite{zeineldin_miccai_2022} | Multimodal CNN Networks for Brain Tumor Segmentation in MRI: A BraTS
2022 Challenge Solution | http://arxiv.org/abs/2212.09310v1 | Automatic segmentation is essential for the brain tumor diagnosis, disease
prognosis, and follow-up therapy of patients with gliomas. Still, accurate
detection of gliomas and their sub-regions in multimodal MRI is very
challenging due to the variety of scanners and imaging protocols. Over the last
years, the BraTS Chal... | true | true | Zeineldin, Ramy A and Karar, Mohamed E and Burgert, Oliver and Mathis-Ullrich, Franziska | 2,022 | null | null | null | arXiv preprint arXiv:2212.09310 | Multimodal CNN Networks for Brain Tumor Segmentation in MRI: A BraTS
2022 Challenge Solution | Multimodal CNN Networks for Brain Tumor Segmentation in MRI | https://link.springer.com/chapter/10.1007/978-3-031-33842-7_11 | The BraTS challenge is designed to encourage research in the field of medical image segmentation, with a focus on segmenting brain tumors in MRI |
Efficient 3D Brain Tumor Segmentation with Axial-Coronal-Sagittal
Embedding | 2506.00434v1 | isensee_nnunet_nature_2021 | \cite{isensee_nnunet_nature_2021} | nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation | null | null | true | false | Isensee, Fabian and Jaeger, Paul F and Kohl, Simon AA and Petersen, Jens and Maier-Hein, Klaus H | 2,021 | null | null | null | Nature methods | nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation | nnU-Net: a self-configuring method for deep learning-based ... - Nature | https://www.nature.com/articles/s41592-020-01008-z | # nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation We developed nnU-Net, a deep learning-based segmentation method that automatically configures itself, including preprocessing, network architecture, training and post-processing for any new task. ### Variability and reproducibili... |
Efficient 3D Brain Tumor Segmentation with Axial-Coronal-Sagittal
Embedding | 2506.00434v1 | wang_transbts_miccai_2021 | \cite{wang_transbts_miccai_2021} | TransBTS: Multimodal Brain Tumor Segmentation Using Transformer | http://arxiv.org/abs/2103.04430v2 | Transformer, which can benefit from global (long-range) information modeling
using self-attention mechanisms, has been successful in natural language
processing and 2D image classification recently. However, both local and global
features are crucial for dense prediction tasks, especially for 3D medical
image segmentat... | true | true | Wang, Wenxuan and Chen, Chen and Ding, Meng and Yu, Hong and Zha, Sen and Li, Jiangyun | 2,021 | null | null | null | null | TransBTS: Multimodal Brain Tumor Segmentation Using Transformer | TransBTS: Multimodal Brain Tumor Segmentation Using Transformer | http://arxiv.org/pdf/2103.04430v2 | Transformer, which can benefit from global (long-range) information modeling
using self-attention mechanisms, has been successful in natural language
processing and 2D image classification recently. However, both local and global
features are crucial for dense prediction tasks, especially for 3D medical
image segmentat... |
Efficient 3D Brain Tumor Segmentation with Axial-Coronal-Sagittal
Embedding | 2506.00434v1 | swinunetr | \cite{swinunetr} | Swin UNETR: Swin Transformers for Semantic Segmentation of Brain Tumors
in MRI Images | http://arxiv.org/abs/2201.01266v1 | Semantic segmentation of brain tumors is a fundamental medical image analysis
task involving multiple MRI imaging modalities that can assist clinicians in
diagnosing the patient and successively studying the progression of the
malignant entity. In recent years, Fully Convolutional Neural Networks (FCNNs)
approaches hav... | true | true | Hatamizadeh, Ali and Nath, Vishwesh and Tang, Yucheng and Yang, Dong and Roth, Holger R and Xu, Daguang | 2,021 | null | null | null | null | Swin UNETR: Swin Transformers for Semantic Segmentation of Brain Tumors
in MRI Images | Swin Transformers for Semantic Segmentation of Brain Tumors in ... | https://arxiv.org/abs/2201.01266 | We propose a novel segmentation model termed Swin UNEt TRansformers (Swin UNETR). Specifically, the task of 3D brain tumor semantic segmentation is |
Efficient 3D Brain Tumor Segmentation with Axial-Coronal-Sagittal
Embedding | 2506.00434v1 | chen_med3d_arxiv_2019 | \cite{chen_med3d_arxiv_2019} | Med3D: Transfer Learning for 3D Medical Image Analysis | http://arxiv.org/abs/1904.00625v4 | The performance on deep learning is significantly affected by volume of
training data. Models pre-trained from massive dataset such as ImageNet become
a powerful weapon for speeding up training convergence and improving accuracy.
Similarly, models based on large dataset are important for the development of
deep learnin... | true | true | Chen, Sihong and Ma, Kai and Zheng, Yefeng | 2,019 | null | null | null | arXiv preprint arXiv:1904.00625 | Med3D: Transfer Learning for 3D Medical Image Analysis | Med3D: Transfer Learning for 3D Medical Image Analysis | http://arxiv.org/pdf/1904.00625v4 | The performance on deep learning is significantly affected by volume of
training data. Models pre-trained from massive dataset such as ImageNet become
a powerful weapon for speeding up training convergence and improving accuracy.
Similarly, models based on large dataset are important for the development of
deep learnin... |
Efficient 3D Brain Tumor Segmentation with Axial-Coronal-Sagittal
Embedding | 2506.00434v1 | zhu_modelgenesis_mia_2021 | \cite{zhu_modelgenesis_mia_2021} | Models Genesis | http://arxiv.org/abs/2004.07882v4 | Transfer learning from natural images to medical images has been established
as one of the most practical paradigms in deep learning for medical image
analysis. To fit this paradigm, however, 3D imaging tasks in the most prominent
imaging modalities (e.g., CT and MRI) have to be reformulated and solved in 2D,
losing ri... | true | true | Zhou, Zongwei and Sodha, Vatsal and Pang, Jiaxuan and Gotway, Michael B and Liang, Jianming | 2,021 | null | null | null | Medical image analysis | Models Genesis | Models Genesis | http://arxiv.org/pdf/2004.07882v4 | Transfer learning from natural images to medical images has been established
as one of the most practical paradigms in deep learning for medical image
analysis. To fit this paradigm, however, 3D imaging tasks in the most prominent
imaging modalities (e.g., CT and MRI) have to be reformulated and solved in 2D,
losing ri... |
Test-time Vocabulary Adaptation for Language-driven Object Detection | 2506.00333v1 | zhu2023survey | \cite{zhu2023survey} | A Survey on Open-Vocabulary Detection and Segmentation: Past, Present,
and Future | http://arxiv.org/abs/2307.09220v2 | As the most fundamental scene understanding tasks, object detection and
segmentation have made tremendous progress in deep learning era. Due to the
expensive manual labeling cost, the annotated categories in existing datasets
are often small-scale and pre-defined, i.e., state-of-the-art fully-supervised
detectors and s... | true | true | Zhu, Chaoyang and Chen, Long | 2,023 | null | null | null | null | A Survey on Open-Vocabulary Detection and Segmentation: Past, Present,
and Future | Awesome OVD-OVS - A Survey on Open-Vocabulary ... | https://github.com/seanzhuh/Awesome-Open-Vocabulary-Detection-and-Segmentation | Awesome OVD-OVS - A Survey on Open-Vocabulary Detection and Segmentation: Past, Present, and Future |
Test-time Vocabulary Adaptation for Language-driven Object Detection | 2506.00333v1 | radford2021learning | \cite{radford2021learning} | Learning Transferable Visual Models From Natural Language Supervision | http://arxiv.org/abs/2103.00020v1 | State-of-the-art computer vision systems are trained to predict a fixed set
of predetermined object categories. This restricted form of supervision limits
their generality and usability since additional labeled data is needed to
specify any other visual concept. Learning directly from raw text about images
is a promisi... | true | true | Radford, Alec and Kim, Jong Wook and Hallacy, Chris and Ramesh, Aditya and Goh, Gabriel and Agarwal, Sandhini and Sastry, Girish and Askell, Amanda and Mishkin, Pamela and Clark, Jack and Krueger, Gretchen and Sutskever, Ilya | 2,021 | null | null | null | null | Learning Transferable Visual Models From Natural Language Supervision | Learning Transferable Visual Models From Natural Language Supervision | http://arxiv.org/pdf/2103.00020v1 | State-of-the-art computer vision systems are trained to predict a fixed set
of predetermined object categories. This restricted form of supervision limits
their generality and usability since additional labeled data is needed to
specify any other visual concept. Learning directly from raw text about images
is a promisi... |
Test-time Vocabulary Adaptation for Language-driven Object Detection | 2506.00333v1 | lin2014microsoft | \cite{lin2014microsoft} | Microsoft COCO: Common Objects in Context | http://arxiv.org/abs/1405.0312v3 | We present a new dataset with the goal of advancing the state-of-the-art in
object recognition by placing the question of object recognition in the context
of the broader question of scene understanding. This is achieved by gathering
images of complex everyday scenes containing common objects in their natural
context. ... | true | true | Lin, Tsung-Yi and Maire, Michael and Belongie, Serge and Hays, James and Perona, Pietro and Ramanan, Deva and Doll{\'a}r, Piotr and Zitnick, C. Lawrence | 2,014 | null | null | null | null | Microsoft COCO: Common Objects in Context | Microsoft COCO: Common Objects in Context | http://arxiv.org/pdf/1405.0312v3 | We present a new dataset with the goal of advancing the state-of-the-art in
object recognition by placing the question of object recognition in the context
of the broader question of scene understanding. This is achieved by gathering
images of complex everyday scenes containing common objects in their natural
context. ... |
Test-time Vocabulary Adaptation for Language-driven Object Detection | 2506.00333v1 | gupta2019lvis | \cite{gupta2019lvis} | LVIS: A Dataset for Large Vocabulary Instance Segmentation | http://arxiv.org/abs/1908.03195v2 | Progress on object detection is enabled by datasets that focus the research
community's attention on open challenges. This process led us from simple
images to complex scenes and from bounding boxes to segmentation masks. In this
work, we introduce LVIS (pronounced `el-vis'): a new dataset for Large
Vocabulary Instance... | true | true | Gupta, Agrim and Dollar, Piotr and Girshick, Ross | 2,019 | null | null | null | null | LVIS: A Dataset for Large Vocabulary Instance Segmentation | LVIS: A Dataset for Large Vocabulary Instance Segmentation | http://arxiv.org/pdf/1908.03195v2 | Progress on object detection is enabled by datasets that focus the research
community's attention on open challenges. This process led us from simple
images to complex scenes and from bounding boxes to segmentation masks. In this
work, we introduce LVIS (pronounced `el-vis'): a new dataset for Large
Vocabulary Instance... |
Test-time Vocabulary Adaptation for Language-driven Object Detection | 2506.00333v1 | deng2009imagenet | \cite{deng2009imagenet} | {ImageNet: a Large-Scale Hierarchical Image Database} | null | null | true | false | Deng, Jia and Dong, Wei and Socher, Richard and Li, Li-Jia and Li, Kai and Fei-Fei, Li | 2,009 | null | null | null | null | {ImageNet: a Large-Scale Hierarchical Image Database} | (PDF) ImageNet: a Large-Scale Hierarchical Image Database | https://www.researchgate.net/publication/221361415_ImageNet_a_Large-Scale_Hierarchical_Image_Database | This paper offers a detailed analysis of ImageNet in its current state: 12 subtrees with 5247 synsets and 3.2 million images in total. |
Test-time Vocabulary Adaptation for Language-driven Object Detection | 2506.00333v1 | zhou2022detecting | \cite{zhou2022detecting} | Detecting Twenty-thousand Classes using Image-level Supervision | http://arxiv.org/abs/2201.02605v3 | Current object detectors are limited in vocabulary size due to the small
scale of detection datasets. Image classifiers, on the other hand, reason about
much larger vocabularies, as their datasets are larger and easier to collect.
We propose Detic, which simply trains the classifiers of a detector on image
classificati... | true | true | Zhou, Xingyi and Girdhar, Rohit and Joulin, Armand and Kr{\"a}henb{\"u}hl, Philipp and Misra, Ishan | 2,022 | null | null | null | null | Detecting Twenty-thousand Classes using Image-level Supervision | [PDF] Detecting Twenty-thousand Classes using Image-level Supervision | https://www.ecva.net/papers/eccv_2022/papers_ECCV/papers/136690344.pdf | We propose Detic, which simply trains the classifiers of a detector on image classification data and thus expands the vocabulary of detectors to tens of |
Test-time Vocabulary Adaptation for Language-driven Object Detection | 2506.00333v1 | zhong2022regionclip | \cite{zhong2022regionclip} | RegionCLIP: Region-based Language-Image Pretraining | http://arxiv.org/abs/2112.09106v1 | Contrastive language-image pretraining (CLIP) using image-text pairs has
achieved impressive results on image classification in both zero-shot and
transfer learning settings. However, we show that directly applying such models
to recognize image regions for object detection leads to poor performance due
to a domain shi... | true | true | Zhong, Yiwu and Yang, Jianwei and Zhang, Pengchuan and Li, Chunyuan and Codella, Noel and Li, Liunian Harold and Zhou, Luowei and Dai, Xiyang and Yuan, Lu and Li, Yin and Gao, Jianfeng | 2,022 | null | null | null | null | RegionCLIP: Region-based Language-Image Pretraining | RegionCLIP: Region-based Language-Image Pretraining - arXiv | https://arxiv.org/abs/2112.09106 | We propose a new method called RegionCLIP that significantly extends CLIP to learn region-level visual representations, thus enabling fine-grained alignment. |
Test-time Vocabulary Adaptation for Language-driven Object Detection | 2506.00333v1 | ma2024codet | \cite{ma2024codet} | CoDet: Co-Occurrence Guided Region-Word Alignment for Open-Vocabulary
Object Detection | http://arxiv.org/abs/2310.16667v1 | Deriving reliable region-word alignment from image-text pairs is critical to
learn object-level vision-language representations for open-vocabulary object
detection. Existing methods typically rely on pre-trained or self-trained
vision-language models for alignment, which are prone to limitations in
localization accura... | true | true | Ma, Chuofan and Jiang, Yi and Wen, Xin and Yuan, Zehuan and Qi, Xiaojuan | 2,023 | null | null | null | null | CoDet: Co-Occurrence Guided Region-Word Alignment for Open-Vocabulary
Object Detection | (NeurIPS2023) CoDet: Co-Occurrence Guided Region ... | https://github.com/CVMI-Lab/CoDet | Train an open-vocabulary detector with web-scale image-text pairs; Align regions and words by co-occurrence instead of region-text similarity |
Test-time Vocabulary Adaptation for Language-driven Object Detection | 2506.00333v1 | liu2024shine | \cite{liu2024shine} | SHiNe: Semantic Hierarchy Nexus for Open-vocabulary Object Detection | http://arxiv.org/abs/2405.10053v1 | Open-vocabulary object detection (OvOD) has transformed detection into a
language-guided task, empowering users to freely define their class
vocabularies of interest during inference. However, our initial investigation
indicates that existing OvOD detectors exhibit significant variability when
dealing with vocabularies... | true | true | Liu, Mingxuan and Hayes, Tyler L. and Ricci, Elisa and Csurka, Gabriela and Volpi, Riccardo | 2,024 | null | null | null | null | SHiNe: Semantic Hierarchy Nexus for Open-vocabulary Object Detection | [PDF] Semantic Hierarchy Nexus for Open-vocabulary Object Detection | https://openaccess.thecvf.com/content/CVPR2024/papers/Liu_SHiNe_Semantic_Hierarchy_Nexus_for_Open-vocabulary_Object_Detection_CVPR_2024_paper.pdf | SHiNe is training-free and can be seamlessly integrated with any off-the-shelf OvOD detector, without incurring additional computational overhead dur- ing |
Diversify and Conquer: Open-set Disagreement for Robust Semi-supervised
Learning with Outliers | 2505.24443v1 | ssl_2 | \cite{ssl_2} | Pseudo-label: The simple and efficient semi-supervised learning method for deep neural networks | null | null | true | false | Lee, Dong-Hyun | 2,013 | null | null | null | null | Pseudo-label: The simple and efficient semi-supervised learning method for deep neural networks | Pseudo-Label : The Simple and Efficient Semi-Supervised ... | https://www.researchgate.net/publication/280581078_Pseudo-Label_The_Simple_and_Efficient_Semi-Supervised_Learning_Method_for_Deep_Neural_Networks | We propose the simple and efficient method of semi-supervised learning for deep neural networks. Basically, the proposed network is trained in a supervised |
Diversify and Conquer: Open-set Disagreement for Robust Semi-supervised
Learning with Outliers | 2505.24443v1 | ssl_9 | \cite{ssl_9} | Semi-supervised Learning by Entropy Minimization | null | null | true | false | Yves Grandvalet and
Yoshua Bengio | 2,004 | null | null | null | null | Semi-supervised Learning by Entropy Minimization | Semi-supervised Learning by Entropy Minimization - NIPS | https://papers.nips.cc/paper/2740-semi-supervised-learning-by-entropy-minimization | We consider the semi-supervised learning problem, where a decision rule is to be learned from labeled and unlabeled data. In this framework, we motivate minimum entropy regularization, which enables to incorporate unlabeled data in the standard supervised learning. In the terminology used here, semi-supervised learning... |
Diversify and Conquer: Open-set Disagreement for Robust Semi-supervised
Learning with Outliers | 2505.24443v1 | ssl_10 | \cite{ssl_10} | Curriculum Labeling: Revisiting Pseudo-Labeling for Semi-Supervised
Learning | http://arxiv.org/abs/2001.06001v2 | In this paper we revisit the idea of pseudo-labeling in the context of
semi-supervised learning where a learning algorithm has access to a small set
of labeled samples and a large set of unlabeled samples. Pseudo-labeling works
by applying pseudo-labels to samples in the unlabeled set by using a model
trained on the co... | true | true | Paola Cascante{-}Bonilla and
Fuwen Tan and
Yanjun Qi and
Vicente Ordonez | 2,021 | null | null | null | null | Curriculum Labeling: Revisiting Pseudo-Labeling for Semi-Supervised
Learning | Revisiting Pseudo-Labeling for Semi-Supervised Learning | https://arxiv.org/abs/2001.06001 | by P Cascante-Bonilla · 2020 · Cited by 409 — In this paper we revisit the idea of pseudo-labeling in the context of semi-supervised learning where a learning algorithm has access to a small set of labeled |
Diversify and Conquer: Open-set Disagreement for Robust Semi-supervised
Learning with Outliers | 2505.24443v1 | ssl_11 | \cite{ssl_11} | Mean teachers are better role models: Weight-averaged consistency
targets improve semi-supervised deep learning results | http://arxiv.org/abs/1703.01780v6 | The recently proposed Temporal Ensembling has achieved state-of-the-art
results in several semi-supervised learning benchmarks. It maintains an
exponential moving average of label predictions on each training example, and
penalizes predictions that are inconsistent with this target. However, because
the targets change ... | true | true | Antti Tarvainen and
Harri Valpola | 2,017 | null | null | null | null | Mean teachers are better role models: Weight-averaged consistency
targets improve semi-supervised deep learning results | [PDF] Weight-averaged consistency targets improve semi-supervised ... | https://arxiv.org/pdf/1703.01780 | Combining Mean Teacher and Residual Networks, we improve the state of the art on CIFAR-10 with 4000 labels from 10.55% to 6.28%, and on. |
Diversify and Conquer: Open-set Disagreement for Robust Semi-supervised
Learning with Outliers | 2505.24443v1 | ssl_12 | \cite{ssl_12} | Regularization With Stochastic Transformations and Perturbations for
Deep Semi-Supervised Learning | http://arxiv.org/abs/1606.04586v1 | Effective convolutional neural networks are trained on large sets of labeled
data. However, creating large labeled datasets is a very costly and
time-consuming task. Semi-supervised learning uses unlabeled data to train a
model with higher accuracy when there is a limited set of labeled data
available. In this paper, w... | true | true | Mehdi Sajjadi and
Mehran Javanmardi and
Tolga Tasdizen | 2,016 | null | null | null | null | Regularization With Stochastic Transformations and Perturbations for
Deep Semi-Supervised Learning | Regularization With Stochastic Transformations and Perturbations ... | https://arxiv.org/abs/1606.04586 | Abstract page for arXiv paper 1606.04586: Regularization With Stochastic Transformations and Perturbations for Deep Semi-Supervised Learning. |
Diversify and Conquer: Open-set Disagreement for Robust Semi-supervised
Learning with Outliers | 2505.24443v1 | ssl_13 | \cite{ssl_13} | Temporal Ensembling for Semi-Supervised Learning | http://arxiv.org/abs/1610.02242v3 | In this paper, we present a simple and efficient method for training deep
neural networks in a semi-supervised setting where only a small portion of
training data is labeled. We introduce self-ensembling, where we form a
consensus prediction of the unknown labels using the outputs of the
network-in-training on differen... | true | true | Samuli Laine and
Timo Aila | 2,017 | null | null | null | null | Temporal Ensembling for Semi-Supervised Learning | Review — Π-Model, Temporal Ensembling ... - Sik-Ho Tsang | https://sh-tsang.medium.com/review-%CF%80-model-temporal-ensembling-temporal-ensembling-for-semi-supervised-learning-9cb6eea6865e | Temporal Ensembling for Semi-Supervised Learning. Stochastic Augmentation, Network Dropout, & Momentum Encoder are Used. |
Diversify and Conquer: Open-set Disagreement for Robust Semi-supervised
Learning with Outliers | 2505.24443v1 | ssl_14 | \cite{ssl_14} | Unsupervised Data Augmentation for Consistency Training | http://arxiv.org/abs/1904.12848v6 | Semi-supervised learning lately has shown much promise in improving deep
learning models when labeled data is scarce. Common among recent approaches is
the use of consistency training on a large amount of unlabeled data to
constrain model predictions to be invariant to input noise. In this work, we
present a new perspe... | true | true | Qizhe Xie and
Zihang Dai and
Eduard H. Hovy and
Thang Luong and
Quoc Le | 2,020 | null | null | null | null | Unsupervised Data Augmentation for Consistency Training | Unsupervised Data Augmentation for Consistency Training | http://arxiv.org/pdf/1904.12848v6 | Semi-supervised learning lately has shown much promise in improving deep
learning models when labeled data is scarce. Common among recent approaches is
the use of consistency training on a large amount of unlabeled data to
constrain model predictions to be invariant to input noise. In this work, we
present a new perspe... |
Diversify and Conquer: Open-set Disagreement for Robust Semi-supervised
Learning with Outliers | 2505.24443v1 | tnnls_2 | \cite{tnnls_2} | MutexMatch: Semi-Supervised Learning with Mutex-Based Consistency
Regularization | http://arxiv.org/abs/2203.14316v2 | The core issue in semi-supervised learning (SSL) lies in how to effectively
leverage unlabeled data, whereas most existing methods tend to put a great
emphasis on the utilization of high-confidence samples yet seldom fully explore
the usage of low-confidence samples. In this paper, we aim to utilize
low-confidence samp... | true | true | Yue Duan and
Zhen Zhao and
Lei Qi and
Lei Wang and
Luping Zhou and
Yinghuan Shi and
Yang Gao | 2,024 | null | null | null | {IEEE} Trans. on Neural Networks and Learning Systems | MutexMatch: Semi-Supervised Learning with Mutex-Based Consistency
Regularization | MutexMatch: Semi-Supervised Learning with Mutex-Based ... - arXiv | https://arxiv.org/abs/2203.14316 | In this paper, we aim to utilize low-confidence samples in a novel way with our proposed mutex-based consistency regularization, namely MutexMatch. |
Diversify and Conquer: Open-set Disagreement for Robust Semi-supervised
Learning with Outliers | 2505.24443v1 | ssl_3 | \cite{ssl_3} | MixMatch: A Holistic Approach to Semi-Supervised Learning | http://arxiv.org/abs/1905.02249v2 | Semi-supervised learning has proven to be a powerful paradigm for leveraging
unlabeled data to mitigate the reliance on large labeled datasets. In this
work, we unify the current dominant approaches for semi-supervised learning to
produce a new algorithm, MixMatch, that works by guessing low-entropy labels
for data-aug... | true | true | David Berthelot and
Nicholas Carlini and
Ian J. Goodfellow and
Nicolas Papernot and
Avital Oliver and
Colin Raffel | 2,019 | null | null | null | null | MixMatch: A Holistic Approach to Semi-Supervised Learning | MixMatch: a holistic approach to semi-supervised learning | https://dl.acm.org/doi/10.5555/3454287.3454741 | A new algorithm, MixMatch, that guesses low-entropy labels for data-augmented un-labeled examples and mixes labeled and unlabeled data using MixUp. |
Diversify and Conquer: Open-set Disagreement for Robust Semi-supervised
Learning with Outliers | 2505.24443v1 | ssl_4 | \cite{ssl_4} | FixMatch: Simplifying Semi-Supervised Learning with Consistency and
Confidence | http://arxiv.org/abs/2001.07685v2 | Semi-supervised learning (SSL) provides an effective means of leveraging
unlabeled data to improve a model's performance. In this paper, we demonstrate
the power of a simple combination of two common SSL methods: consistency
regularization and pseudo-labeling. Our algorithm, FixMatch, first generates
pseudo-labels usin... | true | true | Kihyuk Sohn and
David Berthelot and
Nicholas Carlini and
Zizhao Zhang and
Han Zhang and
Colin Raffel and
Ekin Dogus Cubuk and
Alexey Kurakin and
Chun{-}Liang Li | 2,020 | null | null | null | null | FixMatch: Simplifying Semi-Supervised Learning with Consistency and
Confidence | FixMatch: simplifying semi-supervised learning with consistency and ... | https://dl.acm.org/doi/abs/10.5555/3495724.3495775 | In this paper we propose FixMatch, an algorithm that is a significant simplification of existing SSL methods. |
Diversify and Conquer: Open-set Disagreement for Robust Semi-supervised
Learning with Outliers | 2505.24443v1 | ssl_16 | \cite{ssl_16} | ReMixMatch: Semi-Supervised Learning with Distribution Alignment and
Augmentation Anchoring | http://arxiv.org/abs/1911.09785v2 | We improve the recently-proposed "MixMatch" semi-supervised learning
algorithm by introducing two new techniques: distribution alignment and
augmentation anchoring. Distribution alignment encourages the marginal
distribution of predictions on unlabeled data to be close to the marginal
distribution of ground-truth label... | true | true | David Berthelot and Nicholas Carlini and Ekin D. Cubuk and Alex Kurakin and Kihyuk Sohn and Han Zhang and Colin Raffel | 2,020 | null | null | null | null | ReMixMatch: Semi-Supervised Learning with Distribution Alignment and
Augmentation Anchoring | ReMixMatch: Semi-Supervised Learning with Distribution Alignment ... | https://arxiv.org/abs/1911.09785 | We improve the recently-proposed "MixMatch" semi-supervised learning algorithm by introducing two new techniques: distribution alignment and augmentation |
Diversify and Conquer: Open-set Disagreement for Robust Semi-supervised
Learning with Outliers | 2505.24443v1 | ssl_19 | \cite{ssl_19} | FlexMatch: Boosting Semi-Supervised Learning with Curriculum Pseudo
Labeling | http://arxiv.org/abs/2110.08263v3 | The recently proposed FixMatch achieved state-of-the-art results on most
semi-supervised learning (SSL) benchmarks. However, like other modern SSL
algorithms, FixMatch uses a pre-defined constant threshold for all classes to
select unlabeled data that contribute to the training, thus failing to consider
different learn... | true | true | Zhang, Bowen and Wang, Yidong and Hou, Wenxin and Wu, Hao and Wang, Jindong and Okumura, Manabu and Shinozaki, Takahiro | 2,021 | null | null | null | null | FlexMatch: Boosting Semi-Supervised Learning with Curriculum Pseudo
Labeling | Boosting Semi-Supervised Learning with Curriculum Pseudo Labeling | https://arxiv.org/abs/2110.08263 | We propose Curriculum Pseudo Labeling (CPL), a curriculum learning approach to leverage unlabeled data according to the model's learning status. |
Diversify and Conquer: Open-set Disagreement for Robust Semi-supervised
Learning with Outliers | 2505.24443v1 | ssl_20 | \cite{ssl_20} | FreeMatch: Self-adaptive Thresholding for Semi-supervised Learning | http://arxiv.org/abs/2205.07246v3 | Semi-supervised Learning (SSL) has witnessed great success owing to the
impressive performances brought by various methods based on pseudo labeling and
consistency regularization. However, we argue that existing methods might fail
to utilize the unlabeled data more effectively since they either use a
pre-defined / fixe... | true | true | Yidong Wang and
Hao Chen and
Qiang Heng and
Wenxin Hou and
Yue Fan and
Zhen Wu and
Jindong Wang and
Marios Savvides and
Takahiro Shinozaki and
Bhiksha Raj and... | 2,023 | null | null | null | null | FreeMatch: Self-adaptive Thresholding for Semi-supervised Learning | FreeMatch: Self-adaptive Thresholding for Semi-supervised Learning | https://openreview.net/forum?id=PDrUPTXJI_A | We propose FreeMatch to define and adjust the confidence threshold in a self-adaptive manner for semi-supervised learning. |
Diversify and Conquer: Open-set Disagreement for Robust Semi-supervised
Learning with Outliers | 2505.24443v1 | ssl_8 | \cite{ssl_8} | SoftMatch: Addressing the Quantity-Quality Trade-off in Semi-supervised
Learning | http://arxiv.org/abs/2301.10921v2 | The critical challenge of Semi-Supervised Learning (SSL) is how to
effectively leverage the limited labeled data and massive unlabeled data to
improve the model's generalization performance. In this paper, we first revisit
the popular pseudo-labeling methods via a unified sample weighting formulation
and demonstrate th... | true | true | Hao Chen and Ran Tao and Yue Fan and Yidong Wang and Jindong Wang and Bernt Schiele and Xing Xie and Bhiksha Raj and Marios Savvides | 2,023 | null | null | null | null | SoftMatch: Addressing the Quantity-Quality Trade-off in Semi-supervised
Learning | Addressing the Quantity-Quality Tradeoff in Semi-supervised Learning | https://openreview.net/forum?id=ymt1zQXBDiF | This paper proposes SoftMatch to improve both the quantity and quality of pseudo-labels in semi-supervised learning. Basically, the authors |
Diversify and Conquer: Open-set Disagreement for Robust Semi-supervised
Learning with Outliers | 2505.24443v1 | ssl_6 | \cite{ssl_6} | SimMatch: Semi-supervised Learning with Similarity Matching | http://arxiv.org/abs/2203.06915v2 | Learning with few labeled data has been a longstanding problem in the
computer vision and machine learning research community. In this paper, we
introduced a new semi-supervised learning framework, SimMatch, which
simultaneously considers semantic similarity and instance similarity. In
SimMatch, the consistency regular... | true | true | Mingkai Zheng and
Shan You and
Lang Huang and
Fei Wang and
Chen Qian and
Chang Xu | 2,022 | null | null | null | null | SimMatch: Semi-supervised Learning with Similarity Matching | SimMatch: Semi-supervised Learning with Similarity ... | https://arxiv.org/abs/2203.06915 | by M Zheng · 2022 · Cited by 309 — In this paper, we introduced a new semi-supervised learning framework, SimMatch, which simultaneously considers semantic similarity and instance similarity. |
Diversify and Conquer: Open-set Disagreement for Robust Semi-supervised
Learning with Outliers | 2505.24443v1 | ssl_7 | \cite{ssl_7} | SimMatchV2: Semi-Supervised Learning with Graph Consistency | http://arxiv.org/abs/2308.06692v1 | Semi-Supervised image classification is one of the most fundamental problem
in computer vision, which significantly reduces the need for human labor. In
this paper, we introduce a new semi-supervised learning algorithm - SimMatchV2,
which formulates various consistency regularizations between labeled and
unlabeled data... | true | true | Mingkai Zheng and
Shan You and
Lang Huang and
Chen Luo and
Fei Wang and
Chen Qian and
Chang Xu | 2,023 | null | null | null | null | SimMatchV2: Semi-Supervised Learning with Graph Consistency | Semi-Supervised Learning with Graph Consistency | https://arxiv.org/abs/2308.06692 | by M Zheng · 2023 · Cited by 17 — In this paper, we introduce a new semi-supervised learning algorithm - SimMatchV2, which formulates various consistency regularizations between labeled and |
Diversify and Conquer: Open-set Disagreement for Robust Semi-supervised
Learning with Outliers | 2505.24443v1 | ssl_17 | \cite{ssl_17} | Label Propagation for Deep Semi-supervised Learning | http://arxiv.org/abs/1904.04717v1 | Semi-supervised learning is becoming increasingly important because it can
combine data carefully labeled by humans with abundant unlabeled data to train
deep neural networks. Classic methods on semi-supervised learning that have
focused on transductive learning have not been fully exploited in the inductive
framework ... | true | true | Ahmet Iscen and
Giorgos Tolias and
Yannis Avrithis and
Ondrej Chum | 2,019 | null | null | null | null | Label Propagation for Deep Semi-supervised Learning | [PDF] Label Propagation for Deep Semi-Supervised Learning | https://openaccess.thecvf.com/content_CVPR_2019/papers/Iscen_Label_Propagation_for_Deep_Semi-Supervised_Learning_CVPR_2019_paper.pdf | Label propagation uses a transductive method to generate pseudo-labels for unlabeled data, using a graph based on network embeddings, to train a deep neural |
Diversify and Conquer: Open-set Disagreement for Robust Semi-supervised
Learning with Outliers | 2505.24443v1 | tnnls_3 | \cite{tnnls_3} | Graph-Based Semi-Supervised Learning: {A} Comprehensive Review | null | null | true | false | Zixing Song and
Xiangli Yang and
Zenglin Xu and
Irwin King | 2,023 | null | null | null | {IEEE} Trans. on Neural Networks and Learning Systems | Graph-Based Semi-Supervised Learning: {A} Comprehensive Review | Graph-Based Semi-Supervised Learning | https://ieeexplore.ieee.org/document/9737635 | Graph-Based Semi-Supervised Learning: A Comprehensive Review | IEEE Journals & Magazine | IEEE Xplore Publisher: IEEE An essential class of SSL methods, referred to as graph-based semi-supervised learning (GSSL) methods in the literature, is to first represent each sample as a node in an affinity graph, and then, the l... |
Diversify and Conquer: Open-set Disagreement for Robust Semi-supervised
Learning with Outliers | 2505.24443v1 | ssl_5 | \cite{ssl_5} | CoMatch: Semi-supervised Learning with Contrastive Graph Regularization | http://arxiv.org/abs/2011.11183v2 | Semi-supervised learning has been an effective paradigm for leveraging
unlabeled data to reduce the reliance on labeled data. We propose CoMatch, a
new semi-supervised learning method that unifies dominant approaches and
addresses their limitations. CoMatch jointly learns two representations of the
training data, their... | true | true | Junnan Li and
Caiming Xiong and
Steven C. H. Hoi | 2,021 | null | null | null | null | CoMatch: Semi-supervised Learning with Contrastive Graph Regularization | CoMatch: Semi-Supervised Learning With Contrastive ... | https://openaccess.thecvf.com/content/ICCV2021/papers/Li_CoMatch_Semi-Supervised_Learning_With_Contrastive_Graph_Regularization_ICCV_2021_paper.pdf | by J Li · 2021 · Cited by 384 — We propose CoMatch, a new semi-supervised learning method that unifies dominant approaches and addresses their limitations. CoMatch jointly learns two |
Diversify and Conquer: Open-set Disagreement for Robust Semi-supervised
Learning with Outliers | 2505.24443v1 | rep_3 | \cite{rep_3} | Big Self-Supervised Models are Strong Semi-Supervised Learners | http://arxiv.org/abs/2006.10029v2 | One paradigm for learning from few labeled examples while making best use of
a large amount of unlabeled data is unsupervised pretraining followed by
supervised fine-tuning. Although this paradigm uses unlabeled data in a
task-agnostic way, in contrast to common approaches to semi-supervised learning
for computer visio... | true | true | Ting Chen and
Simon Kornblith and
Kevin Swersky and
Mohammad Norouzi and
Geoffrey E. Hinton | 2,020 | null | null | null | null | Big Self-Supervised Models are Strong Semi-Supervised Learners | [2006.10029] Big Self-Supervised Models are Strong Semi ... | https://arxiv.org/abs/2006.10029 | by T Chen · 2020 · Cited by 2883 — We show that it is surprisingly effective for semi-supervised learning on ImageNet. A key ingredient of our approach is the use of big (deep and wide) networks. |
Diversify and Conquer: Open-set Disagreement for Robust Semi-supervised
Learning with Outliers | 2505.24443v1 | ssl_1 | \cite{ssl_1} | Realistic Evaluation of Deep Semi-Supervised Learning Algorithms | http://arxiv.org/abs/1804.09170v4 | Semi-supervised learning (SSL) provides a powerful framework for leveraging
unlabeled data when labels are limited or expensive to obtain. SSL algorithms
based on deep neural networks have recently proven successful on standard
benchmark tasks. However, we argue that these benchmarks fail to address many
issues that th... | true | true | Avital Oliver and
Augustus Odena and
Colin Raffel and
Ekin Dogus Cubuk and
Ian J. Goodfellow | 2,018 | null | null | null | null | Realistic Evaluation of Deep Semi-Supervised Learning Algorithms | Realistic Evaluation of Deep Semi-Supervised Learning Algorithms | https://arxiv.org/abs/1804.09170 | Semi-supervised learning (SSL) provides a powerful framework for leveraging unlabeled data when labels are limited or expensive to obtain. |
Diversify and Conquer: Open-set Disagreement for Robust Semi-supervised
Learning with Outliers | 2505.24443v1 | ossl_2 | \cite{ossl_2} | Semi-Supervised Learning under Class Distribution Mismatch | null | null | true | false | Yanbei Chen and
Xiatian Zhu and
Wei Li and
Shaogang Gong | 2,020 | null | null | null | null | Semi-Supervised Learning under Class Distribution Mismatch | [PDF] Semi-Supervised Learning under Class Distribution Mismatch | https://ojs.aaai.org/index.php/AAAI/article/view/5763/5619 | Class distribution mismatch in semi-supervised learning occurs when labeled and unlabeled data come from different class distributions, unlike conventional SSL. |
Diversify and Conquer: Open-set Disagreement for Robust Semi-supervised
Learning with Outliers | 2505.24443v1 | ossl_14 | \cite{ossl_14} | SCOMatch: Alleviating Overtrusting in Open-set Semi-supervised Learning | http://arxiv.org/abs/2409.17512v1 | Open-set semi-supervised learning (OSSL) leverages practical open-set
unlabeled data, comprising both in-distribution (ID) samples from seen classes
and out-of-distribution (OOD) samples from unseen classes, for semi-supervised
learning (SSL). Prior OSSL methods initially learned the decision boundary
between ID and OO... | true | true | Wang, Zerun and Xiang, Liuyu and Huang, Lang and Mao, Jiafeng and Xiao, Ling and Yamasaki, Toshihiko | 2,025 | null | null | null | null | SCOMatch: Alleviating Overtrusting in Open-set Semi-supervised Learning | Alleviating Overtrusting in Open-set Semi-supervised Learning - arXiv | https://arxiv.org/abs/2409.17512 | We propose SCOMatch, a novel OSSL method that 1) selects reliable OOD samples as new labeled data with an OOD memory queue and a corresponding update strategy. |
Diversify and Conquer: Open-set Disagreement for Robust Semi-supervised
Learning with Outliers | 2505.24443v1 | ossl_12 | \cite{ossl_12} | Rethinking safe semi-supervised learning: Transferring the open-set problem to a close-set one | null | null | true | false | Ma, Qiankun and Gao, Jiyao and Zhan, Bo and Guo, Yunpeng and Zhou, Jiliu and Wang, Yan | 2,023 | null | null | null | null | Rethinking safe semi-supervised learning: Transferring the open-set problem to a close-set one | [PDF] Rethinking Safe Semi-supervised Learning - CVF Open Access | https://openaccess.thecvf.com/content/ICCV2023/supplemental/Ma_Rethinking_Safe_Semi-supervised_ICCV_2023_supplemental.pdf | Page 1. Rethinking Safe Semi-supervised Learning: Transferring the Open-set Problem to A Close-set One. -Supplementary Material-. 1. Detailed Datasets. In this |
Diversify and Conquer: Open-set Disagreement for Robust Semi-supervised
Learning with Outliers | 2505.24443v1 | ossl_16 | \cite{ossl_16} | Semi-Supervised Learning via Weight-aware Distillation under Class
Distribution Mismatch | http://arxiv.org/abs/2308.11874v1 | Semi-Supervised Learning (SSL) under class distribution mismatch aims to
tackle a challenging problem wherein unlabeled data contain lots of unknown
categories unseen in the labeled ones. In such mismatch scenarios, traditional
SSL suffers severe performance damage due to the harmful invasion of the
instances with unkn... | true | true | Du, Pan and Zhao, Suyun and Sheng, Zisen and Li, Cuiping and Chen, Hong | 2,023 | null | null | null | null | Semi-Supervised Learning via Weight-aware Distillation under Class
Distribution Mismatch | Semi-Supervised Learning via Weight-Aware Distillation ... | https://openaccess.thecvf.com/content/ICCV2023/papers/Du_Semi-Supervised_Learning_via_Weight-Aware_Distillation_under_Class_Distribution_Mismatch_ICCV_2023_paper.pdf | by P Du · 2023 · Cited by 11 — Semi-Supervised Learning (SSL) under class distribu- tion mismatch aims to tackle a challenging problem wherein unlabeled data contain lots of unknown |
Diversify and Conquer: Open-set Disagreement for Robust Semi-supervised
Learning with Outliers | 2505.24443v1 | ossl_5 | \cite{ossl_5} | Safe-Student for Safe Deep Semi-Supervised Learning with Unseen-Class
Unlabeled Data | null | null | true | false | Rundong He and
Zhongyi Han and
Xiankai Lu and
Yilong Yin | 2,022 | null | null | null | null | Safe-Student for Safe Deep Semi-Supervised Learning with Unseen-Class
Unlabeled Data | SAFER-STUDENT for Safe Deep Semi-Supervised Learning With... | https://openreview.net/forum?id=j8i42Lrh0Z | Missing: 04/08/2025 |
Diversify and Conquer: Open-set Disagreement for Robust Semi-supervised
Learning with Outliers | 2505.24443v1 | ossl_6 | \cite{ossl_6} | {SAFER-STUDENT} for Safe Deep Semi-Supervised Learning With Unseen-Class
Unlabeled Data | null | null | true | false | Rundong He and
Zhongyi Han and
Xiankai Lu and
Yilong Yin | 2,024 | null | null | null | {IEEE} Trans. on Knowledge and Data Engineering | {SAFER-STUDENT} for Safe Deep Semi-Supervised Learning With Unseen-Class
Unlabeled Data | SAFER-STUDENT for Safe Deep Semi-Supervised Learning With ... | https://www.researchgate.net/publication/371000311_SAFER-STUDENT_for_Safe_Deep_Semi-Supervised_Learning_With_Unseen-Class_Unlabeled_Data | Deep semi-supervised learning (SSL) methods aim to utilize abundant unlabeled data to improve the seen-class classification. Several similar definitions have emerged to describe this scenario, including safe SSL [9], open-set SSL [22,24,31,45], and the challenge of managing UnLabeled data from Unseen Classes in Semi-Su... |
Diversify and Conquer: Open-set Disagreement for Robust Semi-supervised
Learning with Outliers | 2505.24443v1 | ossl_3 | \cite{ossl_3} | Multi-Task Curriculum Framework for Open-Set Semi-Supervised Learning | http://arxiv.org/abs/2007.11330v1 | Semi-supervised learning (SSL) has been proposed to leverage unlabeled data
for training powerful models when only limited labeled data is available. While
existing SSL methods assume that samples in the labeled and unlabeled data
share the classes of their samples, we address a more complex novel scenario
named open-s... | true | true | Qing Yu and
Daiki Ikami and
Go Irie and
Kiyoharu Aizawa | 2,020 | null | null | null | null | Multi-Task Curriculum Framework for Open-Set Semi-Supervised Learning | YU1ut/Multi-Task-Curriculum-Framework-for-Open-Set-SSL | https://github.com/YU1ut/Multi-Task-Curriculum-Framework-for-Open-Set-SSL | This is the official PyTorch implementation of Multi-Task Curriculum Framework for Open-Set Semi-Supervised Learning. architecture. Requirements. Python 3.7 |
Diversify and Conquer: Open-set Disagreement for Robust Semi-supervised
Learning with Outliers | 2505.24443v1 | ossl_9 | \cite{ossl_9} | Trash to Treasure: Harvesting OOD Data with Cross-Modal Matching for
Open-Set Semi-Supervised Learning | http://arxiv.org/abs/2108.05617v1 | Open-set semi-supervised learning (open-set SSL) investigates a challenging
but practical scenario where out-of-distribution (OOD) samples are contained in
the unlabeled data. While the mainstream technique seeks to completely filter
out the OOD samples for semi-supervised learning (SSL), we propose a novel
training me... | true | true | Junkai Huang and
Chaowei Fang and
Weikai Chen and
Zhenhua Chai and
Xiaolin Wei and
Pengxu Wei and
Liang Lin and
Guanbin Li | 2,021 | null | null | null | null | Trash to Treasure: Harvesting OOD Data with Cross-Modal Matching for
Open-Set Semi-Supervised Learning | [PDF] Harvesting OOD Data With Cross-Modal Matching for Open-Set ... | https://guanbinli.com/papers/4-Huang_Trash_To_Treasure_Harvesting_OOD_Data_With_Cross-Modal_Matching_for_ICCV_2021_paper.pdf | Open-set semi-supervised learning (open-set SSL) inves- tigates a challenging but practical scenario where out-of- distribution (OOD) samples are contained |
Diversify and Conquer: Open-set Disagreement for Robust Semi-supervised
Learning with Outliers | 2505.24443v1 | ossl_10 | \cite{ossl_10} | Out-of-Distributed Semantic Pruning for Robust Semi-Supervised Learning | http://arxiv.org/abs/2305.18158v2 | Recent advances in robust semi-supervised learning (SSL) typically filter
out-of-distribution (OOD) information at the sample level. We argue that an
overlooked problem of robust SSL is its corrupted information on semantic
level, practically limiting the development of the field. In this paper, we
take an initial step... | true | true | Wang, Yu and Qiao, Pengchong and Liu, Chang and Song, Guoli and Zheng, Xiawu and Chen, Jie | 2,023 | null | null | null | null | Out-of-Distributed Semantic Pruning for Robust Semi-Supervised Learning | [PDF] Out-of-Distributed Semantic Pruning for Robust Semi-Supervised ... | https://openaccess.thecvf.com/content/CVPR2023/papers/Wang_Out-of-Distributed_Semantic_Pruning_for_Robust_Semi-Supervised_Learning_CVPR_2023_paper.pdf | Recent advances in robust semi-supervised learning. (SSL) typically filter out-of-distribution (OOD) information at the sample level. |
Diversify and Conquer: Open-set Disagreement for Robust Semi-supervised
Learning with Outliers | 2505.24443v1 | ossl_8 | \cite{ossl_8} | Unknown-Aware Graph Regularization for Robust Semi-supervised Learning
from Uncurated Data | null | null | true | false | Heejo Kong and
Suneung Kim and
Ho{-}Joong Kim and
Seong{-}Whan Lee | 2,024 | null | null | null | null | Unknown-Aware Graph Regularization for Robust Semi-supervised Learning
from Uncurated Data | Unknown-Aware Graph Regularization for Robust Semi- ... | https://www.researchgate.net/publication/379297624_Unknown-Aware_Graph_Regularization_for_Robust_Semi-supervised_Learning_from_Uncurated_Data | In this paper, we propose a robust SSL method for learning from uncurated real-world data within the context of open-set semi-supervised learning (OSSL). Unlike |
Diversify and Conquer: Open-set Disagreement for Robust Semi-supervised
Learning with Outliers | 2505.24443v1 | ossl_4 | \cite{ossl_4} | OpenMatch: Open-set Consistency Regularization for Semi-supervised
Learning with Outliers | http://arxiv.org/abs/2105.14148v2 | Semi-supervised learning (SSL) is an effective means to leverage unlabeled
data to improve a model's performance. Typical SSL methods like FixMatch assume
that labeled and unlabeled data share the same label space. However, in
practice, unlabeled data can contain categories unseen in the labeled set,
i.e., outliers, wh... | true | true | Saito, Kuniaki and Kim, Donghyun and Saenko, Kate | 2,021 | null | null | null | null | OpenMatch: Open-set Consistency Regularization for Semi-supervised
Learning with Outliers | VisionLearningGroup/OP_Match | https://github.com/VisionLearningGroup/OP_Match | OpenMatch: Open-set Consistency Regularization for Semi-supervised Learning with Outliers (NeurIPS 2021) ... This is an PyTorch implementation of OpenMatch. This |
Diversify and Conquer: Open-set Disagreement for Robust Semi-supervised
Learning with Outliers | 2505.24443v1 | ossl_7 | \cite{ossl_7} | IOMatch: Simplifying Open-Set Semi-Supervised Learning with Joint
Inliers and Outliers Utilization | http://arxiv.org/abs/2308.13168v1 | Semi-supervised learning (SSL) aims to leverage massive unlabeled data when
labels are expensive to obtain. Unfortunately, in many real-world applications,
the collected unlabeled data will inevitably contain unseen-class outliers not
belonging to any of the labeled classes. To deal with the challenging open-set
SSL ta... | true | true | Zekun Li and
Lei Qi and
Yinghuan Shi and
Yang Gao | 2,023 | null | null | null | null | IOMatch: Simplifying Open-Set Semi-Supervised Learning with Joint
Inliers and Outliers Utilization | [ICCV 2023 Oral] IOMatch: Simplifying Open-Set Semi-Supervised ... | https://github.com/nukezil/IOMatch | This is the official repository for our ICCV 2023 paper: IOMatch: Simplifying Open-Set Semi-Supervised Learning with Joint Inliers and Outliers Utilization. |
Diversify and Conquer: Open-set Disagreement for Robust Semi-supervised
Learning with Outliers | 2505.24443v1 | ossl_11 | \cite{ossl_11} | SSB: Simple but Strong Baseline for Boosting Performance of Open-Set
Semi-Supervised Learning | http://arxiv.org/abs/2311.10572v1 | Semi-supervised learning (SSL) methods effectively leverage unlabeled data to
improve model generalization. However, SSL models often underperform in
open-set scenarios, where unlabeled data contain outliers from novel categories
that do not appear in the labeled set. In this paper, we study the challenging
and realist... | true | true | Fan, Yue and Kukleva, Anna and Dai, Dengxin and Schiele, Bernt | 2,023 | null | null | null | null | SSB: Simple but Strong Baseline for Boosting Performance of Open-Set
Semi-Supervised Learning | SSB: Simple but Strong Baseline for Boosting Performance ... | https://ieeexplore.ieee.org/iel7/10376473/10376477/10377450.pdf | by Y Fan · 2023 · Cited by 17 — Semi-supervised learning. (SSL) aims to improve model performance by exploiting both labeled and unlabeled data. As one of the most widely used techniques, |
Diversify and Conquer: Open-set Disagreement for Robust Semi-supervised
Learning with Outliers | 2505.24443v1 | ossl_1 | \cite{ossl_1} | Safe Deep Semi-Supervised Learning for Unseen-Class Unlabeled Data | null | null | true | false | Lan{-}Zhe Guo and
Zhenyu Zhang and
Yuan Jiang and
Yufeng Li and
Zhi{-}Hua Zhou | 2,020 | null | null | null | null | Safe Deep Semi-Supervised Learning for Unseen-Class Unlabeled Data | [PDF] Safe Deep Semi-Supervised Learning for Unseen-Class Unlabeled ... | http://proceedings.mlr.press/v119/guo20i/guo20i.pdf | Deep semi-supervised learning (SSL) is proposed to uti- lize a large number of cheap unlabeled data to help deep neural networks improve performance, reducing |
Diversify and Conquer: Open-set Disagreement for Robust Semi-supervised
Learning with Outliers | 2505.24443v1 | ossl_13 | \cite{ossl_13} | Binary Decomposition: A Problem Transformation Perspective for Open-Set Semi-Supervised Learning | null | null | true | false | Hang, Jun-Yi and Zhang, Min-Ling | 2,024 | null | null | null | null | Binary Decomposition: A Problem Transformation Perspective for Open-Set Semi-Supervised Learning | Binary decomposition | Proceedings of the 41st International ... | https://dl.acm.org/doi/10.5555/3692070.3692767 | Binary decomposition: a problem transformation perspective for open-set semi-supervised learning. Computing methodologies · Machine learning. |
Diversify and Conquer: Open-set Disagreement for Robust Semi-supervised
Learning with Outliers | 2505.24443v1 | ossl_17 | \cite{ossl_17} | They are Not Completely Useless: Towards Recycling Transferable
Unlabeled Data for Class-Mismatched Semi-Supervised Learning | http://arxiv.org/abs/2011.13529v4 | Semi-Supervised Learning (SSL) with mismatched classes deals with the problem
that the classes-of-interests in the limited labeled data is only a subset of
the classes in massive unlabeled data. As a result, the classes only possessed
by the unlabeled data may mislead the classifier training and thus hindering
the real... | true | true | Huang, Zhuo and Yang, Jian and Gong, Chen | 2,022 | null | null | null | {IEEE} Trans. on Multimedia | They are Not Completely Useless: Towards Recycling Transferable
Unlabeled Data for Class-Mismatched Semi-Supervised Learning | Towards Recycling Transferable Unlabeled Data for Class ... - arXiv | https://arxiv.org/abs/2011.13529 | They are Not Completely Useless: Towards Recycling Transferable Unlabeled Data for Class-Mismatched Semi-Supervised Learning. Authors:Zhuo Huang |
KairosAD: A SAM-Based Model for Industrial Anomaly Detection on Embedded
Devices | 2505.24334v1 | liu2024deep | \cite{liu2024deep} | Deep Industrial Image Anomaly Detection: A Survey | http://arxiv.org/abs/2301.11514v5 | The recent rapid development of deep learning has laid a milestone in
industrial Image Anomaly Detection (IAD). In this paper, we provide a
comprehensive review of deep learning-based image anomaly detection techniques,
from the perspectives of neural network architectures, levels of supervision,
loss functions, metric... | true | true | Liu, Jiaqi and Xie, Guoyang and Wang, Jinbao and Li, Shangnian and Wang, Chengjie and Zheng, Feng and Jin, Yaochu | 2,024 | null | null | 10.1109/cvpr52688.2022.01392 | Machine Intelligence Research | Deep Industrial Image Anomaly Detection: A Survey | Deep Industrial Image Anomaly Detection: A Survey | http://arxiv.org/pdf/2301.11514v5 | The recent rapid development of deep learning has laid a milestone in
industrial Image Anomaly Detection (IAD). In this paper, we provide a
comprehensive review of deep learning-based image anomaly detection techniques,
from the perspectives of neural network architectures, levels of supervision,
loss functions, metric... |
KairosAD: A SAM-Based Model for Industrial Anomaly Detection on Embedded
Devices | 2505.24334v1 | bergmann2019mvtec | \cite{bergmann2019mvtec} | {MVTec AD — A Comprehensive Real-World Dataset for Unsupervised Anomaly Detection} | null | null | true | false | Bergmann, Paul and Fauser, Michael and Sattlegger, David and Steger, Carsten | 2,019 | null | null | 10.1007/978-3-031-20056-4_23 | null | {MVTec AD — A Comprehensive Real-World Dataset for Unsupervised Anomaly Detection} | The MVTec Anomaly Detection Dataset - ACM Digital Library | https://dl.acm.org/doi/abs/10.1007/s11263-020-01400-4 | (2019a). MVTec AD: A comprehensive real-world dataset for unsupervised anomaly detection. In Proceedings of the IEEE conference on computer vision and pattern |
KairosAD: A SAM-Based Model for Industrial Anomaly Detection on Embedded
Devices | 2505.24334v1 | bergmann2018improving | \cite{bergmann2018improving} | Improving Unsupervised Defect Segmentation by Applying Structural
Similarity to Autoencoders | http://arxiv.org/abs/1807.02011v3 | Convolutional autoencoders have emerged as popular methods for unsupervised
defect segmentation on image data. Most commonly, this task is performed by
thresholding a pixel-wise reconstruction error based on an $\ell^p$ distance.
This procedure, however, leads to large residuals whenever the reconstruction
encompasses ... | true | true | Bergmann, Paul and Löwe, Sindy and Fauser, Michael and Sattlegger, David and Steger, Carsten | 2,019 | null | null | null | null | Improving Unsupervised Defect Segmentation by Applying Structural
Similarity to Autoencoders | (PDF) Improving Unsupervised Defect Segmentation by Applying ... | https://www.researchgate.net/publication/331779705_Improving_Unsupervised_Defect_Segmentation_by_Applying_Structural_Similarity_to_Autoencoders | Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders ; Paul Bergmann at Technical University of Munich. Paul Bergmann. |
KairosAD: A SAM-Based Model for Industrial Anomaly Detection on Embedded
Devices | 2505.24334v1 | liu2020towards | \cite{liu2020towards} | Towards Visually Explaining Variational Autoencoders | http://arxiv.org/abs/1911.07389v7 | Recent advances in Convolutional Neural Network (CNN) model interpretability
have led to impressive progress in visualizing and understanding model
predictions. In particular, gradient-based visual attention methods have driven
much recent effort in using visual attention maps as a means for visual
explanations. A key ... | true | true | Liu, Wenqian and Li, Runze and Zheng, Meng and Karanam, Srikrishna and Wu, Ziyan and Bhanu, Bir and Radke, Richard J. and Camps, Octavia | 2,020 | null | null | 10.1007/978-3-030-20893-6_39 | null | Towards Visually Explaining Variational Autoencoders | Towards Visually Explaining Variational Autoencoders | http://arxiv.org/pdf/1911.07389v7 | Recent advances in Convolutional Neural Network (CNN) model interpretability
have led to impressive progress in visualizing and understanding model
predictions. In particular, gradient-based visual attention methods have driven
much recent effort in using visual attention maps as a means for visual
explanations. A key ... |
KairosAD: A SAM-Based Model for Industrial Anomaly Detection on Embedded
Devices | 2505.24334v1 | akcay2019ganomaly | \cite{akcay2019ganomaly} | GANomaly: Semi-Supervised Anomaly Detection via Adversarial Training | http://arxiv.org/abs/1805.06725v3 | Anomaly detection is a classical problem in computer vision, namely the
determination of the normal from the abnormal when datasets are highly biased
towards one class (normal) due to the insufficient sample size of the other
class (abnormal). While this can be addressed as a supervised learning problem,
a significantl... | true | true | Akcay, Samet and Atapour-Abarghouei, Amir and Breckon, Toby P. | 2,019 | null | null | null | null | GANomaly: Semi-Supervised Anomaly Detection via Adversarial Training | GANomaly Paper Review: Semi-Supervised Anomaly Detection via ... | https://towardsdatascience.com/ganomaly-paper-review-semi-supervised-anomaly-detection-via-adversarial-training-a6f7a64a265f/ | GANomaly is an anomaly detection model that employs adversarial training to capture the data distribution. |
KairosAD: A SAM-Based Model for Industrial Anomaly Detection on Embedded
Devices | 2505.24334v1 | damm2024anomalydino | \cite{damm2024anomalydino} | AnomalyDINO: Boosting Patch-based Few-shot Anomaly Detection with DINOv2 | http://arxiv.org/abs/2405.14529v3 | Recent advances in multimodal foundation models have set new standards in
few-shot anomaly detection. This paper explores whether high-quality visual
features alone are sufficient to rival existing state-of-the-art
vision-language models. We affirm this by adapting DINOv2 for one-shot and
few-shot anomaly detection, wi... | true | true | Damm, Simon and Laszkiewicz, Mike and Lederer, Johannes and Fischer, Asja | 2,024 | null | null | 10.1561/0600000110 | null | AnomalyDINO: Boosting Patch-based Few-shot Anomaly Detection with DINOv2 | [PDF] Boosting Patch-Based Few-Shot Anomaly Detection with DINOv2 | https://openaccess.thecvf.com/content/WACV2025/papers/Damm_AnomalyDINO_Boosting_Patch-Based_Few-Shot_Anomaly_Detection_with_DINOv2_WACV_2025_paper.pdf | Our approach, termed AnomalyDINO, follows the well- established AD framework of patch-level deep nearest neighbor [34, 46], and leverages DINOv2 [30] as a back-. |
KairosAD: A SAM-Based Model for Industrial Anomaly Detection on Embedded
Devices | 2505.24334v1 | roth2022towards | \cite{roth2022towards} | Towards Total Recall in Industrial Anomaly Detection | http://arxiv.org/abs/2106.08265v2 | Being able to spot defective parts is a critical component in large-scale
industrial manufacturing. A particular challenge that we address in this work
is the cold-start problem: fit a model using nominal (non-defective) example
images only. While handcrafted solutions per class are possible, the goal is to
build syste... | true | true | Roth, Karsten and Pemula, Latha and Zepeda, Joaquin and Scholkopf, Bernhard and Brox, Thomas and Gehler, Peter | 2,022 | null | null | 10.1109/cvpr52688.2022.00951 | null | Towards Total Recall in Industrial Anomaly Detection | Towards Total Recall in Industrial Anomaly Detection | http://arxiv.org/pdf/2106.08265v2 | Being able to spot defective parts is a critical component in large-scale
industrial manufacturing. A particular challenge that we address in this work
is the cold-start problem: fit a model using nominal (non-defective) example
images only. While handcrafted solutions per class are possible, the goal is to
build syste... |
KairosAD: A SAM-Based Model for Industrial Anomaly Detection on Embedded
Devices | 2505.24334v1 | jiang2022softpatch | \cite{jiang2022softpatch} | SoftPatch: Unsupervised Anomaly Detection with Noisy Data | http://arxiv.org/abs/2403.14233v1 | Although mainstream unsupervised anomaly detection (AD) algorithms perform
well in academic datasets, their performance is limited in practical
application due to the ideal experimental setting of clean training data.
Training with noisy data is an inevitable problem in real-world anomaly
detection but is seldom discus... | true | true | Jiang, Xi and Liu, Jianlin and Wang, Jinbao and Nie, Qiang and Wu, Kai and Liu, Yong and Wang, Chengjie and Zheng, Feng | 2,022 | null | null | null | null | SoftPatch: Unsupervised Anomaly Detection with Noisy Data | SoftPatch: Unsupervised Anomaly Detection with Noisy Data | http://arxiv.org/pdf/2403.14233v1 | Although mainstream unsupervised anomaly detection (AD) algorithms perform
well in academic datasets, their performance is limited in practical
application due to the ideal experimental setting of clean training data.
Training with noisy data is an inevitable problem in real-world anomaly
detection but is seldom discus... |
KairosAD: A SAM-Based Model for Industrial Anomaly Detection on Embedded
Devices | 2505.24334v1 | li2024sam | \cite{li2024sam} | A SAM-guided Two-stream Lightweight Model for Anomaly Detection | http://arxiv.org/abs/2402.19145v2 | In industrial anomaly detection, model efficiency and mobile-friendliness
become the primary concerns in real-world applications. Simultaneously, the
impressive generalization capabilities of Segment Anything (SAM) have garnered
broad academic attention, making it an ideal choice for localizing unseen
anomalies and div... | true | true | Li, Chenghao and Qi, Lei and Geng, Xin | 2,025 | null | null | 10.1109/cvpr.2019.00982 | ACM Transactions on Multimedia Computing, Communications, and Applications | A SAM-guided Two-stream Lightweight Model for Anomaly Detection | A SAM-guided Two-stream Lightweight Model for Anomaly Detection | https://arxiv.org/html/2402.19145v1 | In this paper, we propose a novel framework called SAM-guided Two-stream Lightweight Model for unsupervised anomaly detection tasks. |
KairosAD: A SAM-Based Model for Industrial Anomaly Detection on Embedded
Devices | 2505.24334v1 | li2024multimodal | \cite{li2024multimodal} | Multimodal Foundation Models: From Specialists to General-Purpose
Assistants | http://arxiv.org/abs/2309.10020v1 | This paper presents a comprehensive survey of the taxonomy and evolution of
multimodal foundation models that demonstrate vision and vision-language
capabilities, focusing on the transition from specialist models to
general-purpose assistants. The research landscape encompasses five core
topics, categorized into two cl... | true | true | Li, Chunyuan and Gan, Zhe and Yang, Zhengyuan and Yang, Jianwei and Li, Linjie and Wang, Lijuan and Gao, Jianfeng | 2,024 | null | null | null | Foundations and Trends in Computer Graphics and Vision | Multimodal Foundation Models: From Specialists to General-Purpose
Assistants | Multimodal Foundation Models: From Specialists to ... | https://www.nowpublishers.com/article/Details/CGV-110 | by C Li · 2024 · Cited by 316 — This monograph presents a comprehensive survey of the taxonomy and evolution of multimodal foundation models that demonstrate vision and vision-language |
KairosAD: A SAM-Based Model for Industrial Anomaly Detection on Embedded
Devices | 2505.24334v1 | radford2021learning | \cite{radford2021learning} | Learning Transferable Visual Models From Natural Language Supervision | http://arxiv.org/abs/2103.00020v1 | State-of-the-art computer vision systems are trained to predict a fixed set
of predetermined object categories. This restricted form of supervision limits
their generality and usability since additional labeled data is needed to
specify any other visual concept. Learning directly from raw text about images
is a promisi... | true | true | Radford, Alec and Kim, Jong Wook and Hallacy, Chris and Ramesh, Aditya and Goh, Gabriel and Agarwal, Sandhini and Sastry, Girish and Askell, Amanda and Mishkin, Pamela and Clark, Jack and Krueger, Gretchen and Sutskever, Ilya | 2,021 | null | null | null | null | Learning Transferable Visual Models From Natural Language Supervision | Learning Transferable Visual Models From Natural Language Supervision | http://arxiv.org/pdf/2103.00020v1 | State-of-the-art computer vision systems are trained to predict a fixed set
of predetermined object categories. This restricted form of supervision limits
their generality and usability since additional labeled data is needed to
specify any other visual concept. Learning directly from raw text about images
is a promisi... |
KairosAD: A SAM-Based Model for Industrial Anomaly Detection on Embedded
Devices | 2505.24334v1 | kirillov2023segment | \cite{kirillov2023segment} | Segment Anything | http://arxiv.org/abs/2304.02643v1 | We introduce the Segment Anything (SA) project: a new task, model, and
dataset for image segmentation. Using our efficient model in a data collection
loop, we built the largest segmentation dataset to date (by far), with over 1
billion masks on 11M licensed and privacy respecting images. The model is
designed and train... | true | true | Kirillov, Alexander and Mintun, Eric and Ravi, Nikhila and Mao, Hanzi and Rolland, Chloe and Gustafson, Laura and Xiao, Tete and Whitehead, Spencer and Berg, Alexander C. and Lo, Wan-Yen and Dollar, Piotr and Girshick, Ross | 2,023 | null | null | 10.1109/tip.2023.3293772 | null | Segment Anything | Segment Anything | http://arxiv.org/pdf/2304.02643v1 | We introduce the Segment Anything (SA) project: a new task, model, and
dataset for image segmentation. Using our efficient model in a data collection
loop, we built the largest segmentation dataset to date (by far), with over 1
billion masks on 11M licensed and privacy respecting images. The model is
designed and train... |
KairosAD: A SAM-Based Model for Industrial Anomaly Detection on Embedded
Devices | 2505.24334v1 | caron2021emerging | \cite{caron2021emerging} | Emerging Properties in Self-Supervised Vision Transformers | http://arxiv.org/abs/2104.14294v2 | In this paper, we question if self-supervised learning provides new
properties to Vision Transformer (ViT) that stand out compared to convolutional
networks (convnets). Beyond the fact that adapting self-supervised methods to
this architecture works particularly well, we make the following observations:
first, self-sup... | true | true | Caron, Mathilde and Touvron, Hugo and Misra, Ishan and J\'egou, Herv\'e and Mairal, Julien and Bojanowski, Piotr and Joulin, Armand | 2,021 | null | null | null | null | Emerging Properties in Self-Supervised Vision Transformers | [PDF] Emerging Properties in Self-Supervised Vision Transformers | https://openaccess.thecvf.com/content/ICCV2021/papers/Caron_Emerging_Properties_in_Self-Supervised_Vision_Transformers_ICCV_2021_paper.pdf | Self-supervised ViT features contain semantic segmentation, scene layout, object boundaries, and perform well with k-NN classifiers, unlike supervised ViTs or |
KairosAD: A SAM-Based Model for Industrial Anomaly Detection on Embedded
Devices | 2505.24334v1 | oquab2023dinov2 | \cite{oquab2023dinov2} | DINOv2: Learning Robust Visual Features without Supervision | http://arxiv.org/abs/2304.07193v2 | The recent breakthroughs in natural language processing for model pretraining
on large quantities of data have opened the way for similar foundation models
in computer vision. These models could greatly simplify the use of images in
any system by producing all-purpose visual features, i.e., features that work
across im... | true | true | Maxime Oquab and Timoth{\'e}e Darcet and Th{\'e}o Moutakanni and Huy V. Vo and Marc Szafraniec and Vasil Khalidov and Pierre Fernandez and Daniel HAZIZA and Francisco Massa and Alaaeldin El-Nouby and Mido Assran and Nicolas Ballas and Wojciech Galuba and Russell Howes and Po-Yao Huang and Shang-Wen Li and Ishan Misra a... | 2,024 | null | null | null | Transactions on Machine Learning Research | DINOv2: Learning Robust Visual Features without Supervision | DINOv2: Learning Robust Visual Features without Supervision | http://arxiv.org/pdf/2304.07193v2 | The recent breakthroughs in natural language processing for model pretraining
on large quantities of data have opened the way for similar foundation models
in computer vision. These models could greatly simplify the use of images in
any system by producing all-purpose visual features, i.e., features that work
across im... |
KairosAD: A SAM-Based Model for Industrial Anomaly Detection on Embedded
Devices | 2505.24334v1 | zhang2023faster | \cite{zhang2023faster} | Faster Segment Anything: Towards Lightweight SAM for Mobile Applications | http://arxiv.org/abs/2306.14289v2 | Segment Anything Model (SAM) has attracted significant attention due to its
impressive zero-shot transfer performance and high versatility for numerous
vision applications (like image editing with fine-grained control). Many of
such applications need to be run on resource-constraint edge devices, like
mobile phones. In... | true | true | Zhang, Chaoning and Han, Dongshen and Qiao, Yu and Kim, Jung Uk and Bae, Sung-Ho and Lee, Seungkyu and Hong, Choong Seon | 2,023 | null | null | 10.1109/iccv48922.2021.00822 | arXiv preprint arXiv:2306.14289 | Faster Segment Anything: Towards Lightweight SAM for Mobile Applications | Faster Segment Anything: Towards Lightweight SAM for Mobile Applications | http://arxiv.org/pdf/2306.14289v2 | Segment Anything Model (SAM) has attracted significant attention due to its
impressive zero-shot transfer performance and high versatility for numerous
vision applications (like image editing with fine-grained control). Many of
such applications need to be run on resource-constraint edge devices, like
mobile phones. In... |
KairosAD: A SAM-Based Model for Industrial Anomaly Detection on Embedded
Devices | 2505.24334v1 | capogrosso2024machine | \cite{capogrosso2024machine} | A Machine Learning-oriented Survey on Tiny Machine Learning | http://arxiv.org/abs/2309.11932v2 | The emergence of Tiny Machine Learning (TinyML) has positively revolutionized
the field of Artificial Intelligence by promoting the joint design of
resource-constrained IoT hardware devices and their learning-based software
architectures. TinyML carries an essential role within the fourth and fifth
industrial revolutio... | true | true | Capogrosso, Luigi and Cunico, Federico and Cheng, Dong Seon and Fummi, Franco and Cristani, Marco | 2,024 | null | null | 10.1109/access.2022.3182659 | IEEE Access | A Machine Learning-oriented Survey on Tiny Machine Learning | (PDF) A Machine Learning-Oriented Survey on Tiny Machine Learning | https://www.researchgate.net/publication/378163073_A_Machine_Learning-oriented_Survey_on_Tiny_Machine_Learning | This comprehensive survey wishes to provide an up-to-date overview focused on all the learning algorithms within TinyML-based solutions. |
KairosAD: A SAM-Based Model for Industrial Anomaly Detection on Embedded
Devices | 2505.24334v1 | vadera2022methods | \cite{vadera2022methods} | Methods for Pruning Deep Neural Networks | http://arxiv.org/abs/2011.00241v2 | This paper presents a survey of methods for pruning deep neural networks. It
begins by categorising over 150 studies based on the underlying approach used
and then focuses on three categories: methods that use magnitude based pruning,
methods that utilise clustering to identify redundancy, and methods that use
sensitiv... | true | true | Vadera, Sunil and Ameen, Salem | 2,022 | null | null | 10.1201/9781003162810-13 | IEEE Access | Methods for Pruning Deep Neural Networks | Methods for Pruning Deep Neural Networks | http://arxiv.org/pdf/2011.00241v2 | This paper presents a survey of methods for pruning deep neural networks. It
begins by categorising over 150 studies based on the underlying approach used
and then focuses on three categories: methods that use magnitude based pruning,
methods that utilise clustering to identify redundancy, and methods that use
sensitiv... |
KairosAD: A SAM-Based Model for Industrial Anomaly Detection on Embedded
Devices | 2505.24334v1 | gholami2022survey | \cite{gholami2022survey} | A Survey of Quantization Methods for Efficient Neural Network Inference | http://arxiv.org/abs/2103.13630v3 | As soon as abstract mathematical computations were adapted to computation on
digital computers, the problem of efficient representation, manipulation, and
communication of the numerical values in those computations arose. Strongly
related to the problem of numerical representation is the problem of
quantization: in wha... | true | true | Gholami, Amir and Kim, Sehoon and Dong, Zhen and Yao, Zhewei and Mahoney, Michael W. and Keutzer, Kurt | 2,022 | null | null | 10.1007/s11263-021-01453-z | null | A Survey of Quantization Methods for Efficient Neural Network Inference | A Survey of Quantization Methods for Efficient Neural Network Inference | http://arxiv.org/pdf/2103.13630v3 | As soon as abstract mathematical computations were adapted to computation on
digital computers, the problem of efficient representation, manipulation, and
communication of the numerical values in those computations arose. Strongly
related to the problem of numerical representation is the problem of
quantization: in wha... |
KairosAD: A SAM-Based Model for Industrial Anomaly Detection on Embedded
Devices | 2505.24334v1 | gou2021knowledge | \cite{gou2021knowledge} | Knowledge Distillation: A Survey | http://arxiv.org/abs/2006.05525v7 | In recent years, deep neural networks have been successful in both industry
and academia, especially for computer vision tasks. The great success of deep
learning is mainly due to its scalability to encode large-scale data and to
maneuver billions of model parameters. However, it is a challenge to deploy
these cumberso... | true | true | Gou, Jianping and Yu, Baosheng and Maybank, Stephen J. and Tao, Dacheng | 2,021 | null | null | null | International Journal of Computer Vision | Knowledge Distillation: A Survey | Knowledge Distillation: A Survey | http://arxiv.org/pdf/2006.05525v7 | In recent years, deep neural networks have been successful in both industry
and academia, especially for computer vision tasks. The great success of deep
learning is mainly due to its scalability to encode large-scale data and to
maneuver billions of model parameters. However, it is a challenge to deploy
these cumberso... |
KairosAD: A SAM-Based Model for Industrial Anomaly Detection on Embedded
Devices | 2505.24334v1 | ren2021comprehensive | \cite{ren2021comprehensive} | A Comprehensive Survey of Neural Architecture Search: Challenges and
Solutions | http://arxiv.org/abs/2006.02903v3 | Deep learning has made breakthroughs and substantial in many fields due to
its powerful automatic representation capabilities. It has been proven that
neural architecture design is crucial to the feature representation of data and
the final performance. However, the design of the neural architecture heavily
relies on t... | true | true | Ren, Pengzhen and Xiao, Yun and Chang, Xiaojun and Huang, Po-yao and Li, Zhihui and Chen, Xiaojiang and Wang, Xin | 2,021 | null | null | 10.1109/tkde.2021.3126456 | ACM Computing Surveys | A Comprehensive Survey of Neural Architecture Search: Challenges and
Solutions | A quick look at NAS (Neural Architecture Search) - Welcome | https://gachiemchiep.github.io/machine%20learning/NAS-survey-2020/ | On this page. 2020 NAS surveyr A Comprehensive Survey of Neural Architecture Search: Challenges and Solutions. The current research results |
KairosAD: A SAM-Based Model for Industrial Anomaly Detection on Embedded
Devices | 2505.24334v1 | brauwers2021general | \cite{brauwers2021general} | A General Survey on Attention Mechanisms in Deep Learning | http://arxiv.org/abs/2203.14263v1 | Attention is an important mechanism that can be employed for a variety of
deep learning models across many different domains and tasks. This survey
provides an overview of the most important attention mechanisms proposed in the
literature. The various attention mechanisms are explained by means of a
framework consistin... | true | true | Brauwers, Gianni and Frasincar, Flavius | 2,023 | null | null | null | IEEE Transactions on Knowledge and Data Engineering | A General Survey on Attention Mechanisms in Deep Learning | A General Survey on Attention Mechanisms in Deep Learning | http://arxiv.org/pdf/2203.14263v1 | Attention is an important mechanism that can be employed for a variety of
deep learning models across many different domains and tasks. This survey
provides an overview of the most important attention mechanisms proposed in the
literature. The various attention mechanisms are explained by means of a
framework consistin... |
KairosAD: A SAM-Based Model for Industrial Anomaly Detection on Embedded
Devices | 2505.24334v1 | vaswani2017attention | \cite{vaswani2017attention} | Attention Is All You Need | http://arxiv.org/abs/1706.03762v7 | The dominant sequence transduction models are based on complex recurrent or
convolutional neural networks in an encoder-decoder configuration. The best
performing models also connect the encoder and decoder through an attention
mechanism. We propose a new simple network architecture, the Transformer, based
solely on at... | true | true | Vaswani, Ashish and Shazeer, Noam and Parmar, Niki and Uszkoreit, Jakob and Jones, Llion and Gomez, Aidan N and Kaiser, {\L}ukasz and Polosukhin, Illia | 2,017 | null | null | 10.1145/3505244 | null | Attention Is All You Need | Attention Is All You Need | http://arxiv.org/pdf/1706.03762v7 | The dominant sequence transduction models are based on complex recurrent or
convolutional neural networks in an encoder-decoder configuration. The best
performing models also connect the encoder and decoder through an attention
mechanism. We propose a new simple network architecture, the Transformer, based
solely on at... |
KairosAD: A SAM-Based Model for Industrial Anomaly Detection on Embedded
Devices | 2505.24334v1 | khan2022transformers | \cite{khan2022transformers} | Transformers in Vision: A Survey | http://arxiv.org/abs/2101.01169v5 | Astounding results from Transformer models on natural language tasks have
intrigued the vision community to study their application to computer vision
problems. Among their salient benefits, Transformers enable modeling long
dependencies between input sequence elements and support parallel processing of
sequence as com... | true | true | Khan, Salman and Naseer, Muzammal and Hayat, Munawar and Zamir, Syed Waqas and Khan, Fahad Shahbaz and Shah, Mubarak | 2,022 | null | null | 10.1007/978-3-031-73209-6_1 | ACM Computing Surveys | Transformers in Vision: A Survey | Transformers in Vision: A Survey | http://arxiv.org/pdf/2101.01169v5 | Astounding results from Transformer models on natural language tasks have
intrigued the vision community to study their application to computer vision
problems. Among their salient benefits, Transformers enable modeling long
dependencies between input sequence elements and support parallel processing of
sequence as com... |
Wav2Sem: Plug-and-Play Audio Semantic Decoupling for 3D Speech-Driven
Facial Animation | 2505.23290v1 | TaylorKYMKRHM17 | \cite{TaylorKYMKRHM17} | A deep learning approach for generalized speech animation | null | null | true | false | Sarah L. Taylor and
Taehwan Kim and
Yisong Yue and
Moshe Mahler and
James Krahe and
Anastasio Garcia Rodriguez and
Jessica K. Hodgins and
Iain A. Matthews | 2,017 | null | null | null | TOG | A deep learning approach for generalized speech animation | [PDF] A Deep Learning Approach for Generalized Speech Animation - TTIC | https://home.ttic.edu/~taehwan/taylor_etal_siggraph2017.pdf | We introduce a simple and efective deep learning approach to automatically generate natural looking speech animation that synchronizes to input speech. |
Wav2Sem: Plug-and-Play Audio Semantic Decoupling for 3D Speech-Driven
Facial Animation | 2505.23290v1 | cao2005expressive | \cite{cao2005expressive} | Expressive Speech-driven Facial Animation with controllable emotions | http://arxiv.org/abs/2301.02008v2 | It is in high demand to generate facial animation with high realism, but it
remains a challenging task. Existing approaches of speech-driven facial
animation can produce satisfactory mouth movement and lip synchronization, but
show weakness in dramatic emotional expressions and flexibility in emotion
control. This pape... | true | true | Cao, Yong and Tien, Wen C and Faloutsos, Petros and Pighin, Fr{\'e}d{\'e}ric | 2,005 | null | null | null | ACM TOG | Expressive Speech-driven Facial Animation with controllable emotions | Expressive Speech-driven Facial Animation with ... | https://github.com/on1262/facialanimation | EXPRESSIVE SPEECH-DRIVEN FACIAL ANIMATION WITH CONTROLLABLE EMOTIONS. Source code for: Expressive Speech-driven Facial Animation with controllable emotions. |
Wav2Sem: Plug-and-Play Audio Semantic Decoupling for 3D Speech-Driven
Facial Animation | 2505.23290v1 | FaceFormer | \cite{FaceFormer} | FaceFormer: Speech-Driven 3D Facial Animation with Transformers | http://arxiv.org/abs/2112.05329v4 | Speech-driven 3D facial animation is challenging due to the complex geometry
of human faces and the limited availability of 3D audio-visual data. Prior
works typically focus on learning phoneme-level features of short audio windows
with limited context, occasionally resulting in inaccurate lip movements. To
tackle this... | true | true | Yingruo Fan and
Zhaojiang Lin and
Jun Saito and
Wenping Wang and
Taku Komura | 2,022 | null | null | null | null | FaceFormer: Speech-Driven 3D Facial Animation with Transformers | [PDF] FaceFormer: Speech-Driven 3D Facial Animation With Transformers | https://openaccess.thecvf.com/content/CVPR2022/papers/Fan_FaceFormer_Speech-Driven_3D_Facial_Animation_With_Transformers_CVPR_2022_paper.pdf | An autoregressive transformer-based architecture for speech-driven 3D facial animation. FaceFormer encodes the long-term audio context and the history of face |
Wav2Sem: Plug-and-Play Audio Semantic Decoupling for 3D Speech-Driven
Facial Animation | 2505.23290v1 | CodeTalker | \cite{CodeTalker} | CodeTalker: Speech-Driven 3D Facial Animation with Discrete Motion Prior | http://arxiv.org/abs/2301.02379v2 | Speech-driven 3D facial animation has been widely studied, yet there is still
a gap to achieving realism and vividness due to the highly ill-posed nature and
scarcity of audio-visual data. Existing works typically formulate the
cross-modal mapping into a regression task, which suffers from the
regression-to-mean proble... | true | true | Jinbo Xing and
Menghan Xia and
Yuechen Zhang and
Xiaodong Cun and
Jue Wang and
Tien{-}Tsin Wong | 2,023 | null | null | null | null | CodeTalker: Speech-Driven 3D Facial Animation with Discrete Motion Prior | Speech-Driven 3D Facial Animation with Discrete Motion Prior - arXiv | https://arxiv.org/abs/2301.02379 | In this paper, we propose to cast speech-driven facial animation as a code query task in a finite proxy space of the learned codebook. |
Wav2Sem: Plug-and-Play Audio Semantic Decoupling for 3D Speech-Driven
Facial Animation | 2505.23290v1 | FaceDiffuser | \cite{FaceDiffuser} | FaceDiffuser: Speech-Driven 3D Facial Animation Synthesis Using
Diffusion | http://arxiv.org/abs/2309.11306v1 | Speech-driven 3D facial animation synthesis has been a challenging task both
in industry and research. Recent methods mostly focus on deterministic deep
learning methods meaning that given a speech input, the output is always the
same. However, in reality, the non-verbal facial cues that reside throughout
the face are ... | true | true | Stefan Stan and
Kazi Injamamul Haque and
Zerrin Yumak | 2,023 | null | null | null | null | FaceDiffuser: Speech-Driven 3D Facial Animation Synthesis Using
Diffusion | Speech-Driven 3D Facial Animation Synthesis Using Diffusion | https://dl.acm.org/doi/10.1145/3623264.3624447 | We present FaceDiffuser, a non-deterministic deep learning model to generate speech-driven facial animations that is trained with both 3D vertex and blendshape |
Wav2Sem: Plug-and-Play Audio Semantic Decoupling for 3D Speech-Driven
Facial Animation | 2505.23290v1 | li2023mask | \cite{li2023mask} | Mask-fpan: Semi-supervised face parsing in the wild with de-occlusion and uv gan | null | null | true | false | Li, Lei and Zhang, Tianfang and Kang, Zhongfeng and Jiang, Xikun | 2,023 | null | null | null | Computers \& Graphics | Mask-fpan: Semi-supervised face parsing in the wild with de-occlusion and uv gan | Mask-FPAN: Semi-Supervised Face Parsing in the Wild ... | https://arxiv.org/abs/2212.09098 | by L Li · 2022 · Cited by 22 — We propose a novel framework termed Mask-FPAN. It uses a de-occlusion module that learns to parse occluded faces in a semi-supervised way. |
Wav2Sem: Plug-and-Play Audio Semantic Decoupling for 3D Speech-Driven
Facial Animation | 2505.23290v1 | haque2023facexhubert | \cite{haque2023facexhubert} | FaceXHuBERT: Text-less Speech-driven E(X)pressive 3D Facial Animation
Synthesis Using Self-Supervised Speech Representation Learning | http://arxiv.org/abs/2303.05416v1 | This paper presents FaceXHuBERT, a text-less speech-driven 3D facial
animation generation method that allows to capture personalized and subtle cues
in speech (e.g. identity, emotion and hesitation). It is also very robust to
background noise and can handle audio recorded in a variety of situations (e.g.
multiple peopl... | true | true | Haque, Kazi Injamamul and Yumak, Zerrin | 2,023 | null | null | null | null | FaceXHuBERT: Text-less Speech-driven E(X)pressive 3D Facial Animation
Synthesis Using Self-Supervised Speech Representation Learning | Text-less Speech-driven E(X)pressive 3D Facial Animation ... | https://www.researchgate.net/publication/372492333_FaceXHuBERT_Text-less_Speech-driven_EXpressive_3D_Facial_Animation_Synthesis_Using_Self-Supervised_Speech_Representation_Learning | This paper presents FaceXHuBERT, a text-less speech-driven 3D facial animation generation method that allows us to capture facial cues related to emotional |
Wav2Sem: Plug-and-Play Audio Semantic Decoupling for 3D Speech-Driven
Facial Animation | 2505.23290v1 | EMOTE | \cite{EMOTE} | Emotional Speech-Driven Animation with Content-Emotion Disentanglement | http://arxiv.org/abs/2306.08990v2 | To be widely adopted, 3D facial avatars must be animated easily,
realistically, and directly from speech signals. While the best recent methods
generate 3D animations that are synchronized with the input audio, they largely
ignore the impact of emotions on facial expressions. Realistic facial animation
requires lip-syn... | true | true | Dan{\v{e}}{\v{c}}ek, Radek and Chhatre, Kiran and Tripathi, Shashank and Wen, Yandong and Black, Michael and Bolkart, Timo | 2,023 | null | null | null | null | Emotional Speech-Driven Animation with Content-Emotion Disentanglement | Emotional Speech-Driven Animation with Content-Emotion ... | https://dl.acm.org/doi/10.1145/3610548.3618183 | We propose EMOTE (Expressive Model Optimized for Talking with Emotion), which generates 3D talking-head avatars that maintain lip-sync from speech. |
Wav2Sem: Plug-and-Play Audio Semantic Decoupling for 3D Speech-Driven
Facial Animation | 2505.23290v1 | peng2023emotalk | \cite{peng2023emotalk} | EmoTalk: Speech-Driven Emotional Disentanglement for 3D Face Animation | http://arxiv.org/abs/2303.11089v2 | Speech-driven 3D face animation aims to generate realistic facial expressions
that match the speech content and emotion. However, existing methods often
neglect emotional facial expressions or fail to disentangle them from speech
content. To address this issue, this paper proposes an end-to-end neural
network to disent... | true | true | Peng, Ziqiao and Wu, Haoyu and Song, Zhenbo and Xu, Hao and Zhu, Xiangyu and He, Jun and Liu, Hongyan and Fan, Zhaoxin | 2,023 | null | null | null | null | EmoTalk: Speech-Driven Emotional Disentanglement for 3D Face Animation | Speech-Driven Emotional Disentanglement for 3D Face Animation | https://arxiv.org/abs/2303.11089 | This paper proposes an end-to-end neural network to disentangle different emotions in speech so as to generate rich 3D facial expressions. |
Wav2Sem: Plug-and-Play Audio Semantic Decoupling for 3D Speech-Driven
Facial Animation | 2505.23290v1 | thambiraja20233diface | \cite{thambiraja20233diface} | 3DiFACE: Diffusion-based Speech-driven 3D Facial Animation and Editing | http://arxiv.org/abs/2312.00870v1 | We present 3DiFACE, a novel method for personalized speech-driven 3D facial
animation and editing. While existing methods deterministically predict facial
animations from speech, they overlook the inherent one-to-many relationship
between speech and facial expressions, i.e., there are multiple reasonable
facial express... | true | true | Balamurugan Thambiraja and
Sadegh Aliakbarian and
Darren Cosker and
Justus Thies | 2,023 | null | null | null | CoRR | 3DiFACE: Diffusion-based Speech-driven 3D Facial Animation and Editing | [2312.00870] 3DiFACE: Diffusion-based Speech-driven 3D ... | https://arxiv.org/abs/2312.00870 | by B Thambiraja · 2023 · Cited by 18 — Abstract:We present 3DiFACE, a novel method for personalized speech-driven 3D facial animation and editing. |
Wav2Sem: Plug-and-Play Audio Semantic Decoupling for 3D Speech-Driven
Facial Animation | 2505.23290v1 | VOCA | \cite{VOCA} | Capture, Learning, and Synthesis of 3D Speaking Styles | http://arxiv.org/abs/1905.03079v1 | Audio-driven 3D facial animation has been widely explored, but achieving
realistic, human-like performance is still unsolved. This is due to the lack of
available 3D datasets, models, and standard evaluation metrics. To address
this, we introduce a unique 4D face dataset with about 29 minutes of 4D scans
captured at 60... | true | true | Daniel Cudeiro and
Timo Bolkart and
Cassidy Laidlaw and
Anurag Ranjan and
Michael J. Black | 2,019 | null | null | null | null | Capture, Learning, and Synthesis of 3D Speaking Styles | Capture, Learning, and Synthesis of 3D Speaking Styles | http://arxiv.org/pdf/1905.03079v1 | Audio-driven 3D facial animation has been widely explored, but achieving
realistic, human-like performance is still unsolved. This is due to the lack of
available 3D datasets, models, and standard evaluation metrics. To address
this, we introduce a unique 4D face dataset with about 29 minutes of 4D scans
captured at 60... |
Wav2Sem: Plug-and-Play Audio Semantic Decoupling for 3D Speech-Driven
Facial Animation | 2505.23290v1 | LG-LDM | \cite{LG-LDM} | Expressive 3D Facial Animation Generation Based on Local-to-global Latent Diffusion | null | null | true | false | Song, Wenfeng and Wang, Xuan and Jiang, Yiming and Li, Shuai and Hao, Aimin and Hou, Xia and Qin, Hong | 2,024 | null | null | null | TVCG | Expressive 3D Facial Animation Generation Based on Local-to-global Latent Diffusion | wangxuanx/Face-Diffusion-Model: The official pytorch code ... | https://github.com/wangxuanx/Face-Diffusion-Model | Expressive 3D Facial Animation Generation Based on Local-to-global Latent Diffusion ... Our method generates realistic facial animations by syncing lips with |
Wav2Sem: Plug-and-Play Audio Semantic Decoupling for 3D Speech-Driven
Facial Animation | 2505.23290v1 | fu2024mimic | \cite{fu2024mimic} | Mimic: Speaking Style Disentanglement for Speech-Driven 3D Facial
Animation | http://arxiv.org/abs/2312.10877v1 | Speech-driven 3D facial animation aims to synthesize vivid facial animations
that accurately synchronize with speech and match the unique speaking style.
However, existing works primarily focus on achieving precise lip
synchronization while neglecting to model the subject-specific speaking style,
often resulting in unr... | true | true | Hui Fu and
Zeqing Wang and
Ke Gong and
Keze Wang and
Tianshui Chen and
Haojie Li and
Haifeng Zeng and
Wenxiong Kang | 2,024 | null | null | null | null | Mimic: Speaking Style Disentanglement for Speech-Driven 3D Facial
Animation | [PDF] Speaking Style Disentanglement for Speech-Driven 3D Facial ... | https://ojs.aaai.org/index.php/AAAI/article/view/27945/27910 | We propose Mimic for style-content disentanglement and synthesizing facial animations matching an identity-specific speaking style, as illustrated in Figure 2. |
Wav2Sem: Plug-and-Play Audio Semantic Decoupling for 3D Speech-Driven
Facial Animation | 2505.23290v1 | wav2lip | \cite{wav2lip} | A Lip Sync Expert Is All You Need for Speech to Lip Generation In The
Wild | http://arxiv.org/abs/2008.10010v1 | In this work, we investigate the problem of lip-syncing a talking face video
of an arbitrary identity to match a target speech segment. Current works excel
at producing accurate lip movements on a static image or videos of specific
people seen during the training phase. However, they fail to accurately morph
the lip mo... | true | true | K. R. Prajwal and
Rudrabha Mukhopadhyay and
Vinay P. Namboodiri and
C. V. Jawahar | 2,020 | null | null | null | null | A Lip Sync Expert Is All You Need for Speech to Lip Generation In The
Wild | [2008.10010] A Lip Sync Expert Is All You Need for Speech ... | https://arxiv.org/abs/2008.10010 | **arXiv:2008.10010** (cs) View a PDF of the paper titled A Lip Sync Expert Is All You Need for Speech to Lip Generation In The Wild, by K R Prajwal and 3 other authors (or arXiv:2008.10010v1 [cs.CV] for this version) View a PDF of the paper titled A Lip Sync Expert Is All You Need for Speech to Lip Generation In The W... |
Wav2Sem: Plug-and-Play Audio Semantic Decoupling for 3D Speech-Driven
Facial Animation | 2505.23290v1 | DBLP:conf/bmvc/ChenLLYW21 | \cite{DBLP:conf/bmvc/ChenLLYW21} | Talking Head Generation with Audio and Speech Related Facial Action
Units | null | null | true | false | Sen Chen and
Zhilei Liu and
Jiaxing Liu and
Zhengxiang Yan and
Longbiao Wang | 2,021 | null | null | null | null | Talking Head Generation with Audio and Speech Related Facial Action
Units | Talking Head Generation with Audio and Speech Related Facial ... | https://arxiv.org/abs/2110.09951 | In this paper, we propose a novel recurrent generative network that uses both audio and speech-related facial action units (AUs) as the driving information. |
Wav2Sem: Plug-and-Play Audio Semantic Decoupling for 3D Speech-Driven
Facial Animation | 2505.23290v1 | DeepSpeech | \cite{DeepSpeech} | Deep Speech: Scaling up end-to-end speech recognition | http://arxiv.org/abs/1412.5567v2 | We present a state-of-the-art speech recognition system developed using
end-to-end deep learning. Our architecture is significantly simpler than
traditional speech systems, which rely on laboriously engineered processing
pipelines; these traditional systems also tend to perform poorly when used in
noisy environments. I... | true | true | Awni Y. Hannun and
Carl Case and
Jared Casper and
Bryan Catanzaro and
Greg Diamos and
Erich Elsen and
Ryan Prenger and
Sanjeev Satheesh and
Shubho Sengupta and
... | 2,014 | null | null | null | CoRR | Deep Speech: Scaling up end-to-end speech recognition | [PDF] Deep Speech: Scaling up end-to-end speech recognition - arXiv | https://arxiv.org/pdf/1412.5567 | Deep Speech is an end-to-end speech recognition system using deep learning, a simpler architecture, and a large RNN trained with multiple GPUs. |
Wav2Sem: Plug-and-Play Audio Semantic Decoupling for 3D Speech-Driven
Facial Animation | 2505.23290v1 | wav2vec | \cite{wav2vec} | wav2vec 2.0: A Framework for Self-Supervised Learning of Speech
Representations | http://arxiv.org/abs/2006.11477v3 | We show for the first time that learning powerful representations from speech
audio alone followed by fine-tuning on transcribed speech can outperform the
best semi-supervised methods while being conceptually simpler. wav2vec 2.0
masks the speech input in the latent space and solves a contrastive task
defined over a qu... | true | true | Alexei Baevski and
Yuhao Zhou and
Abdelrahman Mohamed and
Michael Auli | 2,020 | null | null | null | null | wav2vec 2.0: A Framework for Self-Supervised Learning of Speech
Representations | wav2vec 2.0: A Framework for Self-Supervised Learning of Speech ... | https://arxiv.org/abs/2006.11477 | wav2vec 2.0 masks the speech input in the latent space and solves a contrastive task defined over a quantization of the latent representations |
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