parent_paper_title stringclasses 63
values | parent_paper_arxiv_id stringclasses 63
values | citation_shorthand stringlengths 2 56 | raw_citation_text stringlengths 9 63 | cited_paper_title stringlengths 5 161 | cited_paper_arxiv_link stringlengths 32 37 ⌀ | cited_paper_abstract stringlengths 406 1.92k ⌀ | has_metadata bool 1
class | is_arxiv_paper bool 2
classes | bib_paper_authors stringlengths 2 2.44k ⌀ | bib_paper_year float64 1.97k 2.03k ⌀ | bib_paper_month stringclasses 16
values | bib_paper_url stringlengths 20 116 ⌀ | bib_paper_doi stringclasses 269
values | bib_paper_journal stringlengths 3 148 ⌀ | original_title stringlengths 5 161 | search_res_title stringlengths 4 122 | search_res_url stringlengths 22 267 | search_res_content stringlengths 19 1.92k |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Wav2Sem: Plug-and-Play Audio Semantic Decoupling for 3D Speech-Driven
Facial Animation | 2505.23290v1 | hubert | \cite{hubert} | HuBERT: Self-Supervised Speech Representation Learning by Masked
Prediction of Hidden Units | http://arxiv.org/abs/2106.07447v1 | Self-supervised approaches for speech representation learning are challenged
by three unique problems: (1) there are multiple sound units in each input
utterance, (2) there is no lexicon of input sound units during the pre-training
phase, and (3) sound units have variable lengths with no explicit segmentation.
To deal ... | true | true | Wei{-}Ning Hsu and
Benjamin Bolte and
Yao{-}Hung Hubert Tsai and
Kushal Lakhotia and
Ruslan Salakhutdinov and
Abdelrahman Mohamed | 2,021 | null | null | null | ACM TASLP | HuBERT: Self-Supervised Speech Representation Learning by Masked
Prediction of Hidden Units | HuBERT: Self-Supervised Speech Representation Learning ... - arXiv | https://arxiv.org/abs/2106.07447 | We propose the Hidden-Unit BERT (HuBERT) approach for self-supervised speech representation learning, which utilizes an offline clustering step to provide |
Wav2Sem: Plug-and-Play Audio Semantic Decoupling for 3D Speech-Driven
Facial Animation | 2505.23290v1 | ao2023gesturediffuclip | \cite{ao2023gesturediffuclip} | GestureDiffuCLIP: Gesture Diffusion Model with CLIP Latents | http://arxiv.org/abs/2303.14613v4 | The automatic generation of stylized co-speech gestures has recently received
increasing attention. Previous systems typically allow style control via
predefined text labels or example motion clips, which are often not flexible
enough to convey user intent accurately. In this work, we present
GestureDiffuCLIP, a neural... | true | true | Ao, Tenglong and Zhang, Zeyi and Liu, Libin | 2,023 | null | null | null | ACM TOG | GestureDiffuCLIP: Gesture Diffusion Model with CLIP Latents | GestureDiffuCLIP: Gesture Diffusion Model with CLIP Latents | http://arxiv.org/pdf/2303.14613v4 | The automatic generation of stylized co-speech gestures has recently received
increasing attention. Previous systems typically allow style control via
predefined text labels or example motion clips, which are often not flexible
enough to convey user intent accurately. In this work, we present
GestureDiffuCLIP, a neural... |
Wav2Sem: Plug-and-Play Audio Semantic Decoupling for 3D Speech-Driven
Facial Animation | 2505.23290v1 | liang2024omg | \cite{liang2024omg} | OMG: Towards Open-vocabulary Motion Generation via Mixture of
Controllers | http://arxiv.org/abs/2312.08985v3 | We have recently seen tremendous progress in realistic text-to-motion
generation. Yet, the existing methods often fail or produce implausible motions
with unseen text inputs, which limits the applications. In this paper, we
present OMG, a novel framework, which enables compelling motion generation from
zero-shot open-v... | true | true | Liang, Han and Bao, Jiacheng and Zhang, Ruichi and Ren, Sihan and Xu, Yuecheng and Yang, Sibei and Chen, Xin and Yu, Jingyi and Xu, Lan | 2,024 | null | null | null | null | OMG: Towards Open-vocabulary Motion Generation via Mixture of
Controllers | [PDF] OMG: Towards Open-vocabulary Motion Generation via Mixture of ... | https://openaccess.thecvf.com/content/CVPR2024/papers/Liang_OMG_Towards_Open-vocabulary_Motion_Generation_via_Mixture_of_Controllers_CVPR_2024_paper.pdf | We propose a fine-tuning scheme for text conditioning, utilizing a mixture of controllers to effectively improve the alignment between text and motion. 2. |
Wav2Sem: Plug-and-Play Audio Semantic Decoupling for 3D Speech-Driven
Facial Animation | 2505.23290v1 | zhang2022motiondiffuse | \cite{zhang2022motiondiffuse} | MotionDiffuse: Text-Driven Human Motion Generation with Diffusion Model | http://arxiv.org/abs/2208.15001v1 | Human motion modeling is important for many modern graphics applications,
which typically require professional skills. In order to remove the skill
barriers for laymen, recent motion generation methods can directly generate
human motions conditioned on natural languages. However, it remains challenging
to achieve diver... | true | true | Mingyuan Zhang and
Zhongang Cai and
Liang Pan and
Fangzhou Hong and
Xinying Guo and
Lei Yang and
Ziwei Liu | 2,024 | null | null | null | TPAMI | MotionDiffuse: Text-Driven Human Motion Generation with Diffusion Model | Text-Driven Human Motion Generation With Diffusion Model | https://dl.acm.org/doi/abs/10.1109/TPAMI.2024.3355414 | MotionDiffuse responds to fine-grained instructions on body parts, and arbitrary-length motion synthesis with time-varied text prompts. |
Wav2Sem: Plug-and-Play Audio Semantic Decoupling for 3D Speech-Driven
Facial Animation | 2505.23290v1 | mughal2024convofusion | \cite{mughal2024convofusion} | ConvoFusion: Multi-Modal Conversational Diffusion for Co-Speech Gesture
Synthesis | http://arxiv.org/abs/2403.17936v1 | Gestures play a key role in human communication. Recent methods for co-speech
gesture generation, while managing to generate beat-aligned motions, struggle
generating gestures that are semantically aligned with the utterance. Compared
to beat gestures that align naturally to the audio signal, semantically
coherent gest... | true | true | Mughal, Muhammad Hamza and Dabral, Rishabh and Habibie, Ikhsanul and Donatelli, Lucia and Habermann, Marc and Theobalt, Christian | 2,024 | null | null | null | null | ConvoFusion: Multi-Modal Conversational Diffusion for Co-Speech Gesture
Synthesis | Multi-Modal Conversational Diffusion for Co-Speech Gesture ... - arXiv | https://arxiv.org/abs/2403.17936 | We present ConvoFusion, a diffusion-based approach for multi-modal gesture synthesis, which can not only generate gestures based on multi-modal speech inputs. |
Wav2Sem: Plug-and-Play Audio Semantic Decoupling for 3D Speech-Driven
Facial Animation | 2505.23290v1 | zhao2024media2face | \cite{zhao2024media2face} | Media2Face: Co-speech Facial Animation Generation With Multi-Modality
Guidance | http://arxiv.org/abs/2401.15687v2 | The synthesis of 3D facial animations from speech has garnered considerable
attention. Due to the scarcity of high-quality 4D facial data and
well-annotated abundant multi-modality labels, previous methods often suffer
from limited realism and a lack of lexible conditioning. We address this
challenge through a trilogy.... | true | true | Qingcheng Zhao and
Pengyu Long and
Qixuan Zhang and
Dafei Qin and
Han Liang and
Longwen Zhang and
Yingliang Zhang and
Jingyi Yu and
Lan Xu | 2,024 | null | null | null | null | Media2Face: Co-speech Facial Animation Generation With Multi-Modality
Guidance | Co-speech Facial Animation Generation With Multi-Modality Guidance | https://arxiv.org/abs/2401.15687 | We propose Media2Face, a diffusion model in GNPFA latent space for co-speech facial animation generation, accepting rich multi-modality guidances from audio, |
Wav2Sem: Plug-and-Play Audio Semantic Decoupling for 3D Speech-Driven
Facial Animation | 2505.23290v1 | DBLP:conf/cvpr/ChhatreDABPBB24 | \cite{DBLP:conf/cvpr/ChhatreDABPBB24} | Emotional Speech-driven 3D Body Animation via Disentangled Latent
Diffusion | http://arxiv.org/abs/2312.04466v2 | Existing methods for synthesizing 3D human gestures from speech have shown
promising results, but they do not explicitly model the impact of emotions on
the generated gestures. Instead, these methods directly output animations from
speech without control over the expressed emotion. To address this limitation,
we presen... | true | true | Kiran Chhatre and
Radek Danecek and
Nikos Athanasiou and
Giorgio Becherini and
Christopher E. Peters and
Michael J. Black and
Timo Bolkart | 2,024 | null | null | null | null | Emotional Speech-driven 3D Body Animation via Disentangled Latent
Diffusion | [2312.04466] Emotional Speech-driven 3D Body Animation via ... | https://arxiv.org/abs/2312.04466 | To account for this, AMUSE maps the driving audio to three disentangled latent vectors: one for content, one for emotion, and one for personal |
Wav2Sem: Plug-and-Play Audio Semantic Decoupling for 3D Speech-Driven
Facial Animation | 2505.23290v1 | ElizaldeZR19 | \cite{ElizaldeZR19} | Cross Modal Audio Search and Retrieval with Joint Embeddings Based
on Text and Audio | null | null | true | false | Benjamin Elizalde and
Shuayb Zarar and
Bhiksha Raj | 2,019 | null | null | null | null | Cross Modal Audio Search and Retrieval with Joint Embeddings Based
on Text and Audio | Cross Modal Audio Search and Retrieval with Joint Embeddings ... | https://www.microsoft.com/en-us/research/publication/cross-modal-audio-search-and-retrieval-with-joint-embeddings-based-on-text-and-audio/ | Missing: 04/08/2025 |
Wav2Sem: Plug-and-Play Audio Semantic Decoupling for 3D Speech-Driven
Facial Animation | 2505.23290v1 | Yu0L19 | \cite{Yu0L19} | Mining Audio, Text and Visual Information for Talking Face Generation | null | null | true | false | Lingyun Yu and
Jun Yu and
Qiang Ling | 2,019 | null | null | null | null | Mining Audio, Text and Visual Information for Talking Face Generation | Mining Audio, Text and Visual Information for Talking Face Generation | https://ieeexplore.ieee.org/document/8970886 | First, a multimodal learning method is proposed to generate accurate mouth landmarks with multimedia inputs (both text and audio). Second, a network named |
Wav2Sem: Plug-and-Play Audio Semantic Decoupling for 3D Speech-Driven
Facial Animation | 2505.23290v1 | EMAGE | \cite{EMAGE} | EMAGE: Towards Unified Holistic Co-Speech Gesture Generation via
Expressive Masked Audio Gesture Modeling | http://arxiv.org/abs/2401.00374v5 | We propose EMAGE, a framework to generate full-body human gestures from audio
and masked gestures, encompassing facial, local body, hands, and global
movements. To achieve this, we first introduce BEAT2 (BEAT-SMPLX-FLAME), a new
mesh-level holistic co-speech dataset. BEAT2 combines a MoShed SMPL-X body with
FLAME head ... | true | true | Haiyang Liu and
Zihao Zhu and
Giorgio Becherini and
Yichen Peng and
Mingyang Su and
You Zhou and
Xuefei Zhe and
Naoya Iwamoto and
Bo Zheng and
Michael J. Blac... | 2,024 | null | null | null | null | EMAGE: Towards Unified Holistic Co-Speech Gesture Generation via
Expressive Masked Audio Gesture Modeling | EMAGE - CVPR 2024 Open Access Repository | https://openaccess.thecvf.com/content/CVPR2024/html/Liu_EMAGE_Towards_Unified_Holistic_Co-Speech_Gesture_Generation_via_Expressive_Masked_CVPR_2024_paper.html | EMAGE: Towards Unified Holistic Co-Speech Gesture Generation via Expressive Masked Audio Gesture Modeling. Haiyang Liu, Zihao Zhu, Giorgio Becherini, Yichen |
Proximal Algorithm Unrolling: Flexible and Efficient Reconstruction
Networks for Single-Pixel Imaging | 2505.23180v1 | RN5318 | \cite{RN5318} | Snapshot Compressive Imaging: Principle, Implementation, Theory,
Algorithms and Applications | http://arxiv.org/abs/2103.04421v1 | Capturing high-dimensional (HD) data is a long-term challenge in signal
processing and related fields. Snapshot compressive imaging (SCI) uses a
two-dimensional (2D) detector to capture HD ($\ge3$D) data in a {\em snapshot}
measurement. Via novel optical designs, the 2D detector samples the HD data in
a {\em compressiv... | true | true | Yuan, Xin and Brady, David J. and Katsaggelos, Aggelos K. | 2,021 | null | null | 10.1109/msp.2020.3023869 | IEEE Signal Processing Magazine | Snapshot Compressive Imaging: Principle, Implementation, Theory,
Algorithms and Applications | Snapshot Compressive Imaging: Theory, Algorithms, and ... | https://www.researchgate.net/publication/349697698_Snapshot_Compressive_Imaging_Theory_Algorithms_and_Applications | Snapshot compressive imaging (SCI) uses a 2D detector to capture HD (>3D) data in a snapshot measurement. Via novel optical designs, the 2D detector samples the |
Proximal Algorithm Unrolling: Flexible and Efficient Reconstruction
Networks for Single-Pixel Imaging | 2505.23180v1 | wang2023full | \cite{wang2023full} | Full-resolution and full-dynamic-range coded aperture compressive temporal imaging | null | null | true | false | Wang, Ping and Wang, Lishun and Qiao, Mu and Yuan, Xin | 2,023 | null | null | null | Optics Letters | Full-resolution and full-dynamic-range coded aperture compressive temporal imaging | Full-resolution and full-dynamic-range coded aperture ... | https://opg.optica.org/abstract.cfm?uri=ol-48-18-4813 | by P Wang · 2023 · Cited by 9 — Coded aperture compressive temporal imaging (CACTI) aims to capture a sequence of video frames in a single shot, using an off-the-shelf 2D sensor. |
Proximal Algorithm Unrolling: Flexible and Efficient Reconstruction
Networks for Single-Pixel Imaging | 2505.23180v1 | wang2024hierarchical | \cite{wang2024hierarchical} | Hierarchical Separable Video Transformer for Snapshot Compressive
Imaging | http://arxiv.org/abs/2407.11946v2 | Transformers have achieved the state-of-the-art performance on solving the
inverse problem of Snapshot Compressive Imaging (SCI) for video, whose
ill-posedness is rooted in the mixed degradation of spatial masking and
temporal aliasing. However, previous Transformers lack an insight into the
degradation and thus have l... | true | true | Wang, Ping and Zhang, Yulun and Wang, Lishun and Yuan, Xin | 2,024 | null | null | null | null | Hierarchical Separable Video Transformer for Snapshot Compressive
Imaging | pwangcs/HiSViT: [ECCV 2024] Hierarchical Separable ... | https://github.com/pwangcs/HiSViT | [ECCV 2024] Hierarchical Separable Video Transformer for Snapshot Compressive Imaging · Ping Wang, Yulun Zhang, Lishun Wang, Xin Yuan. Video SCI Reconstruction |
Proximal Algorithm Unrolling: Flexible and Efficient Reconstruction
Networks for Single-Pixel Imaging | 2505.23180v1 | wang2023deep | \cite{wang2023deep} | Deep Optics for Video Snapshot Compressive Imaging | http://arxiv.org/abs/2404.05274v1 | Video snapshot compressive imaging (SCI) aims to capture a sequence of video
frames with only a single shot of a 2D detector, whose backbones rest in
optical modulation patterns (also known as masks) and a computational
reconstruction algorithm. Advanced deep learning algorithms and mature hardware
are putting video SC... | true | true | Wang, Ping and Wang, Lishun and Yuan, Xin | 2,023 | null | null | null | null | Deep Optics for Video Snapshot Compressive Imaging | Deep Optics for Video Snapshot Compressive Imaging | http://arxiv.org/pdf/2404.05274v1 | Video snapshot compressive imaging (SCI) aims to capture a sequence of video
frames with only a single shot of a 2D detector, whose backbones rest in
optical modulation patterns (also known as masks) and a computational
reconstruction algorithm. Advanced deep learning algorithms and mature hardware
are putting video SC... |
Proximal Algorithm Unrolling: Flexible and Efficient Reconstruction
Networks for Single-Pixel Imaging | 2505.23180v1 | figueiredo2007gradient | \cite{figueiredo2007gradient} | Gradient projection for sparse reconstruction: Application to compressed sensing and other inverse problems | null | null | true | false | Figueiredo, M{\'a}rio AT and Nowak, Robert D and Wright, Stephen J | 2,007 | null | null | null | IEEE Journal of Selected Topics in Signal Processing | Gradient projection for sparse reconstruction: Application to compressed sensing and other inverse problems | Gradient Projection for Sparse Reconstruction: Application ... | https://citeseerx.ist.psu.edu/document?repid=rep1&type=pdf&doi=a5a5f31a9d521db9566db94410b06defbbd40c22 | by MAT Figueiredo · Cited by 4600 — Gradient projection (GP) algorithms are proposed for sparse reconstruction in signal processing, using bound-constrained quadratic programming, and are faster |
Proximal Algorithm Unrolling: Flexible and Efficient Reconstruction
Networks for Single-Pixel Imaging | 2505.23180v1 | 4587391 | \cite{4587391} | An efficient algorithm for compressed MR imaging using total variation and wavelets | null | null | true | false | Shiqian Ma and Wotao Yin and Yin Zhang and Chakraborty, Amit | 2,008 | null | null | null | null | An efficient algorithm for compressed MR imaging using total variation and wavelets | Compressed MRI reconstruction exploiting a rotation-invariant total ... | https://www.sciencedirect.com/science/article/abs/pii/S0730725X19307507 | An efficient algorithm for compressed MR imaging using total variation and wavelets. M. Lustig et al. Compressed sensing MRI. IEEE Signal Processing Magazine. |
Proximal Algorithm Unrolling: Flexible and Efficient Reconstruction
Networks for Single-Pixel Imaging | 2505.23180v1 | he2009exploiting | \cite{he2009exploiting} | Exploiting structure in wavelet-based Bayesian compressive sensing | null | null | true | false | He, Lihan and Carin, Lawrence | 2,009 | null | null | null | IEEE Transactions on Signal Processing | Exploiting structure in wavelet-based Bayesian compressive sensing | Exploiting structure in wavelet-based Bayesian compressive sensing | https://dl.acm.org/doi/abs/10.1109/tsp.2009.2022003 | The structure exploited within the wavelet coefficients is consistent with that used in wavelet-based compression algorithms. A hierarchical Bayesian model is |
Proximal Algorithm Unrolling: Flexible and Efficient Reconstruction
Networks for Single-Pixel Imaging | 2505.23180v1 | blumensath2009iterative | \cite{blumensath2009iterative} | Iterative Hard Thresholding for Compressed Sensing | http://arxiv.org/abs/0805.0510v1 | Compressed sensing is a technique to sample compressible signals below the
Nyquist rate, whilst still allowing near optimal reconstruction of the signal.
In this paper we present a theoretical analysis of the iterative hard
thresholding algorithm when applied to the compressed sensing recovery problem.
We show that the... | true | true | Blumensath, Thomas and Davies, Mike E | 2,009 | null | null | null | Applied and Computational Harmonic Analysis | Iterative Hard Thresholding for Compressed Sensing | Iterative Hard Thresholding for Compressed Sensing | http://arxiv.org/pdf/0805.0510v1 | Compressed sensing is a technique to sample compressible signals below the
Nyquist rate, whilst still allowing near optimal reconstruction of the signal.
In this paper we present a theoretical analysis of the iterative hard
thresholding algorithm when applied to the compressed sensing recovery problem.
We show that the... |
Proximal Algorithm Unrolling: Flexible and Efficient Reconstruction
Networks for Single-Pixel Imaging | 2505.23180v1 | beck2009fast | \cite{beck2009fast} | A fast iterative shrinkage-thresholding algorithm for linear inverse problems | null | null | true | false | Beck, Amir and Teboulle, Marc | 2,009 | null | null | null | SIAM Journal on Imaging Sciences | A fast iterative shrinkage-thresholding algorithm for linear inverse problems | [PDF] A Fast Iterative Shrinkage-Thresholding Algorithm for Linear Inverse ... | https://www.ceremade.dauphine.fr/~carlier/FISTA | Abstract. We consider the class of iterative shrinkage-thresholding algorithms (ISTA) for solving linear inverse problems arising in signal/image processing |
Proximal Algorithm Unrolling: Flexible and Efficient Reconstruction
Networks for Single-Pixel Imaging | 2505.23180v1 | kim2010compressed | \cite{kim2010compressed} | Compressed sensing using a Gaussian scale mixtures model in wavelet domain | null | null | true | false | Kim, Yookyung and Nadar, Mariappan S and Bilgin, Ali | 2,010 | null | null | null | null | Compressed sensing using a Gaussian scale mixtures model in wavelet domain | Compressed Sensing With a Gaussian Scale Mixture ... | https://pmc.ncbi.nlm.nih.gov/articles/PMC6207971/ | by J Meng · 2018 · Cited by 11 — In this method, the structure dependencies of signals in the wavelet domain were incorporated into the imaging framework through the Gaussian scale mixture |
Proximal Algorithm Unrolling: Flexible and Efficient Reconstruction
Networks for Single-Pixel Imaging | 2505.23180v1 | yang2011alternating | \cite{yang2011alternating} | Alternating Direction Algorithms for {$\ell_{1}$}-Problems in Compressive Sensing | null | null | true | false | Yang, Junfeng and Zhang, Yin | 2,011 | null | null | null | SIAM Journal on Scientific Computing | Alternating Direction Algorithms for {$\ell_{1}$}-Problems in Compressive Sensing | [PDF] alternating direction algorithms for `1-problems in compressive ... | https://www.cmor-faculty.rice.edu/~zhang/reports/tr0937.pdf | In this paper, we propose and study the use of alternating direction algorithms for several `1-norm minimization problems arising from sparse solution recovery |
Proximal Algorithm Unrolling: Flexible and Efficient Reconstruction
Networks for Single-Pixel Imaging | 2505.23180v1 | dong2014compressive | \cite{dong2014compressive} | Compressive sensing via nonlocal low-rank regularization | null | null | true | false | Dong, Weisheng and Shi, Guangming and Li, Xin and Ma, Yi and Huang, Feng | 2,014 | null | null | null | IEEE Transactions on Image Processing | Compressive sensing via nonlocal low-rank regularization | [PDF] Compressive Sensing via Nonlocal Low-rank Regularization | http://people.eecs.berkeley.edu/~yima/psfile/CS_low_rank_final.pdf | Experimental results have shown that the proposed NLR-CS algorithm can significantly outperform existing state-of-the-art CS techniques for image recovery. |
Proximal Algorithm Unrolling: Flexible and Efficient Reconstruction
Networks for Single-Pixel Imaging | 2505.23180v1 | Metzler2016FromDT | \cite{Metzler2016FromDT} | From Denoising to Compressed Sensing | http://arxiv.org/abs/1406.4175v5 | A denoising algorithm seeks to remove noise, errors, or perturbations from a
signal. Extensive research has been devoted to this arena over the last several
decades, and as a result, today's denoisers can effectively remove large
amounts of additive white Gaussian noise. A compressed sensing (CS)
reconstruction algorit... | true | true | Metzler, Christopher A and Maleki, Arian and Baraniuk, Richard G | 2,016 | null | null | null | IEEE Transactions on Information Theory | From Denoising to Compressed Sensing | From Denoising to Compressed Sensing | http://arxiv.org/pdf/1406.4175v5 | A denoising algorithm seeks to remove noise, errors, or perturbations from a
signal. Extensive research has been devoted to this arena over the last several
decades, and as a result, today's denoisers can effectively remove large
amounts of additive white Gaussian noise. A compressed sensing (CS)
reconstruction algorit... |
Proximal Algorithm Unrolling: Flexible and Efficient Reconstruction
Networks for Single-Pixel Imaging | 2505.23180v1 | zhang2021plug | \cite{zhang2021plug} | Deep Plug-and-Play Prior for Hyperspectral Image Restoration | http://arxiv.org/abs/2209.08240v1 | Deep-learning-based hyperspectral image (HSI) restoration methods have gained
great popularity for their remarkable performance but often demand expensive
network retraining whenever the specifics of task changes. In this paper, we
propose to restore HSIs in a unified approach with an effective plug-and-play
method, wh... | true | true | Zhang, Kai and Li, Yawei and Zuo, Wangmeng and Zhang, Lei and Van Gool, Luc and Timofte, Radu | 2,021 | null | null | null | IEEE Transactions on Pattern Analysis and Machine Intelligence | Deep Plug-and-Play Prior for Hyperspectral Image Restoration | Deep Plug-and-Play Prior for Hyperspectral Image Restoration | https://www.researchgate.net/publication/363667470_Deep_Plug-and-Play_Prior_for_Hyperspectral_Image_Restoration | In this paper, we propose to restore HSIs in a unified approach with an effective plug-and-play method, which can jointly retain the flexibility |
Proximal Algorithm Unrolling: Flexible and Efficient Reconstruction
Networks for Single-Pixel Imaging | 2505.23180v1 | hurault2022gradient | \cite{hurault2022gradient} | Gradient Step Denoiser for convergent Plug-and-Play | http://arxiv.org/abs/2110.03220v2 | Plug-and-Play methods constitute a class of iterative algorithms for imaging
problems where regularization is performed by an off-the-shelf denoiser.
Although Plug-and-Play methods can lead to tremendous visual performance for
various image problems, the few existing convergence guarantees are based on
unrealistic (or ... | true | true | Hurault, Samuel and Leclaire, Arthur and Papadakis, Nicolas | 2,022 | null | null | null | null | Gradient Step Denoiser for convergent Plug-and-Play | [2110.03220] Gradient Step Denoiser for convergent Plug-and-Play | https://arxiv.org/abs/2110.03220 | We propose a new type of Plug-and-Play methods, based on half-quadratic splitting, for which the denoiser is realized as a gradient descent step. |
Proximal Algorithm Unrolling: Flexible and Efficient Reconstruction
Networks for Single-Pixel Imaging | 2505.23180v1 | hurault2022proximal | \cite{hurault2022proximal} | Proximal denoiser for convergent plug-and-play optimization with nonconvex regularization | null | null | true | false | Hurault, Samuel and Leclaire, Arthur and Papadakis, Nicolas | 2,022 | null | null | null | null | Proximal denoiser for convergent plug-and-play optimization with nonconvex regularization | [PDF] Proximal Denoiser for Convergent Plug-and-Play Optimization with ... | https://icml.cc/media/icml-2022/Slides/18135.pdf | Proximal Denoiser for Convergent. Plug-and-Play Optimization with Nonconvex. Regularization. Samuel Hurault, Arthur Leclaire, Nicolas Papadakis. Institut de |
Proximal Algorithm Unrolling: Flexible and Efficient Reconstruction
Networks for Single-Pixel Imaging | 2505.23180v1 | fangs | \cite{fangs} | What's in a Prior? Learned Proximal Networks for Inverse Problems | http://arxiv.org/abs/2310.14344v2 | Proximal operators are ubiquitous in inverse problems, commonly appearing as
part of algorithmic strategies to regularize problems that are otherwise
ill-posed. Modern deep learning models have been brought to bear for these
tasks too, as in the framework of plug-and-play or deep unrolling, where they
loosely resemble ... | true | true | Fang, Zhenghan and Buchanan, Sam and Sulam, Jeremias | null | null | null | null | null | What's in a Prior? Learned Proximal Networks for Inverse Problems | What's in a Prior? Learned Proximal Networks for Inverse Problems | http://arxiv.org/pdf/2310.14344v2 | Proximal operators are ubiquitous in inverse problems, commonly appearing as
part of algorithmic strategies to regularize problems that are otherwise
ill-posed. Modern deep learning models have been brought to bear for these
tasks too, as in the framework of plug-and-play or deep unrolling, where they
loosely resemble ... |
Proximal Algorithm Unrolling: Flexible and Efficient Reconstruction
Networks for Single-Pixel Imaging | 2505.23180v1 | hu2024stochastic | \cite{hu2024stochastic} | Stochastic Deep Restoration Priors for Imaging Inverse Problems | http://arxiv.org/abs/2410.02057v1 | Deep neural networks trained as image denoisers are widely used as priors for
solving imaging inverse problems. While Gaussian denoising is thought
sufficient for learning image priors, we show that priors from deep models
pre-trained as more general restoration operators can perform better. We
introduce Stochastic dee... | true | true | Hu, Yuyang and Peng, Albert and Gan, Weijie and Milanfar, Peyman and Delbracio, Mauricio and Kamilov, Ulugbek S | 2,024 | null | null | null | arXiv preprint arXiv:2410.02057 | Stochastic Deep Restoration Priors for Imaging Inverse Problems | Stochastic Deep Restoration Priors for Imaging Inverse Problems | http://arxiv.org/pdf/2410.02057v1 | Deep neural networks trained as image denoisers are widely used as priors for
solving imaging inverse problems. While Gaussian denoising is thought
sufficient for learning image priors, we show that priors from deep models
pre-trained as more general restoration operators can perform better. We
introduce Stochastic dee... |
Proximal Algorithm Unrolling: Flexible and Efficient Reconstruction
Networks for Single-Pixel Imaging | 2505.23180v1 | kulkarni2016reconnet | \cite{kulkarni2016reconnet} | ReconNet: Non-Iterative Reconstruction of Images from Compressively
Sensed Random Measurements | http://arxiv.org/abs/1601.06892v2 | The goal of this paper is to present a non-iterative and more importantly an
extremely fast algorithm to reconstruct images from compressively sensed (CS)
random measurements. To this end, we propose a novel convolutional neural
network (CNN) architecture which takes in CS measurements of an image as input
and outputs ... | true | true | Kulkarni, Kuldeep and Lohit, Suhas and Turaga, Pavan and Kerviche, Ronan and Ashok, Amit | 2,016 | null | null | null | null | ReconNet: Non-Iterative Reconstruction of Images from Compressively
Sensed Random Measurements | ReconNet: Non-Iterative Reconstruction of Images From ... | https://openaccess.thecvf.com/content_cvpr_2016/papers/Kulkarni_ReconNet_Non-Iterative_Reconstruction_CVPR_2016_paper.pdf | by K Kulkarni · 2016 · Cited by 941 — ReconNet is a non-iterative, fast CNN algorithm that reconstructs images from compressively sensed measurements, using a novel CNN architecture. |
Proximal Algorithm Unrolling: Flexible and Efficient Reconstruction
Networks for Single-Pixel Imaging | 2505.23180v1 | shi2019image | \cite{shi2019image} | Image compressed sensing using convolutional neural network | null | null | true | false | Shi, Wuzhen and Jiang, Feng and Liu, Shaohui and Zhao, Debin | 2,019 | null | null | null | IEEE Transactions on Image Processing | Image compressed sensing using convolutional neural network | inofficialamanjha/Image-Compressed-Sensing-using- ... | https://github.com/inofficialamanjha/Image-Compressed-Sensing-using-convolutional-Neural-Network | We have implemented an image CS framework using Convolutional Neural Network (CSNet), that includes a sampling network and a reconstruction network, which are |
Proximal Algorithm Unrolling: Flexible and Efficient Reconstruction
Networks for Single-Pixel Imaging | 2505.23180v1 | shi2019scalable | \cite{shi2019scalable} | Scalable convolutional neural network for image compressed sensing | null | null | true | false | Shi, Wuzhen and Jiang, Feng and Liu, Shaohui and Zhao, Debin | 2,019 | null | null | null | null | Scalable convolutional neural network for image compressed sensing | Scalable Convolutional Neural Network for Image ... | https://openaccess.thecvf.com/content_CVPR_2019/papers/Shi_Scalable_Convolutional_Neural_Network_for_Image_Compressed_Sensing_CVPR_2019_paper.pdf | by W Shi · 2019 · Cited by 205 — compressed sensing. SCSNet is the first to implement s- calable sampling and scalable reconstruction using CNN, which provides both coarse granular scalability |
Proximal Algorithm Unrolling: Flexible and Efficient Reconstruction
Networks for Single-Pixel Imaging | 2505.23180v1 | yao2019dr2 | \cite{yao2019dr2} | Dr2-net: Deep residual reconstruction network for image compressive sensing | null | null | true | false | Yao, Hantao and Dai, Feng and Zhang, Shiliang and Zhang, Yongdong and Tian, Qi and Xu, Changsheng | 2,019 | null | null | null | Neurocomputing | Dr2-net: Deep residual reconstruction network for image compressive sensing | DR2-Net: Deep Residual Reconstruction Network for Image Compressive Sensing | http://arxiv.org/pdf/1702.05743v4 | Most traditional algorithms for compressive sensing image reconstruction
suffer from the intensive computation. Recently, deep learning-based
reconstruction algorithms have been reported, which dramatically reduce the
time complexity than iterative reconstruction algorithms. In this paper, we
propose a novel \textbf{D}... |
Proximal Algorithm Unrolling: Flexible and Efficient Reconstruction
Networks for Single-Pixel Imaging | 2505.23180v1 | metzler2017learned | \cite{metzler2017learned} | Learned D-AMP: Principled Neural Network based Compressive Image Recovery | null | null | true | false | Metzler, Chris and Mousavi, Ali and Baraniuk, Richard | 2,017 | null | null | null | null | Learned D-AMP: Principled Neural Network based Compressive Image Recovery | Learned D-AMP: Principled Neural Network based Compressive Image Recovery | http://arxiv.org/pdf/1704.06625v4 | Compressive image recovery is a challenging problem that requires fast and
accurate algorithms. Recently, neural networks have been applied to this
problem with promising results. By exploiting massively parallel GPU processing
architectures and oodles of training data, they can run orders of magnitude
faster than exis... |
Proximal Algorithm Unrolling: Flexible and Efficient Reconstruction
Networks for Single-Pixel Imaging | 2505.23180v1 | zhang2018ista | \cite{zhang2018ista} | ISTA-Net: Interpretable optimization-inspired deep network for image compressive sensing | null | null | true | false | Zhang, Jian and Ghanem, Bernard | 2,018 | null | null | null | null | ISTA-Net: Interpretable optimization-inspired deep network for image compressive sensing | ISTA-Net: Interpretable Optimization-Inspired Deep Network for ... | https://ieeexplore.ieee.org/iel7/8576498/8578098/08578294.pdf | ISTA-Net is a structured deep network inspired by ISTA for image compressive sensing, combining traditional and network-based methods, with learned parameters. |
Proximal Algorithm Unrolling: Flexible and Efficient Reconstruction
Networks for Single-Pixel Imaging | 2505.23180v1 | yang2018admm | \cite{yang2018admm} | ADMM-CSNet: A deep learning approach for image compressive sensing | null | null | true | false | Yang, Yan and Sun, Jian and Li, Huibin and Xu, Zongben | 2,018 | null | null | null | IEEE Transactions on Pattern Analysis and Machine Intelligence | ADMM-CSNet: A deep learning approach for image compressive sensing | ADMM-CSNet: A Deep Learning Approach for Image Compressive ... | https://ieeexplore.ieee.org/document/8550778/ | In this paper, we propose two versions of a novel deep learning architecture, dubbed as ADMM-CSNet, by combining the traditional model-based CS method and data |
Proximal Algorithm Unrolling: Flexible and Efficient Reconstruction
Networks for Single-Pixel Imaging | 2505.23180v1 | zhang2020optimization | \cite{zhang2020optimization} | Optimization-inspired compact deep compressive sensing | null | null | true | false | Zhang, Jian and Zhao, Chen and Gao, Wen | 2,020 | null | null | null | IEEE Journal of Selected Topics in Signal Processing | Optimization-inspired compact deep compressive sensing | Optimization-Inspired Compact Deep Compressive Sensing | https://ieeexplore.ieee.org/document/9019857/ | In this paper, we propose a novel framework to design an OPtimization-INspired Explicable deep Network, dubbed OPINE-Net, for adaptive sampling and recovery. |
Proximal Algorithm Unrolling: Flexible and Efficient Reconstruction
Networks for Single-Pixel Imaging | 2505.23180v1 | zhang2020amp | \cite{zhang2020amp} | AMP-Net: Denoising-based deep unfolding for compressive image sensing | null | null | true | false | Zhang, Zhonghao and Liu, Yipeng and Liu, Jiani and Wen, Fei and Zhu, Ce | 2,020 | null | null | null | IEEE Transactions on Image Processing | AMP-Net: Denoising-based deep unfolding for compressive image sensing | Denoising-Based Deep Unfolding for Compressive Image ... | https://ieeexplore.ieee.org/iel7/83/9263394/09298950.pdf | by Z Zhang · 2020 · Cited by 297 — AMP-Net is a deep unfolding model for compressive image sensing, established by unfolding the denoising process of the approximate message |
Proximal Algorithm Unrolling: Flexible and Efficient Reconstruction
Networks for Single-Pixel Imaging | 2505.23180v1 | shen2022transcs | \cite{shen2022transcs} | TransCS: a transformer-based hybrid architecture for image compressed sensing | null | null | true | false | Shen, Minghe and Gan, Hongping and Ning, Chao and Hua, Yi and Zhang, Tao | 2,022 | null | null | null | IEEE Transactions on Image Processing | TransCS: a transformer-based hybrid architecture for image compressed sensing | TransCS: A Transformer-Based Hybrid Architecture for ... | https://www.researchgate.net/publication/364935930_TransCS_A_Transformer-based_Hybrid_Architecture_for_Image_Compressed_Sensing | In this paper, we propose a novel Transformer-based hybrid architecture (dubbed TransCS) to achieve high-quality image CS. In the sampling module, TransCS |
Proximal Algorithm Unrolling: Flexible and Efficient Reconstruction
Networks for Single-Pixel Imaging | 2505.23180v1 | song2021memory | \cite{song2021memory} | Memory-Augmented Deep Unfolding Network for Compressive Sensing | http://arxiv.org/abs/2110.09766v2 | Mapping a truncated optimization method into a deep neural network, deep
unfolding network (DUN) has attracted growing attention in compressive sensing
(CS) due to its good interpretability and high performance. Each stage in DUNs
corresponds to one iteration in optimization. By understanding DUNs from the
perspective ... | true | true | Song, Jiechong and Chen, Bin and Zhang, Jian | 2,021 | null | null | null | null | Memory-Augmented Deep Unfolding Network for Compressive Sensing | Memory-Augmented Deep Unfolding Network for Compressive ... | https://dl.acm.org/doi/10.1145/3474085.3475562 | Learning memory augmented cascading network for compressed sensing of images. In Proceedings of the European Conference on Computer Vision (ECCV) |
Proximal Algorithm Unrolling: Flexible and Efficient Reconstruction
Networks for Single-Pixel Imaging | 2505.23180v1 | you2021coast | \cite{you2021coast} | COAST: COntrollable Arbitrary-Sampling NeTwork for Compressive Sensing | http://arxiv.org/abs/2107.07225v1 | Recent deep network-based compressive sensing (CS) methods have achieved
great success. However, most of them regard different sampling matrices as
different independent tasks and need to train a specific model for each target
sampling matrix. Such practices give rise to inefficiency in computing and
suffer from poor g... | true | true | You, Di and Zhang, Jian and Xie, Jingfen and Chen, Bin and Ma, Siwei | 2,021 | null | null | null | IEEE Transactions on Image Processing | COAST: COntrollable Arbitrary-Sampling NeTwork for Compressive Sensing | COntrollable Arbitrary-Sampling NeTwork for Compressive ... | https://ieeexplore.ieee.org/iel7/83/9263394/09467810.pdf | by D You · 2021 · Cited by 150 — In this paper, we pro- pose a novel COntrollable Arbitrary-Sampling neTwork, dubbed. COAST, to solve CS problems of arbitrary-sampling matrices. |
Proximal Algorithm Unrolling: Flexible and Efficient Reconstruction
Networks for Single-Pixel Imaging | 2505.23180v1 | mou2022deep | \cite{mou2022deep} | Deep Generalized Unfolding Networks for Image Restoration | http://arxiv.org/abs/2204.13348v1 | Deep neural networks (DNN) have achieved great success in image restoration.
However, most DNN methods are designed as a black box, lacking transparency and
interpretability. Although some methods are proposed to combine traditional
optimization algorithms with DNN, they usually demand pre-defined degradation
processes... | true | true | Mou, Chong and Wang, Qian and Zhang, Jian | 2,022 | null | null | null | null | Deep Generalized Unfolding Networks for Image Restoration | Deep Generalized Unfolding Networks for Image Restoration | http://arxiv.org/pdf/2204.13348v1 | Deep neural networks (DNN) have achieved great success in image restoration.
However, most DNN methods are designed as a black box, lacking transparency and
interpretability. Although some methods are proposed to combine traditional
optimization algorithms with DNN, they usually demand pre-defined degradation
processes... |
Proximal Algorithm Unrolling: Flexible and Efficient Reconstruction
Networks for Single-Pixel Imaging | 2505.23180v1 | ye2023csformer | \cite{ye2023csformer} | CSformer: Bridging Convolution and Transformer for Compressive Sensing | http://arxiv.org/abs/2112.15299v1 | Convolution neural networks (CNNs) have succeeded in compressive image
sensing. However, due to the inductive bias of locality and weight sharing, the
convolution operations demonstrate the intrinsic limitations in modeling the
long-range dependency. Transformer, designed initially as a
sequence-to-sequence model, exce... | true | true | Ye, Dongjie and Ni, Zhangkai and Wang, Hanli and Zhang, Jian and Wang, Shiqi and Kwong, Sam | 2,023 | null | null | null | IEEE Transactions on Image Processing | CSformer: Bridging Convolution and Transformer for Compressive Sensing | CSformer: Bridging Convolution and Transformer for Compressive Sensing | http://arxiv.org/pdf/2112.15299v1 | Convolution neural networks (CNNs) have succeeded in compressive image
sensing. However, due to the inductive bias of locality and weight sharing, the
convolution operations demonstrate the intrinsic limitations in modeling the
long-range dependency. Transformer, designed initially as a
sequence-to-sequence model, exce... |
Proximal Algorithm Unrolling: Flexible and Efficient Reconstruction
Networks for Single-Pixel Imaging | 2505.23180v1 | song2023optimization | \cite{song2023optimization} | Optimization-Inspired Cross-Attention Transformer for Compressive
Sensing | http://arxiv.org/abs/2304.13986v1 | By integrating certain optimization solvers with deep neural networks, deep
unfolding network (DUN) with good interpretability and high performance has
attracted growing attention in compressive sensing (CS). However, existing DUNs
often improve the visual quality at the price of a large number of parameters
and have t... | true | true | Song, Jiechong and Mou, Chong and Wang, Shiqi and Ma, Siwei and Zhang, Jian | 2,023 | null | null | null | null | Optimization-Inspired Cross-Attention Transformer for Compressive
Sensing | Optimization-Inspired Cross-Attention Transformer for ... | https://arxiv.org/abs/2304.13986 | by J Song · 2023 · Cited by 70 — In this paper, we propose an Optimization-inspired Cross-attention Transformer (OCT) module as an iterative process, leading to a lightweight OCT-based |
Proximal Algorithm Unrolling: Flexible and Efficient Reconstruction
Networks for Single-Pixel Imaging | 2505.23180v1 | wang2023saunet | \cite{wang2023saunet} | Saunet: Spatial-attention unfolding network for image compressive sensing | null | null | true | false | Wang, Ping and Yuan, Xin | 2,023 | null | null | null | null | Saunet: Spatial-attention unfolding network for image compressive sensing | Spatial-Attention Unfolding Network for Image Compressive Sensing". | https://github.com/pwangcs/SAUNet | SAUNet has achieved SOTA performance. More importantly, SAUNet contributes to real-world image compressive sensing systems, such as single-pixel cameras. |
Proximal Algorithm Unrolling: Flexible and Efficient Reconstruction
Networks for Single-Pixel Imaging | 2505.23180v1 | wang2024ufc | \cite{wang2024ufc} | UFC-Net: Unrolling Fixed-point Continuous Network for Deep Compressive Sensing | null | null | true | false | Wang, Xiaoyang and Gan, Hongping | 2,024 | null | null | null | null | UFC-Net: Unrolling Fixed-point Continuous Network for Deep Compressive Sensing | [PDF] UFC-Net: Unrolling Fixed-point Continuous Network for Deep ... | https://openaccess.thecvf.com/content/CVPR2024/papers/Wang_UFC-Net_Unrolling_Fixed-point_Continuous_Network_for_Deep_Compressive_Sensing_CVPR_2024_paper.pdf | In this paper, we propose Unrolling Fixed- point Continuous Network (UFC-Net), a novel deep CS framework motivated by the traditional fixed-point contin- uous |
Proximal Algorithm Unrolling: Flexible and Efficient Reconstruction
Networks for Single-Pixel Imaging | 2505.23180v1 | guo2024cpp | \cite{guo2024cpp} | CPP-Net: Embracing Multi-Scale Feature Fusion into Deep Unfolding CP-PPA Network for Compressive Sensing | null | null | true | false | Guo, Zhen and Gan, Hongping | 2,024 | null | null | null | null | CPP-Net: Embracing Multi-Scale Feature Fusion into Deep Unfolding CP-PPA Network for Compressive Sensing | [PDF] Embracing Multi-Scale Feature Fusion into Deep Unfolding CP-PPA ... | https://openaccess.thecvf.com/content/CVPR2024/papers/Guo_CPP-Net_Embracing_Multi-Scale_Feature_Fusion_into_Deep_Unfolding_CP-PPA_Network_CVPR_2024_paper.pdf | In this paper, we propose CPP-Net, a novel deep unfolding CS framework, inspired by the primal- dual hybrid strategy of the Chambolle and Pock Proximal. Point |
Proximal Algorithm Unrolling: Flexible and Efficient Reconstruction
Networks for Single-Pixel Imaging | 2505.23180v1 | qu2024dual | \cite{qu2024dual} | Dual-Scale Transformer for Large-Scale Single-Pixel Imaging | http://arxiv.org/abs/2404.05001v1 | Single-pixel imaging (SPI) is a potential computational imaging technique
which produces image by solving an illposed reconstruction problem from few
measurements captured by a single-pixel detector. Deep learning has achieved
impressive success on SPI reconstruction. However, previous poor reconstruction
performance a... | true | true | Qu, Gang and Wang, Ping and Yuan, Xin | 2,024 | null | null | null | null | Dual-Scale Transformer for Large-Scale Single-Pixel Imaging | [PDF] Dual-Scale Transformer for Large-Scale Single-Pixel Imaging | https://openaccess.thecvf.com/content/CVPR2024/papers/Qu_Dual-Scale_Transformer_for_Large-Scale_Single-Pixel_Imaging_CVPR_2024_paper.pdf | In this paper, we propose a deep unfolding network with hybrid-attention. Transformer on Kronecker SPI model, dubbed HATNet, to im- prove the imaging quality of |
Proximal Algorithm Unrolling: Flexible and Efficient Reconstruction
Networks for Single-Pixel Imaging | 2505.23180v1 | yuan2016generalized | \cite{yuan2016generalized} | Generalized Alternating Projection Based Total Variation Minimization
for Compressive Sensing | http://arxiv.org/abs/1511.03890v1 | We consider the total variation (TV) minimization problem used for
compressive sensing and solve it using the generalized alternating projection
(GAP) algorithm. Extensive results demonstrate the high performance of proposed
algorithm on compressive sensing, including two dimensional images,
hyperspectral images and vi... | true | true | Yuan, Xin | 2,016 | null | null | null | null | Generalized Alternating Projection Based Total Variation Minimization
for Compressive Sensing | Generalized alternating projection based total variation minimization ... | https://ieeexplore.ieee.org/document/7532817/ | We consider the total variation (TV) minimization problem used for compressive sensing and solve it using the generalized alternating projection (GAP) |
Proximal Algorithm Unrolling: Flexible and Efficient Reconstruction
Networks for Single-Pixel Imaging | 2505.23180v1 | geman1995nonlinear | \cite{geman1995nonlinear} | Nonlinear image recovery with half-quadratic regularization | null | null | true | false | Geman, Donald and Yang, Chengda | 1,995 | null | null | null | IEEE transactions on Image Processing | Nonlinear image recovery with half-quadratic regularization | Nonlinear image recovery with half-quadratic regularization | https://www.semanticscholar.org/paper/Nonlinear-image-recovery-with-half-quadratic-Geman-Yang/1c99baa92387ead70c668dde6a6ed73b20697a6f | This approach is based on an auxiliary array and an extended objective function in which the original variables appear quadratically and the auxiliary |
Proximal Algorithm Unrolling: Flexible and Efficient Reconstruction
Networks for Single-Pixel Imaging | 2505.23180v1 | romano2017little | \cite{romano2017little} | The Little Engine that Could: Regularization by Denoising (RED) | http://arxiv.org/abs/1611.02862v3 | Removal of noise from an image is an extensively studied problem in image
processing. Indeed, the recent advent of sophisticated and highly effective
denoising algorithms lead some to believe that existing methods are touching
the ceiling in terms of noise removal performance. Can we leverage this
impressive achievemen... | true | true | Romano, Yaniv and Elad, Michael and Milanfar, Peyman | 2,017 | null | null | null | SIAM Journal on Imaging Sciences | The Little Engine that Could: Regularization by Denoising (RED) | The Little Engine that Could: Regularization by Denoising (RED) | http://arxiv.org/pdf/1611.02862v3 | Removal of noise from an image is an extensively studied problem in image
processing. Indeed, the recent advent of sophisticated and highly effective
denoising algorithms lead some to believe that existing methods are touching
the ceiling in terms of noise removal performance. Can we leverage this
impressive achievemen... |
PS4PRO: Pixel-to-pixel Supervision for Photorealistic Rendering and
Optimization | 2505.22616v1 | choi2007motion | \cite{choi2007motion} | Motion-compensated frame interpolation using bilateral motion estimation and adaptive overlapped block motion compensation | null | null | true | false | Choi, Byeong-Doo and Han, Jong-Woo and Kim, Chang-Su and Ko, Sung-Jea | 2,007 | null | null | null | IEEE Transactions on Circuits and Systems for Video Technology | Motion-compensated frame interpolation using bilateral motion estimation and adaptive overlapped block motion compensation | Motion-compensated frame interpolation using bilateral ... | https://pure.korea.ac.kr/en/publications/motion-compensated-frame-interpolation-using-bilateral-motion-est/fingerprints/ | Dive into the research topics of 'Motion-compensated frame interpolation using bilateral motion estimation and adaptive overlapped block motion compensation'. |
PS4PRO: Pixel-to-pixel Supervision for Photorealistic Rendering and
Optimization | 2505.22616v1 | parihar2022comprehensive | \cite{parihar2022comprehensive} | AceVFI: A Comprehensive Survey of Advances in Video Frame Interpolation | http://arxiv.org/abs/2506.01061v1 | Video Frame Interpolation (VFI) is a fundamental Low-Level Vision (LLV) task
that synthesizes intermediate frames between existing ones while maintaining
spatial and temporal coherence. VFI techniques have evolved from classical
motion compensation-based approach to deep learning-based approach, including
kernel-, flow... | true | true | Parihar, Anil Singh and Varshney, Disha and Pandya, Kshitija and Aggarwal, Ashray | 2,022 | null | null | null | The Visual Computer | AceVFI: A Comprehensive Survey of Advances in Video Frame Interpolation | AceVFI: A Comprehensive Survey of Advances in Video Frame Interpolation | http://arxiv.org/pdf/2506.01061v1 | Video Frame Interpolation (VFI) is a fundamental Low-Level Vision (LLV) task
that synthesizes intermediate frames between existing ones while maintaining
spatial and temporal coherence. VFI techniques have evolved from classical
motion compensation-based approach to deep learning-based approach, including
kernel-, flow... |
PS4PRO: Pixel-to-pixel Supervision for Photorealistic Rendering and
Optimization | 2505.22616v1 | DAIN | \cite{DAIN} | Depth-Aware Video Frame Interpolation | http://arxiv.org/abs/1904.00830v1 | Video frame interpolation aims to synthesize nonexistent frames in-between
the original frames. While significant advances have been made from the recent
deep convolutional neural networks, the quality of interpolation is often
reduced due to large object motion or occlusion. In this work, we propose a
video frame inte... | true | true | Bao, Wenbo and Lai, Wei-Sheng and Ma, Chao and Zhang, Xiaoyun and Gao, Zhiyong and Yang, Ming-Hsuan | 2,019 | null | null | null | null | Depth-Aware Video Frame Interpolation | [PDF] Depth-Aware Video Frame Interpolation - CVF Open Access | https://openaccess.thecvf.com/content_CVPR_2019/papers/Bao_Depth-Aware_Video_Frame_Interpolation_CVPR_2019_paper.pdf | Video frame interpolation aims to synthesize non- existent frames in-between the original frames. While sig- nificant advances have been made from the |
PS4PRO: Pixel-to-pixel Supervision for Photorealistic Rendering and
Optimization | 2505.22616v1 | RIFE | \cite{RIFE} | Real-Time Intermediate Flow Estimation for Video Frame Interpolation | http://arxiv.org/abs/2011.06294v12 | Real-time video frame interpolation (VFI) is very useful in video processing,
media players, and display devices. We propose RIFE, a Real-time Intermediate
Flow Estimation algorithm for VFI. To realize a high-quality flow-based VFI
method, RIFE uses a neural network named IFNet that can estimate the
intermediate flows ... | true | true | Huang, Zhewei and Zhang, Tianyuan and Heng, Wen and Shi, Boxin and Zhou, Shuchang | 2,022 | null | null | null | null | Real-Time Intermediate Flow Estimation for Video Frame Interpolation | Real-Time Intermediate Flow Estimation for Video Frame ... | https://www.ecva.net/papers/eccv_2022/papers_ECCV/papers/136740608.pdf | Video Frame Interpolation (VFI) aims to synthesize intermediate frames between two consecutive video frames. VFI supports various applications like slow-motion. |
PS4PRO: Pixel-to-pixel Supervision for Photorealistic Rendering and
Optimization | 2505.22616v1 | m2m | \cite{m2m} | Many-to-many Splatting for Efficient Video Frame Interpolation | http://arxiv.org/abs/2204.03513v1 | Motion-based video frame interpolation commonly relies on optical flow to
warp pixels from the inputs to the desired interpolation instant. Yet due to
the inherent challenges of motion estimation (e.g. occlusions and
discontinuities), most state-of-the-art interpolation approaches require
subsequent refinement of the w... | true | true | Hu, Ping and Niklaus, Simon and Sclaroff, Stan and Saenko, Kate | 2,022 | null | null | null | null | Many-to-many Splatting for Efficient Video Frame Interpolation | Many-to-many Splatting for Efficient Video Frame Interpolation | https://ieeexplore.ieee.org/iel7/9878378/9878366/09878793.pdf | In this work, we propose a fully differentiable Many-to-Many (M2M) splatting framework to interpolate frames efficiently. Specifically, given a frame pair, we |
PS4PRO: Pixel-to-pixel Supervision for Photorealistic Rendering and
Optimization | 2505.22616v1 | EMA | \cite{EMA} | Extracting Motion and Appearance via Inter-Frame Attention for Efficient
Video Frame Interpolation | http://arxiv.org/abs/2303.00440v2 | Effectively extracting inter-frame motion and appearance information is
important for video frame interpolation (VFI). Previous works either extract
both types of information in a mixed way or elaborate separate modules for each
type of information, which lead to representation ambiguity and low efficiency.
In this pap... | true | true | Zhang, Guozhen and Zhu, Yuhan and Wang, Haonan and Chen, Youxin and Wu, Gangshan and Wang, Limin | 2,023 | null | null | null | null | Extracting Motion and Appearance via Inter-Frame Attention for Efficient
Video Frame Interpolation | Extracting Motion and Appearance via Inter-Frame Attention ... | https://openaccess.thecvf.com/content/CVPR2023/papers/Zhang_Extracting_Motion_and_Appearance_via_Inter-Frame_Attention_for_Efficient_Video_CVPR_2023_paper.pdf | by G Zhang · 2023 · Cited by 157 — We propose to utilize inter-frame attention to extract both motion and appearance information simultane- ously for video frame interpolation. • An hybrid CNN |
PS4PRO: Pixel-to-pixel Supervision for Photorealistic Rendering and
Optimization | 2505.22616v1 | unisim | \cite{unisim} | UniSim: A Neural Closed-Loop Sensor Simulator | http://arxiv.org/abs/2308.01898v1 | Rigorously testing autonomy systems is essential for making safe self-driving
vehicles (SDV) a reality. It requires one to generate safety critical scenarios
beyond what can be collected safely in the world, as many scenarios happen
rarely on public roads. To accurately evaluate performance, we need to test the
SDV on ... | true | true | Yang, Ze and Chen, Yun and Wang, Jingkang and Manivasagam, Sivabalan and Ma, Wei-Chiu and Yang, Anqi Joyce and Urtasun, Raquel | 2,023 | null | null | null | null | UniSim: A Neural Closed-Loop Sensor Simulator | [2308.01898] UniSim: A Neural Closed-Loop Sensor Simulator - arXiv | https://arxiv.org/abs/2308.01898 | A neural sensor simulator that takes a single recorded log captured by a sensor-equipped vehicle and converts it into a realistic closed-loop multi-sensor |
PS4PRO: Pixel-to-pixel Supervision for Photorealistic Rendering and
Optimization | 2505.22616v1 | neurad | \cite{neurad} | NeuRAD: Neural Rendering for Autonomous Driving | http://arxiv.org/abs/2311.15260v3 | Neural radiance fields (NeRFs) have gained popularity in the autonomous
driving (AD) community. Recent methods show NeRFs' potential for closed-loop
simulation, enabling testing of AD systems, and as an advanced training data
augmentation technique. However, existing methods often require long training
times, dense sem... | true | true | Tonderski, Adam and Lindstr{\"o}m, Carl and Hess, Georg and Ljungbergh, William and Svensson, Lennart and Petersson, Christoffer | 2,024 | null | null | null | null | NeuRAD: Neural Rendering for Autonomous Driving | NeuRAD: Neural Rendering for Autonomous Driving | http://arxiv.org/pdf/2311.15260v3 | Neural radiance fields (NeRFs) have gained popularity in the autonomous
driving (AD) community. Recent methods show NeRFs' potential for closed-loop
simulation, enabling testing of AD systems, and as an advanced training data
augmentation technique. However, existing methods often require long training
times, dense sem... |
PS4PRO: Pixel-to-pixel Supervision for Photorealistic Rendering and
Optimization | 2505.22616v1 | cao2024lightning | \cite{cao2024lightning} | Lightning NeRF: Efficient Hybrid Scene Representation for Autonomous
Driving | http://arxiv.org/abs/2403.05907v1 | Recent studies have highlighted the promising application of NeRF in
autonomous driving contexts. However, the complexity of outdoor environments,
combined with the restricted viewpoints in driving scenarios, complicates the
task of precisely reconstructing scene geometry. Such challenges often lead to
diminished quali... | true | true | Cao, Junyi and Li, Zhichao and Wang, Naiyan and Ma, Chao | 2,024 | null | null | null | arXiv preprint arXiv:2403.05907 | Lightning NeRF: Efficient Hybrid Scene Representation for Autonomous
Driving | Efficient Hybrid Scene Representation for Autonomous Driving - arXiv | https://arxiv.org/abs/2403.05907 | We present Lightning NeRF. It uses an efficient hybrid scene representation that effectively utilizes the geometry prior from LiDAR in autonomous driving |
PS4PRO: Pixel-to-pixel Supervision for Photorealistic Rendering and
Optimization | 2505.22616v1 | jiang2023alignerf | \cite{jiang2023alignerf} | AligNeRF: High-Fidelity Neural Radiance Fields via Alignment-Aware
Training | http://arxiv.org/abs/2211.09682v1 | Neural Radiance Fields (NeRFs) are a powerful representation for modeling a
3D scene as a continuous function. Though NeRF is able to render complex 3D
scenes with view-dependent effects, few efforts have been devoted to exploring
its limits in a high-resolution setting. Specifically, existing NeRF-based
methods face s... | true | true | Jiang, Yifan and Hedman, Peter and Mildenhall, Ben and Xu, Dejia and Barron, Jonathan T and Wang, Zhangyang and Xue, Tianfan | 2,023 | null | null | null | null | AligNeRF: High-Fidelity Neural Radiance Fields via Alignment-Aware
Training | [PDF] High-Fidelity Neural Radiance Fields via Alignment-Aware Training | https://openaccess.thecvf.com/content/CVPR2023/papers/Jiang_AligNeRF_High-Fidelity_Neural_Radiance_Fields_via_Alignment-Aware_Training_CVPR_2023_paper.pdf | AligNeRF uses staged training: starting with an initial “normal” pre-training stage, followed by an alignment-aware fine-tuning stage. We choose mip-NeRF. 360 |
PS4PRO: Pixel-to-pixel Supervision for Photorealistic Rendering and
Optimization | 2505.22616v1 | wynn2023diffusionerf | \cite{wynn2023diffusionerf} | DiffusioNeRF: Regularizing Neural Radiance Fields with Denoising
Diffusion Models | http://arxiv.org/abs/2302.12231v3 | Under good conditions, Neural Radiance Fields (NeRFs) have shown impressive
results on novel view synthesis tasks. NeRFs learn a scene's color and density
fields by minimizing the photometric discrepancy between training views and
differentiable renderings of the scene. Once trained from a sufficient set of
views, NeRF... | true | true | Wynn, Jamie and Turmukhambetov, Daniyar | 2,023 | null | null | null | null | DiffusioNeRF: Regularizing Neural Radiance Fields with Denoising
Diffusion Models | Regularizing Neural Radiance Fields with Denoising Diffusion Models | https://arxiv.org/abs/2302.12231 | NeRFs learn a scene's color and density fields by minimizing the photometric discrepancy between training views and differentiable renderings of the scene. |
PS4PRO: Pixel-to-pixel Supervision for Photorealistic Rendering and
Optimization | 2505.22616v1 | 3dgsEh | \cite{3dgsEh} | 3DGS-Enhancer: Enhancing Unbounded 3D Gaussian Splatting with View-consistent 2D Diffusion Priors | null | null | true | false | Liu, Xi and Zhou, Chaoyi and Huang, Siyu | 2,024 | null | null | null | arXiv preprint arXiv:2410.16266 | 3DGS-Enhancer: Enhancing Unbounded 3D Gaussian Splatting with View-consistent 2D Diffusion Priors | Enhancing Unbounded 3D Gaussian Splatting with View- ... | https://arxiv.org/abs/2410.16266 | Image 4: arxiv logo>cs> arXiv:2410.16266 **arXiv:2410.16266** (cs) View a PDF of the paper titled 3DGS-Enhancer: Enhancing Unbounded 3D Gaussian Splatting with View-consistent 2D Diffusion Priors, by Xi Liu and 2 other authors View a PDF of the paper titled 3DGS-Enhancer: Enhancing Unbounded 3D Gaussian Splatting wit... |
PS4PRO: Pixel-to-pixel Supervision for Photorealistic Rendering and
Optimization | 2505.22616v1 | yu2024viewcrafter | \cite{yu2024viewcrafter} | ViewCrafter: Taming Video Diffusion Models for High-fidelity Novel View
Synthesis | http://arxiv.org/abs/2409.02048v1 | Despite recent advancements in neural 3D reconstruction, the dependence on
dense multi-view captures restricts their broader applicability. In this work,
we propose \textbf{ViewCrafter}, a novel method for synthesizing high-fidelity
novel views of generic scenes from single or sparse images with the prior of
video diff... | true | true | Yu, Wangbo and Xing, Jinbo and Yuan, Li and Hu, Wenbo and Li, Xiaoyu and Huang, Zhipeng and Gao, Xiangjun and Wong, Tien-Tsin and Shan, Ying and Tian, Yonghong | 2,024 | null | null | null | arXiv preprint arXiv:2409.02048 | ViewCrafter: Taming Video Diffusion Models for High-fidelity Novel View
Synthesis | Taming Video Diffusion Models for High-fidelity Novel View ... | https://github.com/Drexubery/ViewCrafter | ViewCrafter can generate high-fidelity novel views from a single or sparse reference image, while also supporting highly precise pose control. |
Universal Domain Adaptation for Semantic Segmentation | 2505.22458v1 | long2015fully | \cite{long2015fully} | Fully Convolutional Networks for Semantic Segmentation | http://arxiv.org/abs/1411.4038v2 | Convolutional networks are powerful visual models that yield hierarchies of
features. We show that convolutional networks by themselves, trained
end-to-end, pixels-to-pixels, exceed the state-of-the-art in semantic
segmentation. Our key insight is to build "fully convolutional" networks that
take input of arbitrary siz... | true | true | Long, Jonathan and Shelhamer, Evan and Darrell, Trevor | 2,015 | null | null | null | null | Fully Convolutional Networks for Semantic Segmentation | Fully Convolutional Networks for Semantic Segmentation | http://arxiv.org/pdf/1411.4038v2 | Convolutional networks are powerful visual models that yield hierarchies of
features. We show that convolutional networks by themselves, trained
end-to-end, pixels-to-pixels, exceed the state-of-the-art in semantic
segmentation. Our key insight is to build "fully convolutional" networks that
take input of arbitrary siz... |
Universal Domain Adaptation for Semantic Segmentation | 2505.22458v1 | chen2017deeplab | \cite{chen2017deeplab} | DeepLab: Semantic Image Segmentation with Deep Convolutional Nets,
Atrous Convolution, and Fully Connected CRFs | http://arxiv.org/abs/1606.00915v2 | In this work we address the task of semantic image segmentation with Deep
Learning and make three main contributions that are experimentally shown to
have substantial practical merit. First, we highlight convolution with
upsampled filters, or 'atrous convolution', as a powerful tool in dense
prediction tasks. Atrous co... | true | true | Chen, Liang-Chieh and Papandreou, George and Kokkinos, Iasonas and Murphy, Kevin and Yuille, Alan L | 2,017 | null | null | null | IEEE transactions on pattern analysis and machine intelligence | DeepLab: Semantic Image Segmentation with Deep Convolutional Nets,
Atrous Convolution, and Fully Connected CRFs | [PDF] DeepLab: Semantic Image Segmentation with Deep Convolutional ... | http://arxiv.org/pdf/1606.00915 | A deep convolutional neural network (VGG-16 [4] or ResNet-101 [11] in this work) trained in the task of image classification is re-purposed to the task of semantic segmentation by (1) transforming all the fully connected layers to convolutional layers ( i.e ., fully convo-lutional network [14]) and (2) increasing featu... |
Universal Domain Adaptation for Semantic Segmentation | 2505.22458v1 | liu2015parsenet | \cite{liu2015parsenet} | ParseNet: Looking Wider to See Better | http://arxiv.org/abs/1506.04579v2 | We present a technique for adding global context to deep convolutional
networks for semantic segmentation. The approach is simple, using the average
feature for a layer to augment the features at each location. In addition, we
study several idiosyncrasies of training, significantly increasing the
performance of baselin... | true | true | Liu, Wei and Rabinovich, Andrew and Berg, Alexander C | 2,015 | null | null | null | arXiv preprint arXiv:1506.04579 | ParseNet: Looking Wider to See Better | ParseNet: Looking Wider to See Better | http://arxiv.org/pdf/1506.04579v2 | We present a technique for adding global context to deep convolutional
networks for semantic segmentation. The approach is simple, using the average
feature for a layer to augment the features at each location. In addition, we
study several idiosyncrasies of training, significantly increasing the
performance of baselin... |
Universal Domain Adaptation for Semantic Segmentation | 2505.22458v1 | zhao2017pyramid | \cite{zhao2017pyramid} | Pyramid Scene Parsing Network | http://arxiv.org/abs/1612.01105v2 | Scene parsing is challenging for unrestricted open vocabulary and diverse
scenes. In this paper, we exploit the capability of global context information
by different-region-based context aggregation through our pyramid pooling
module together with the proposed pyramid scene parsing network (PSPNet). Our
global prior re... | true | true | Zhao, Hengshuang and Shi, Jianping and Qi, Xiaojuan and Wang, Xiaogang and Jia, Jiaya | 2,017 | null | null | null | null | Pyramid Scene Parsing Network | Pyramid Scene Parsing Network | http://arxiv.org/pdf/1612.01105v2 | Scene parsing is challenging for unrestricted open vocabulary and diverse
scenes. In this paper, we exploit the capability of global context information
by different-region-based context aggregation through our pyramid pooling
module together with the proposed pyramid scene parsing network (PSPNet). Our
global prior re... |
Universal Domain Adaptation for Semantic Segmentation | 2505.22458v1 | zhao2018psanet | \cite{zhao2018psanet} | Psanet: Point-wise spatial attention network for scene parsing | null | null | true | false | Zhao, Hengshuang and Zhang, Yi and Liu, Shu and Shi, Jianping and Loy, Chen Change and Lin, Dahua and Jia, Jiaya | 2,018 | null | null | null | null | Psanet: Point-wise spatial attention network for scene parsing | [PDF] PSANet: Point-wise Spatial Attention Network for Scene Parsing | https://hszhao.github.io/paper/eccv18_psanet.pdf | In this paper, we propose the point-wise spatial attention network (PSANet) to aggregate long-range contextual information in a flexible and adaptive man- ner. |
Universal Domain Adaptation for Semantic Segmentation | 2505.22458v1 | zhu2019asymmetric | \cite{zhu2019asymmetric} | Asymmetric Non-local Neural Networks for Semantic Segmentation | http://arxiv.org/abs/1908.07678v5 | The non-local module works as a particularly useful technique for semantic
segmentation while criticized for its prohibitive computation and GPU memory
occupation. In this paper, we present Asymmetric Non-local Neural Network to
semantic segmentation, which has two prominent components: Asymmetric Pyramid
Non-local Blo... | true | true | Zhu, Zhen and Xu, Mengde and Bai, Song and Huang, Tengteng and Bai, Xiang | 2,019 | null | null | null | null | Asymmetric Non-local Neural Networks for Semantic Segmentation | Asymmetric Non-Local Neural Networks for Semantic ... | https://openaccess.thecvf.com/content_ICCV_2019/papers/Zhu_Asymmetric_Non-Local_Neural_Networks_for_Semantic_Segmentation_ICCV_2019_paper.pdf | In this paper, we present Asymmetric Non-local Neural Network to semantic segmentation, which has two promi-nent components: Asymmetric Pyramid Non-local Block (APNB) and Asymmetric Fusion Non-local Block (AFNB). Motivated by the spatial pyramid pooling [12, 16, 46] strategy, we propose to embed a pyramid sampling modu... |
Universal Domain Adaptation for Semantic Segmentation | 2505.22458v1 | xie2021segformer | \cite{xie2021segformer} | SegFormer: Simple and Efficient Design for Semantic Segmentation with
Transformers | http://arxiv.org/abs/2105.15203v3 | We present SegFormer, a simple, efficient yet powerful semantic segmentation
framework which unifies Transformers with lightweight multilayer perception
(MLP) decoders. SegFormer has two appealing features: 1) SegFormer comprises a
novel hierarchically structured Transformer encoder which outputs multiscale
features. I... | true | true | Xie, Enze and Wang, Wenhai and Yu, Zhiding and Anandkumar, Anima and Alvarez, Jose M and Luo, Ping | 2,021 | null | null | null | Advances in Neural Information Processing Systems | SegFormer: Simple and Efficient Design for Semantic Segmentation with
Transformers | [PDF] SegFormer: Simple and Efficient Design for Semantic Segmentation ... | https://proceedings.neurips.cc/paper/2021/file/64f1f27bf1b4ec22924fd0acb550c235-Paper.pdf | We present SegFormer, a simple, efficient yet powerful semantic segmentation framework which unifies Transformers with lightweight multilayer perceptron. |
Universal Domain Adaptation for Semantic Segmentation | 2505.22458v1 | zheng2021rethinking | \cite{zheng2021rethinking} | Rethinking Semantic Segmentation from a Sequence-to-Sequence Perspective
with Transformers | http://arxiv.org/abs/2012.15840v3 | Most recent semantic segmentation methods adopt a fully-convolutional network
(FCN) with an encoder-decoder architecture. The encoder progressively reduces
the spatial resolution and learns more abstract/semantic visual concepts with
larger receptive fields. Since context modeling is critical for segmentation,
the late... | true | true | Zheng, Sixiao and Lu, Jiachen and Zhao, Hengshuang and Zhu, Xiatian and Luo, Zekun and Wang, Yabiao and Fu, Yanwei and Feng, Jianfeng and Xiang, Tao and Torr, Philip HS and others | 2,021 | null | null | null | null | Rethinking Semantic Segmentation from a Sequence-to-Sequence Perspective
with Transformers | [PDF] Rethinking Semantic Segmentation From a Sequence-to-Sequence ... | https://openaccess.thecvf.com/content/CVPR2021/papers/Zheng_Rethinking_Semantic_Segmentation_From_a_Sequence-to-Sequence_Perspective_With_Transformers_CVPR_2021_paper.pdf | In this paper, we aim to provide an alternative perspective by treating semantic segmenta- tion as a sequence-to-sequence prediction task. Specifically, we |
Universal Domain Adaptation for Semantic Segmentation | 2505.22458v1 | tsai2018learning | \cite{tsai2018learning} | Learning to Adapt Structured Output Space for Semantic Segmentation | http://arxiv.org/abs/1802.10349v3 | Convolutional neural network-based approaches for semantic segmentation rely
on supervision with pixel-level ground truth, but may not generalize well to
unseen image domains. As the labeling process is tedious and labor intensive,
developing algorithms that can adapt source ground truth labels to the target
domain is ... | true | true | Tsai, Yi-Hsuan and Hung, Wei-Chih and Schulter, Samuel and Sohn, Kihyuk and Yang, Ming-Hsuan and Chandraker, Manmohan | 2,018 | null | null | null | null | Learning to Adapt Structured Output Space for Semantic Segmentation | Learning to Adapt Structured Output Space for Semantic Segmentation | http://arxiv.org/pdf/1802.10349v3 | Convolutional neural network-based approaches for semantic segmentation rely
on supervision with pixel-level ground truth, but may not generalize well to
unseen image domains. As the labeling process is tedious and labor intensive,
developing algorithms that can adapt source ground truth labels to the target
domain is ... |
Universal Domain Adaptation for Semantic Segmentation | 2505.22458v1 | hong2018conditional | \cite{hong2018conditional} | Conditional generative adversarial network for structured domain adaptation | null | null | true | false | Hong, Weixiang and Wang, Zhenzhen and Yang, Ming and Yuan, Junsong | 2,018 | null | null | null | null | Conditional generative adversarial network for structured domain adaptation | Conditional Generative Adversarial Network for Structured Domain ... | https://weixianghong.github.io/publications/2018-10-04-CVPR/ | Conditional Generative Adversarial Network for Structured Domain Adaptation. Published in IEEE Conference on Computer Vision and Pattern Recognition (CVPR), |
Universal Domain Adaptation for Semantic Segmentation | 2505.22458v1 | kim2020learning | \cite{kim2020learning} | Learning Texture Invariant Representation for Domain Adaptation of
Semantic Segmentation | http://arxiv.org/abs/2003.00867v2 | Since annotating pixel-level labels for semantic segmentation is laborious,
leveraging synthetic data is an attractive solution. However, due to the domain
gap between synthetic domain and real domain, it is challenging for a model
trained with synthetic data to generalize to real data. In this paper,
considering the f... | true | true | Kim, Myeongjin and Byun, Hyeran | 2,020 | null | null | null | null | Learning Texture Invariant Representation for Domain Adaptation of
Semantic Segmentation | Learning Texture Invariant Representation for Domain ... | https://openaccess.thecvf.com/content_CVPR_2020/papers/Kim_Learning_Texture_Invariant_Representation_for_Domain_Adaptation_of_Semantic_Segmentation_CVPR_2020_paper.pdf | by M Kim · 2020 · Cited by 351 — We design a method to adapt to the target domain's tex- ture for domain adaptation of semantic segmentation, combining pixel-level method and self-training. 2. |
Universal Domain Adaptation for Semantic Segmentation | 2505.22458v1 | pan2020unsupervised | \cite{pan2020unsupervised} | Unsupervised Intra-domain Adaptation for Semantic Segmentation through
Self-Supervision | http://arxiv.org/abs/2004.07703v4 | Convolutional neural network-based approaches have achieved remarkable
progress in semantic segmentation. However, these approaches heavily rely on
annotated data which are labor intensive. To cope with this limitation,
automatically annotated data generated from graphic engines are used to train
segmentation models. H... | true | true | Pan, Fei and Shin, Inkyu and Rameau, Francois and Lee, Seokju and Kweon, In So | 2,020 | null | null | null | null | Unsupervised Intra-domain Adaptation for Semantic Segmentation through
Self-Supervision | [PDF] Unsupervised Intra-Domain Adaptation for Semantic Segmentation ... | https://openaccess.thecvf.com/content_CVPR_2020/papers/Pan_Unsupervised_Intra-Domain_Adaptation_for_Semantic_Segmentation_Through_Self-Supervision_CVPR_2020_paper.pdf | In this work, we propose a two-step self- supervised domain adaptation approach to minimize the inter-domain and intra-domain gap together. First, we con- duct |
Universal Domain Adaptation for Semantic Segmentation | 2505.22458v1 | tsai2019domain | \cite{tsai2019domain} | Domain Adaptation for Structured Output via Discriminative Patch
Representations | http://arxiv.org/abs/1901.05427v4 | Predicting structured outputs such as semantic segmentation relies on
expensive per-pixel annotations to learn supervised models like convolutional
neural networks. However, models trained on one data domain may not generalize
well to other domains without annotations for model finetuning. To avoid the
labor-intensive ... | true | true | Tsai, Yi-Hsuan and Sohn, Kihyuk and Schulter, Samuel and Chandraker, Manmohan | 2,019 | null | null | null | null | Domain Adaptation for Structured Output via Discriminative Patch
Representations | Domain Adaptation for Structured Output via Discriminative ... | https://www.computer.org/csdl/proceedings-article/iccv/2019/480300b456/1hVlpOKL1FC | by YH Tsai · 2019 · Cited by 417 — We propose to learn discriminative feature representations of patches in the source domain by discovering multiple modes of patch-wise output distribution ...See more |
Universal Domain Adaptation for Semantic Segmentation | 2505.22458v1 | chen2019synergistic | \cite{chen2019synergistic} | Synergistic Image and Feature Adaptation: Towards Cross-Modality Domain
Adaptation for Medical Image Segmentation | http://arxiv.org/abs/1901.08211v4 | This paper presents a novel unsupervised domain adaptation framework, called
Synergistic Image and Feature Adaptation (SIFA), to effectively tackle the
problem of domain shift. Domain adaptation has become an important and hot
topic in recent studies on deep learning, aiming to recover performance
degradation when appl... | true | true | Chen, Cheng and Dou, Qi and Chen, Hao and Qin, Jing and Heng, Pheng-Ann | 2,019 | null | null | null | null | Synergistic Image and Feature Adaptation: Towards Cross-Modality Domain
Adaptation for Medical Image Segmentation | Synergistic Image and Feature Adaptation: Towards Cross-Modality ... | https://aaai.org/papers/00865-synergistic-image-and-feature-adaptation-towards-cross-modality-domain-adaptation-for-medical-image-segmentation/ | This paper presents a novel unsupervised domain adaptation framework, called Synergistic Image and Feature Adaptation (SIFA), to effectively |
Universal Domain Adaptation for Semantic Segmentation | 2505.22458v1 | du2019ssf | \cite{du2019ssf} | Ssf-dan: Separated semantic feature based domain adaptation network for semantic segmentation | null | null | true | false | Du, Liang and Tan, Jingang and Yang, Hongye and Feng, Jianfeng and Xue, Xiangyang and Zheng, Qibao and Ye, Xiaoqing and Zhang, Xiaolin | 2,019 | null | null | null | null | Ssf-dan: Separated semantic feature based domain adaptation network for semantic segmentation | ICCV 2019 Open Access Repository | https://openaccess.thecvf.com/content_ICCV_2019/html/Du_SSF-DAN_Separated_Semantic_Feature_Based_Domain_Adaptation_Network_for_Semantic_ICCV_2019_paper.html | by L Du · 2019 · Cited by 213 — In this work, we propose a Separated Semantic Feature based domain adaptation network, named SSF-DAN, for semantic segmentation. First, a Semantic-wise |
Universal Domain Adaptation for Semantic Segmentation | 2505.22458v1 | melas2021pixmatch | \cite{melas2021pixmatch} | PixMatch: Unsupervised Domain Adaptation via Pixelwise Consistency
Training | http://arxiv.org/abs/2105.08128v1 | Unsupervised domain adaptation is a promising technique for semantic
segmentation and other computer vision tasks for which large-scale data
annotation is costly and time-consuming. In semantic segmentation, it is
attractive to train models on annotated images from a simulated (source) domain
and deploy them on real (t... | true | true | Melas-Kyriazi, Luke and Manrai, Arjun K | 2,021 | null | null | null | null | PixMatch: Unsupervised Domain Adaptation via Pixelwise Consistency
Training | Unsupervised Domain Adaptation via Pixelwise Consistency Training | https://arxiv.org/abs/2105.08128 | PixMatch is an unsupervised domain adaptation method using target-domain consistency training, enforcing pixelwise consistency between predictions and |
Universal Domain Adaptation for Semantic Segmentation | 2505.22458v1 | hoyer2022daformer | \cite{hoyer2022daformer} | DAFormer: Improving Network Architectures and Training Strategies for
Domain-Adaptive Semantic Segmentation | http://arxiv.org/abs/2111.14887v2 | As acquiring pixel-wise annotations of real-world images for semantic
segmentation is a costly process, a model can instead be trained with more
accessible synthetic data and adapted to real images without requiring their
annotations. This process is studied in unsupervised domain adaptation (UDA).
Even though a large ... | true | true | Hoyer, Lukas and Dai, Dengxin and Van Gool, Luc | 2,022 | null | null | null | null | DAFormer: Improving Network Architectures and Training Strategies for
Domain-Adaptive Semantic Segmentation | lhoyer/DAFormer: [CVPR22] Official Implementation of ... | https://github.com/lhoyer/DAFormer | DAFormer: Improving Network Architectures and Training Strategies for Domain-Adaptive Semantic Segmentation. by Lukas Hoyer, Dengxin Dai, and Luc Van Gool. |
Universal Domain Adaptation for Semantic Segmentation | 2505.22458v1 | hoyer2022hrda | \cite{hoyer2022hrda} | HRDA: Context-Aware High-Resolution Domain-Adaptive Semantic
Segmentation | http://arxiv.org/abs/2204.13132v2 | Unsupervised domain adaptation (UDA) aims to adapt a model trained on the
source domain (e.g. synthetic data) to the target domain (e.g. real-world data)
without requiring further annotations on the target domain. This work focuses
on UDA for semantic segmentation as real-world pixel-wise annotations are
particularly e... | true | true | Hoyer, Lukas and Dai, Dengxin and Van Gool, Luc | 2,022 | null | null | null | null | HRDA: Context-Aware High-Resolution Domain-Adaptive Semantic
Segmentation | [PDF] HRDA: Context-Aware High-Resolution Domain-Adaptive Semantic ... | https://www.ecva.net/papers/eccv_2022/papers_ECCV/papers/136900370.pdf | HRDA is a multi-resolution training approach for UDA, using high-resolution crops for details and low-resolution for context, with a learned scale attention. |
Universal Domain Adaptation for Semantic Segmentation | 2505.22458v1 | zou2018unsupervised | \cite{zou2018unsupervised} | Unsupervised domain adaptation for semantic segmentation via class-balanced self-training | null | null | true | false | Zou, Yang and Yu, Zhiding and Kumar, BVK and Wang, Jinsong | 2,018 | null | null | null | null | Unsupervised domain adaptation for semantic segmentation via class-balanced self-training | Unsupervised Domain Adaptation for Semantic ... | https://openaccess.thecvf.com/content_ECCV_2018/papers/Yang_Zou_Unsupervised_Domain_Adaptation_ECCV_2018_paper.pdf | by Y Zou · 2018 · Cited by 1832 — A class-balanced self-training (CBST) is introduced to overcome the imbalance issue of transfer- ring difficulty among classes via generating pseudo-labels with |
Universal Domain Adaptation for Semantic Segmentation | 2505.22458v1 | chen2019domain | \cite{chen2019domain} | Domain adaptation for semantic segmentation with maximum squares loss | null | null | true | false | Chen, Minghao and Xue, Hongyang and Cai, Deng | 2,019 | null | null | null | null | Domain adaptation for semantic segmentation with maximum squares loss | Domain Adaptation for Semantic Segmentation with Maximum Squares Loss | http://arxiv.org/pdf/1909.13589v1 | Deep neural networks for semantic segmentation always require a large number
of samples with pixel-level labels, which becomes the major difficulty in their
real-world applications. To reduce the labeling cost, unsupervised domain
adaptation (UDA) approaches are proposed to transfer knowledge from labeled
synthesized d... |
Universal Domain Adaptation for Semantic Segmentation | 2505.22458v1 | zou2019confidence | \cite{zou2019confidence} | Confidence Regularized Self-Training | http://arxiv.org/abs/1908.09822v3 | Recent advances in domain adaptation show that deep self-training presents a
powerful means for unsupervised domain adaptation. These methods often involve
an iterative process of predicting on target domain and then taking the
confident predictions as pseudo-labels for retraining. However, since
pseudo-labels can be n... | true | true | Zou, Yang and Yu, Zhiding and Liu, Xiaofeng and Kumar, BVK and Wang, Jinsong | 2,019 | null | null | null | null | Confidence Regularized Self-Training | [1908.09822] Confidence Regularized Self-Training - arXiv | https://arxiv.org/abs/1908.09822 | We propose a confidence regularized self-training (CRST) framework, formulated as regularized self-training. Our method treats pseudo-labels as continuous |
Universal Domain Adaptation for Semantic Segmentation | 2505.22458v1 | wang2021domain | \cite{wang2021domain} | Domain adaptive semantic segmentation with self-supervised depth estimation | null | null | true | false | Wang, Qin and Dai, Dengxin and Hoyer, Lukas and Van Gool, Luc and Fink, Olga | 2,021 | null | null | null | null | Domain adaptive semantic segmentation with self-supervised depth estimation | [PDF] Domain Adaptive Semantic Segmentation With Self-Supervised ... | https://openaccess.thecvf.com/content/ICCV2021/papers/Wang_Domain_Adaptive_Semantic_Segmentation_With_Self-Supervised_Depth_Estimation_ICCV_2021_paper.pdf | Domain Adaptive Semantic Segmentation with Self-Supervised Depth Estimation Qin Wang1 Dengxin Dai1,2* Lukas Hoyer1 Luc Van Gool1,3 Olga Fink1 1ETH Zurich, Switzerland 2MPI for Informatics, Germany 3KU Lueven, Belgium {qwang,lhoyer,ofink}@ethz.ch {dai,vangool}@vision.ee.ethz.ch Abstract Domain adaptation for semantic se... |
Universal Domain Adaptation for Semantic Segmentation | 2505.22458v1 | lian2019constructing | \cite{lian2019constructing} | Constructing Self-motivated Pyramid Curriculums for Cross-Domain
Semantic Segmentation: A Non-Adversarial Approach | http://arxiv.org/abs/1908.09547v1 | We propose a new approach, called self-motivated pyramid curriculum domain
adaptation (PyCDA), to facilitate the adaptation of semantic segmentation
neural networks from synthetic source domains to real target domains. Our
approach draws on an insight connecting two existing works: curriculum domain
adaptation and self... | true | true | Lian, Qing and Lv, Fengmao and Duan, Lixin and Gong, Boqing | 2,019 | null | null | null | null | Constructing Self-motivated Pyramid Curriculums for Cross-Domain
Semantic Segmentation: A Non-Adversarial Approach | lianqing11/PyCDA - A Non-Adversarial Approach | https://github.com/lianqing11/PyCDA | PyCDA. Code for Constructing Self-motivated Pyramid Curriculums for Cross-Domain Semantic Segmentation: A Non-Adversarial Approach.See more |
Universal Domain Adaptation for Semantic Segmentation | 2505.22458v1 | li2019bidirectional | \cite{li2019bidirectional} | Bidirectional Learning for Domain Adaptation of Semantic Segmentation | http://arxiv.org/abs/1904.10620v1 | Domain adaptation for semantic image segmentation is very necessary since
manually labeling large datasets with pixel-level labels is expensive and time
consuming. Existing domain adaptation techniques either work on limited
datasets, or yield not so good performance compared with supervised learning.
In this paper, we... | true | true | Li, Yunsheng and Yuan, Lu and Vasconcelos, Nuno | 2,019 | null | null | null | null | Bidirectional Learning for Domain Adaptation of Semantic Segmentation | Bidirectional Learning for Domain Adaptation of Semantic Segmentation | http://arxiv.org/pdf/1904.10620v1 | Domain adaptation for semantic image segmentation is very necessary since
manually labeling large datasets with pixel-level labels is expensive and time
consuming. Existing domain adaptation techniques either work on limited
datasets, or yield not so good performance compared with supervised learning.
In this paper, we... |
Universal Domain Adaptation for Semantic Segmentation | 2505.22458v1 | wang2021uncertainty | \cite{wang2021uncertainty} | Uncertainty-aware pseudo label refinery for domain adaptive semantic segmentation | null | null | true | false | Wang, Yuxi and Peng, Junran and Zhang, ZhaoXiang | 2,021 | null | null | null | null | Uncertainty-aware pseudo label refinery for domain adaptive semantic segmentation | [PDF] Uncertainty-Aware Pseudo Label Refinery for Domain Adaptive ... | https://openaccess.thecvf.com/content/ICCV2021/papers/Wang_Uncertainty-Aware_Pseudo_Label_Refinery_for_Domain_Adaptive_Semantic_Segmentation_ICCV_2021_paper.pdf | Domain Adaptation for Semantic Segmentation (DASS) aims to train a network that can assign pixel-level labels to unlabeled target data by learning from labeled |
Universal Domain Adaptation for Semantic Segmentation | 2505.22458v1 | zhang2021prototypical | \cite{zhang2021prototypical} | Prototypical Pseudo Label Denoising and Target Structure Learning for
Domain Adaptive Semantic Segmentation | http://arxiv.org/abs/2101.10979v2 | Self-training is a competitive approach in domain adaptive segmentation,
which trains the network with the pseudo labels on the target domain. However
inevitably, the pseudo labels are noisy and the target features are dispersed
due to the discrepancy between source and target domains. In this paper, we
rely on represe... | true | true | Zhang, Pan and Zhang, Bo and Zhang, Ting and Chen, Dong and Wang, Yong and Wen, Fang | 2,021 | null | null | null | null | Prototypical Pseudo Label Denoising and Target Structure Learning for
Domain Adaptive Semantic Segmentation | Prototypical Pseudo Label Denoising and Target Structure ... | https://openaccess.thecvf.com/content/CVPR2021/papers/Zhang_Prototypical_Pseudo_Label_Denoising_and_Target_Structure_Learning_for_Domain_CVPR_2021_paper.pdf | by P Zhang · 2021 · Cited by 674 — This paper uses prototypes to address noisy pseudo labels in unsupervised domain adaptation, online correcting them and aligning soft assignments for a compact |
Universal Domain Adaptation for Semantic Segmentation | 2505.22458v1 | tranheden2021dacs | \cite{tranheden2021dacs} | DACS: Domain Adaptation via Cross-domain Mixed Sampling | http://arxiv.org/abs/2007.08702v2 | Semantic segmentation models based on convolutional neural networks have
recently displayed remarkable performance for a multitude of applications.
However, these models typically do not generalize well when applied on new
domains, especially when going from synthetic to real data. In this paper we
address the problem ... | true | true | Tranheden, Wilhelm and Olsson, Viktor and Pinto, Juliano and Svensson, Lennart | 2,021 | null | null | null | null | DACS: Domain Adaptation via Cross-domain Mixed Sampling | DACS: Domain Adaptation via Cross-domain Mixed Sampling - arXiv | https://arxiv.org/abs/2007.08702 | We propose DACS: Domain Adaptation via Cross-domain mixed Sampling, which mixes images from the two domains along with the corresponding labels and pseudo- |
Universal Domain Adaptation for Semantic Segmentation | 2505.22458v1 | you2019universal | \cite{you2019universal} | Universal Multi-Source Domain Adaptation | http://arxiv.org/abs/2011.02594v1 | Unsupervised domain adaptation enables intelligent models to transfer
knowledge from a labeled source domain to a similar but unlabeled target
domain. Recent study reveals that knowledge can be transferred from one source
domain to another unknown target domain, called Universal Domain Adaptation
(UDA). However, in the... | true | true | You, Kaichao and Long, Mingsheng and Cao, Zhangjie and Wang, Jianmin and Jordan, Michael I | 2,019 | null | null | null | null | Universal Multi-Source Domain Adaptation | [2011.02594] Universal Multi-Source Domain Adaptation - arXiv | https://arxiv.org/abs/2011.02594 | In this paper, we formally propose a more general domain adaptation setting, universal multi-source domain adaptation (UMDA), where the label sets of multiple |
Universal Domain Adaptation for Semantic Segmentation | 2505.22458v1 | fu2020learning | \cite{fu2020learning} | Learning to detect open classes for universal domain adaptation | null | null | true | false | Fu, Bo and Cao, Zhangjie and Long, Mingsheng and Wang, Jianmin | 2,020 | null | null | null | null | Learning to detect open classes for universal domain adaptation | Learning to Detect Open Classes for Universal Domain ... | https://paperswithcode.com/paper/learning-to-detect-open-classes-for-universal | Universal domain adaptation (UDA) transfers knowledge between domains without any constraint on the label sets, extending the applicability of domain |
Universal Domain Adaptation for Semantic Segmentation | 2505.22458v1 | bucci2020effectiveness | \cite{bucci2020effectiveness} | On the Effectiveness of Image Rotation for Open Set Domain Adaptation | http://arxiv.org/abs/2007.12360v1 | Open Set Domain Adaptation (OSDA) bridges the domain gap between a labeled
source domain and an unlabeled target domain, while also rejecting target
classes that are not present in the source. To avoid negative transfer, OSDA
can be tackled by first separating the known/unknown target samples and then
aligning known ta... | true | true | Bucci, Silvia and Loghmani, Mohammad Reza and Tommasi, Tatiana | 2,020 | null | null | null | null | On the Effectiveness of Image Rotation for Open Set Domain Adaptation | On the Effectiveness of Image Rotation for Open Set Domain Adaptation | http://arxiv.org/pdf/2007.12360v1 | Open Set Domain Adaptation (OSDA) bridges the domain gap between a labeled
source domain and an unlabeled target domain, while also rejecting target
classes that are not present in the source. To avoid negative transfer, OSDA
can be tackled by first separating the known/unknown target samples and then
aligning known ta... |
Universal Domain Adaptation for Semantic Segmentation | 2505.22458v1 | saito2020universal | \cite{saito2020universal} | Universal Domain Adaptation through Self Supervision | http://arxiv.org/abs/2002.07953v3 | Unsupervised domain adaptation methods traditionally assume that all source
categories are present in the target domain. In practice, little may be known
about the category overlap between the two domains. While some methods address
target settings with either partial or open-set categories, they assume that
the partic... | true | true | Saito, Kuniaki and Kim, Donghyun and Sclaroff, Stan and Saenko, Kate | 2,020 | null | null | null | Advances in neural information processing systems | Universal Domain Adaptation through Self Supervision | Universal Domain Adaptation through Self Supervision | http://arxiv.org/pdf/2002.07953v3 | Unsupervised domain adaptation methods traditionally assume that all source
categories are present in the target domain. In practice, little may be known
about the category overlap between the two domains. While some methods address
target settings with either partial or open-set categories, they assume that
the partic... |
Universal Domain Adaptation for Semantic Segmentation | 2505.22458v1 | saito2021ovanet | \cite{saito2021ovanet} | OVANet: One-vs-All Network for Universal Domain Adaptation | http://arxiv.org/abs/2104.03344v4 | Universal Domain Adaptation (UNDA) aims to handle both domain-shift and
category-shift between two datasets, where the main challenge is to transfer
knowledge while rejecting unknown classes which are absent in the labeled
source data but present in the unlabeled target data. Existing methods manually
set a threshold t... | true | true | Saito, Kuniaki and Saenko, Kate | 2,021 | null | null | null | null | OVANet: One-vs-All Network for Universal Domain Adaptation | One-vs-All Network for Universal Domain Adaptation | https://arxiv.org/abs/2104.03344 | by K Saito · 2021 · Cited by 203 — We propose to train a one-vs-all classifier for each class using labeled source data. Then, we adapt the open-set classifier to the target domain by minimizing |
RC-AutoCalib: An End-to-End Radar-Camera Automatic Calibration Network | 2505.22427v1 | sugimoto2004obstacle | \cite{sugimoto2004obstacle} | Obstacle detection using millimeter-wave radar and its visualization on image sequence | null | null | true | false | Sugimoto, Shigeki and Tateda, Hayato and Takahashi, Hidekazu and Okutomi, Masatoshi | 2,004 | null | null | null | null | Obstacle detection using millimeter-wave radar and its visualization on image sequence | Obstacle detection using millimeter-wave radar and its visualization ... | https://ieeexplore.ieee.org/iel5/9258/29387/01334537.pdf | This section presents a calibration result between the sensors along with segmentation and vi- sualization results using real radar/image frame sequences. |
RC-AutoCalib: An End-to-End Radar-Camera Automatic Calibration Network | 2505.22427v1 | wang2011integrating | \cite{wang2011integrating} | Integrating millimeter wave radar with a monocular vision sensor for on-road obstacle detection applications | null | null | true | false | Wang, Tao and Zheng, Nanning and Xin, Jingmin and Ma, Zheng | 2,011 | null | null | null | Sensors | Integrating millimeter wave radar with a monocular vision sensor for on-road obstacle detection applications | Integrating millimeter wave radar with a monocular vision sensor for ... | https://pubmed.ncbi.nlm.nih.gov/22164117/ | This paper presents a systematic scheme for fusing millimeter wave (MMW) radar and a monocular vision sensor for on-road obstacle detection. |
RC-AutoCalib: An End-to-End Radar-Camera Automatic Calibration Network | 2505.22427v1 | kim2014data | \cite{kim2014data} | Data fusion of radar and image measurements for multi-object tracking via Kalman filtering | null | null | true | false | Kim, Du Yong and Jeon, Moongu | 2,014 | null | null | null | Information Sciences | Data fusion of radar and image measurements for multi-object tracking via Kalman filtering | (PDF) Data fusion of radar and image measurements for multi-object ... | https://www.researchgate.net/publication/278072957_Data_fusion_of_radar_and_image_measurements_for_multi-object_tracking_via_Kalman_filtering | Data fusion of radar and image measurements for multi-object tracking via Kalman filtering. September 2014; Information Sciences 278:641-652. |
RC-AutoCalib: An End-to-End Radar-Camera Automatic Calibration Network | 2505.22427v1 | kim2018radar | \cite{kim2018radar} | Radar and vision sensor fusion for object detection in autonomous vehicle surroundings | null | null | true | false | Kim, Jihun and Han, Dong Seog and Senouci, Benaoumeur | 2,018 | null | null | null | null | Radar and vision sensor fusion for object detection in autonomous vehicle surroundings | Radar and Vision Sensor Fusion for Object Detection ... - IEEE Xplore | https://ieeexplore.ieee.org/document/8436959 | Radar and Vision Sensor Fusion for Object Detection in Autonomous Vehicle Surroundings | IEEE Conference Publication | IEEE Xplore * IEEE _Xplore_ Publisher: IEEE Multi-sensor data fusion for advanced driver assistance systems (ADAS) in the automotive industry has received much attention recently due to the emergence... |
RC-AutoCalib: An End-to-End Radar-Camera Automatic Calibration Network | 2505.22427v1 | kim2017comparative | \cite{kim2017comparative} | Comparative analysis of RADAR-IR sensor fusion methods for object detection | null | null | true | false | Kim, Taehwan and Kim, Sungho and Lee, Eunryung and Park, Miryong | 2,017 | null | null | null | null | Comparative analysis of RADAR-IR sensor fusion methods for object detection | Comparative analysis of RADAR-IR sensor fusion methods for ... | https://ieeexplore.ieee.org/document/8204237/ | This paper presents the Radar and IR sensor fusion method for objection detection. The infrared camera parameter calibration with Levenberg-Marquardt (LM) |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.