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Learning Pyramid-structured Long-range Dependencies for 3D Human Pose
Estimation | 2506.02853v1 | fang2018learning | \cite{fang2018learning} | Learning Pose Grammar to Encode Human Body Configuration for 3D Pose
Estimation | http://arxiv.org/abs/1710.06513v6 | In this paper, we propose a pose grammar to tackle the problem of 3D human
pose estimation. Our model directly takes 2D pose as input and learns a
generalized 2D-3D mapping function. The proposed model consists of a base
network which efficiently captures pose-aligned features and a hierarchy of
Bi-directional RNNs (BR... | true | true | Fang, Hao-Shu and Xu, Yuanlu and Wang, Wenguan and Liu, Xiaobai and Zhu, Song-Chun | 2,018 | null | null | null | null | Learning Pose Grammar to Encode Human Body Configuration for 3D Pose
Estimation | [PDF] Learning Pose Grammar to Encode Human Body Configuration for ... | https://cdn.aaai.org/ojs/12270/12270-13-15798-1-2-20201228.pdf | In this paper, we propose a pose grammar to tackle the prob- lem of 3D human pose estimation. Our model directly takes. 2D pose as input and learns a |
Learning Pyramid-structured Long-range Dependencies for 3D Human Pose
Estimation | 2506.02853v1 | he2021db | \cite{he2021db} | {DB-LSTM: Densely-connected Bi-directional LSTM for human action recognition} | null | null | true | false | He, Jun-Yan and Wu, Xiao and Cheng, Zhi-Qi and Yuan, Zhaoquan and Jiang, Yu-Gang | 2,021 | null | null | null | Neurocomputing | {DB-LSTM: Densely-connected Bi-directional LSTM for human action recognition} | Densely-connected Bi-directional LSTM for human action ... | https://www.sciencedirect.com/science/article/pii/S0925231220317859 | To boost the effectiveness and robustness of modeling long-range action recognition, a Densely-connected Bi-directional LSTM (DB-LSTM) network is novelly proposed to model the visual and temporal associations in both forward and backward directions. To overcome the drawbacks of existing methods, a long-range temporal m... |
Learning Pyramid-structured Long-range Dependencies for 3D Human Pose
Estimation | 2506.02853v1 | zeng2021learning | \cite{zeng2021learning} | Learning Skeletal Graph Neural Networks for Hard 3D Pose Estimation | http://arxiv.org/abs/2108.07181v2 | Various deep learning techniques have been proposed to solve the single-view
2D-to-3D pose estimation problem. While the average prediction accuracy has
been improved significantly over the years, the performance on hard poses with
depth ambiguity, self-occlusion, and complex or rare poses is still far from
satisfactor... | true | true | Zeng, Ailing and Sun, Xiao and Yang, Lei and Zhao, Nanxuan and Liu, Minhao and Xu, Qiang | 2,021 | null | null | null | null | Learning Skeletal Graph Neural Networks for Hard 3D Pose Estimation | Learning Skeletal Graph Neural Networks for Hard 3D Pose Estimation | http://arxiv.org/pdf/2108.07181v2 | Various deep learning techniques have been proposed to solve the single-view
2D-to-3D pose estimation problem. While the average prediction accuracy has
been improved significantly over the years, the performance on hard poses with
depth ambiguity, self-occlusion, and complex or rare poses is still far from
satisfactor... |
Learning Pyramid-structured Long-range Dependencies for 3D Human Pose
Estimation | 2506.02853v1 | zhang2023learning | \cite{zhang2023learning} | Learning Enriched Hop-Aware Correlation for Robust {3D} Human Pose Estimation | null | null | true | false | Zhang, Shengping and Wang, Chenyang and Nie, Liqiang and Yao, Hongxun and Huang, Qingming and Tian, Qi | 2,023 | null | null | null | International Journal of Computer Vision | Learning Enriched Hop-Aware Correlation for Robust {3D} Human Pose Estimation | Learning Enriched Hop-Aware Correlation for Robust 3D Human ... | https://link.springer.com/article/10.1007/s11263-023-01770-5 | This paper proposes a parallel hop-aware graph attention network (PHGANet) for 3D human pose estimation, which learns enriched hop-aware correlation of the |
Learning Pyramid-structured Long-range Dependencies for 3D Human Pose
Estimation | 2506.02853v1 | li2022exploiting | \cite{li2022exploiting} | Exploiting Temporal Contexts with Strided Transformer for 3D Human Pose
Estimation | http://arxiv.org/abs/2103.14304v8 | Despite the great progress in 3D human pose estimation from videos, it is
still an open problem to take full advantage of a redundant 2D pose sequence to
learn representative representations for generating one 3D pose. To this end,
we propose an improved Transformer-based architecture, called Strided
Transformer, which... | true | true | Li, Wenhao and Liu, Hong and Ding, Runwei and Liu, Mengyuan and Wang, Pichao and Yang, Wenming | 2,022 | null | null | null | IEEE Transactions on Multimedia | Exploiting Temporal Contexts with Strided Transformer for 3D Human Pose
Estimation | Vegetebird/StridedTransformer-Pose3D | https://github.com/Vegetebird/StridedTransformer-Pose3D | Exploiting Temporal Contexts with Strided Transformer for 3D Human Pose Estimation. This is the official implementation of the approach described in the paper. |
Learning Pyramid-structured Long-range Dependencies for 3D Human Pose
Estimation | 2506.02853v1 | zhang2022mixste | \cite{zhang2022mixste} | MixSTE: Seq2seq Mixed Spatio-Temporal Encoder for 3D Human Pose
Estimation in Video | http://arxiv.org/abs/2203.00859v4 | Recent transformer-based solutions have been introduced to estimate 3D human
pose from 2D keypoint sequence by considering body joints among all frames
globally to learn spatio-temporal correlation. We observe that the motions of
different joints differ significantly. However, the previous methods cannot
efficiently mo... | true | true | Zhang, Jinlu and Tu, Zhigang and Yang, Jianyu and Chen, Yujin and Yuan, Junsong | 2,022 | null | null | null | null | MixSTE: Seq2seq Mixed Spatio-Temporal Encoder for 3D Human Pose
Estimation in Video | MixSTE: Seq2seq Mixed Spatio-Temporal Encoder for 3D Human ... | https://github.com/JinluZhang1126/MixSTE | Official implementation of CVPR 2022 paper(MixSTE: Seq2seq Mixed Spatio-Temporal Encoder for 3D Human Pose Estimation in Video). |
Learning Pyramid-structured Long-range Dependencies for 3D Human Pose
Estimation | 2506.02853v1 | vaswani2017attention | \cite{vaswani2017attention} | Attention Is All You Need | http://arxiv.org/abs/1706.03762v7 | The dominant sequence transduction models are based on complex recurrent or
convolutional neural networks in an encoder-decoder configuration. The best
performing models also connect the encoder and decoder through an attention
mechanism. We propose a new simple network architecture, the Transformer, based
solely on at... | true | true | Vaswani, Ashish and Shazeer, Noam and Parmar, Niki and Uszkoreit, Jakob and Jones, Llion and Gomez, Aidan N and Kaiser, {\L}ukasz and Polosukhin, Illia | 2,017 | null | null | null | Advances in Neural Information Processing Systems | Attention Is All You Need | Attention Is All You Need | http://arxiv.org/pdf/1706.03762v7 | The dominant sequence transduction models are based on complex recurrent or
convolutional neural networks in an encoder-decoder configuration. The best
performing models also connect the encoder and decoder through an attention
mechanism. We propose a new simple network architecture, the Transformer, based
solely on at... |
Learning Pyramid-structured Long-range Dependencies for 3D Human Pose
Estimation | 2506.02853v1 | zhou2019hemlets | \cite{zhou2019hemlets} | HEMlets Pose: Learning Part-Centric Heatmap Triplets for Accurate 3D
Human Pose Estimation | http://arxiv.org/abs/1910.12032v1 | Estimating 3D human pose from a single image is a challenging task. This work
attempts to address the uncertainty of lifting the detected 2D joints to the 3D
space by introducing an intermediate state - Part-Centric Heatmap Triplets
(HEMlets), which shortens the gap between the 2D observation and the 3D
interpretation.... | true | true | Zhou, Kun and Han, Xiaoguang and Jiang, Nianjuan and Jia, Kui and Lu, Jiangbo | 2,019 | null | null | null | null | HEMlets Pose: Learning Part-Centric Heatmap Triplets for Accurate 3D
Human Pose Estimation | redrock303/HEMlets | https://github.com/redrock303/HEMlets | Here we provide our implementation of HEMlets PoSh: Learning Part-Centric Heatmap Triplets for 3D Human Pose and Shape Estimation. |
Learning Pyramid-structured Long-range Dependencies for 3D Human Pose
Estimation | 2506.02853v1 | zeng2020srnet | \cite{zeng2020srnet} | SRNet: Improving Generalization in 3D Human Pose Estimation with a
Split-and-Recombine Approach | http://arxiv.org/abs/2007.09389v1 | Human poses that are rare or unseen in a training set are challenging for a
network to predict. Similar to the long-tailed distribution problem in visual
recognition, the small number of examples for such poses limits the ability of
networks to model them. Interestingly, local pose distributions suffer less
from the lo... | true | true | Zeng, Ailing and Sun, Xiao and Huang, Fuyang and Liu, Minhao and Xu, Qiang and Lin, Stephen | 2,020 | null | null | null | null | SRNet: Improving Generalization in 3D Human Pose Estimation with a
Split-and-Recombine Approach | GitHub - ailingzengzzz/Split-and-Recombine-Net: Code for "SRNet | https://github.com/ailingzengzzz/Split-and-Recombine-Net | This is the original PyTorch implementation of the following work: SRNet: Improving Generalization in 3D Human Pose Estimation with a Split-and-Recombine |
Learning Pyramid-structured Long-range Dependencies for 3D Human Pose
Estimation | 2506.02853v1 | xue2022boosting | \cite{xue2022boosting} | Boosting monocular {3D} human pose estimation with part aware attention | null | null | true | false | Xue, Youze and Chen, Jiansheng and Gu, Xiangming and Ma, Huimin and Ma, Hongbing | 2,022 | null | null | null | IEEE Transactions on Image Processing | Boosting monocular {3D} human pose estimation with part aware attention | Boosting Monocular 3D Human Pose Estimation With Part Aware ... | https://ieeexplore.ieee.org/iel7/83/9626658/09798770.pdf | We thus propose the Part Aware. Dictionary Attention module to calculate the attention for the part-wise features of input in a dictionary, which contains. |
Learning Pyramid-structured Long-range Dependencies for 3D Human Pose
Estimation | 2506.02853v1 | wu2022hpgcn | \cite{wu2022hpgcn} | {HPGCN: Hierarchical poselet-guided graph convolutional network for {3D} pose estimation} | null | null | true | false | Wu, Yongpeng and Kong, Dehui and Wang, Shaofan and Li, Jinghua and Yin, Baocai | 2,022 | null | null | null | Neurocomputing | {HPGCN: Hierarchical poselet-guided graph convolutional network for {3D} pose estimation} | HPGCN: Hierarchical poselet-guided graph convolutional network ... | https://www.sciencedirect.com/science/article/pii/S0925231221016817 | We propose a hierarchical poselet-guided graph convolutional network (HPGCN) for 3D pose estimation from 2D poses. |
Learning Pyramid-structured Long-range Dependencies for 3D Human Pose
Estimation | 2506.02853v1 | xu2021graph | \cite{xu2021graph} | Graph Stacked Hourglass Networks for 3D Human Pose Estimation | http://arxiv.org/abs/2103.16385v1 | In this paper, we propose a novel graph convolutional network architecture,
Graph Stacked Hourglass Networks, for 2D-to-3D human pose estimation tasks. The
proposed architecture consists of repeated encoder-decoder, in which
graph-structured features are processed across three different scales of human
skeletal represe... | true | true | Xu, Tianhan and Takano, Wataru | 2,021 | null | null | null | null | Graph Stacked Hourglass Networks for 3D Human Pose Estimation | Graph Stacked Hourglass Networks for 3D Human Pose Estimation | http://arxiv.org/pdf/2103.16385v1 | In this paper, we propose a novel graph convolutional network architecture,
Graph Stacked Hourglass Networks, for 2D-to-3D human pose estimation tasks. The
proposed architecture consists of repeated encoder-decoder, in which
graph-structured features are processed across three different scales of human
skeletal represe... |
Learning Pyramid-structured Long-range Dependencies for 3D Human Pose
Estimation | 2506.02853v1 | hua2022unet | \cite{hua2022unet} | Weakly-supervised {3D} human pose estimation with cross-view U-shaped graph convolutional network | null | null | true | false | Hua, Guoliang and Liu, Hong and Li, Wenhao and Zhang, Qian and Ding, Runwei and Xu, Xin | 2,022 | null | null | null | IEEE Transactions on Multimedia | Weakly-supervised {3D} human pose estimation with cross-view U-shaped graph convolutional network | Weakly-supervised 3D Human Pose Estimation with Cross-view U ... | https://arxiv.org/abs/2105.10882 | [2105.10882] Weakly-supervised 3D Human Pose Estimation with Cross-view U-shaped Graph Convolutional Network **arXiv:2105.10882** (cs) Title:Weakly-supervised 3D Human Pose Estimation with Cross-view U-shaped Graph Convolutional Network View a PDF of the paper titled Weakly-supervised 3D Human Pose Estimation with Cro... |
Learning Pyramid-structured Long-range Dependencies for 3D Human Pose
Estimation | 2506.02853v1 | wu2022p2t | \cite{wu2022p2t} | P2T: Pyramid Pooling Transformer for Scene Understanding | http://arxiv.org/abs/2106.12011v6 | Recently, the vision transformer has achieved great success by pushing the
state-of-the-art of various vision tasks. One of the most challenging problems
in the vision transformer is that the large sequence length of image tokens
leads to high computational cost (quadratic complexity). A popular solution to
this proble... | true | true | Wu, Yu-Huan and Liu, Yun and Zhan, Xin and Cheng, Ming-Ming | 2,022 | null | null | null | IEEE Transactions on Pattern Analysis and Machine Intelligence | P2T: Pyramid Pooling Transformer for Scene Understanding | P2T: Pyramid Pooling Transformer for Scene Understanding | http://arxiv.org/pdf/2106.12011v6 | Recently, the vision transformer has achieved great success by pushing the
state-of-the-art of various vision tasks. One of the most challenging problems
in the vision transformer is that the large sequence length of image tokens
leads to high computational cost (quadratic complexity). A popular solution to
this proble... |
Learning Pyramid-structured Long-range Dependencies for 3D Human Pose
Estimation | 2506.02853v1 | PVT | \cite{PVT} | Pyramid Vision Transformer: A Versatile Backbone for Dense Prediction
without Convolutions | http://arxiv.org/abs/2102.12122v2 | Although using convolutional neural networks (CNNs) as backbones achieves
great successes in computer vision, this work investigates a simple backbone
network useful for many dense prediction tasks without convolutions. Unlike the
recently-proposed Transformer model (e.g., ViT) that is specially designed for
image clas... | true | true | Wang, Wenhai and Xie, Enze and Li, Xiang and Fan, Deng-Ping and Song, Kaitao and Liang, Ding and Lu, Tong and Luo, Ping and Shao, Ling | 2,021 | null | null | null | null | Pyramid Vision Transformer: A Versatile Backbone for Dense Prediction
without Convolutions | Pyramid Vision Transformer: A Versatile Backbone for Dense ... - arXiv | https://arxiv.org/abs/2102.12122 | Image 4: arxiv logo>cs> arXiv:2102.12122 Authors:Wenhai Wang, Enze Xie, Xiang Li, Deng-Ping Fan, Kaitao Song, Ding Liang, Tong Lu, Ping Luo, Ling Shao View a PDF of the paper titled Pyramid Vision Transformer: A Versatile Backbone for Dense Prediction without Convolutions, by Wenhai Wang and 8 other authors View a PDF... |
Probabilistic Online Event Downsampling | 2506.02547v1 | cohen2018spatial | \cite{cohen2018spatial} | Spatial and temporal downsampling in event-based visual classification | null | null | true | false | Cohen, Gregory and Afshar, Saeed and Orchard, Garrick and Tapson, Jonathan and Benosman, Ryad and van Schaik, Andre | 2,018 | null | null | null | IEEE Transactions on Neural Networks and Learning Systems | Spatial and temporal downsampling in event-based visual classification | Spatial and Temporal Downsampling in Event-Based ... | https://www.researchgate.net/publication/322566649_Spatial_and_Temporal_Downsampling_in_Event-Based_Visual_Classification | The results show that both spatial downsampling and temporal downsampling produce improved classification accuracy and, additionally, a lower overall data rate. |
Probabilistic Online Event Downsampling | 2506.02547v1 | ghoshevdownsampling | \cite{ghoshevdownsampling} | EvDownsampling: a robust method for downsampling event camera data | null | null | true | false | Ghosh, Anindya and Nowotny, Thomas and Knight, James | null | null | null | null | null | EvDownsampling: a robust method for downsampling event camera data | a robust method for downsampling event camera data | https://sussex.figshare.com/articles/conference_contribution/EvDownsampling_a_robust_method_for_downsampling_event_camera_data/26970640 | by A Ghosh · Cited by 1 — We present a bio-inspired spatio-temporal downsampling technique that can downsample event streams by factors of up to 16 times. |
Probabilistic Online Event Downsampling | 2506.02547v1 | barrios2018less | \cite{barrios2018less} | Less data same information for event-based sensors: A bioinspired filtering and data reduction algorithm | null | null | true | false | Barrios-Avil{\'e}s, Juan and Rosado-Mu{\~n}oz, Alfredo and Medus, Leandro D and Bataller-Mompe{\'a}n, Manuel and Guerrero-Mart{\'\i}nez, Juan F | 2,018 | null | null | null | Sensors | Less data same information for event-based sensors: A bioinspired filtering and data reduction algorithm | Less Data Same Information for Event-Based Sensors: A Bioinspired ... | https://pmc.ncbi.nlm.nih.gov/articles/PMC6308842/ | This work proposes a filtering algorithm (LDSI—Less Data Same Information) which reduces the generated data from event-based sensors without loss of relevant |
Probabilistic Online Event Downsampling | 2506.02547v1 | gupta2020implementing | \cite{gupta2020implementing} | Implementing a foveal-pit inspired filter in a Spiking Convolutional
Neural Network: a preliminary study | http://arxiv.org/abs/2105.14326v1 | We have presented a Spiking Convolutional Neural Network (SCNN) that
incorporates retinal foveal-pit inspired Difference of Gaussian filters and
rank-order encoding. The model is trained using a variant of the
backpropagation algorithm adapted to work with spiking neurons, as implemented
in the Nengo library. We have e... | true | true | Gupta, Shriya TP and Bhattacharya, Basabdatta Sen | 2,020 | null | null | null | null | Implementing a foveal-pit inspired filter in a Spiking Convolutional
Neural Network: a preliminary study | (PDF) Implementing a foveal-pit inspired filter in a Spiking ... | https://www.researchgate.net/publication/352016174_Implementing_a_foveal-pit_inspired_filter_in_a_Spiking_Convolutional_Neural_Network_a_preliminary_study | We have presented a Spiking Convolutional Neural Network (SCNN) that incorporates retinal foveal-pit inspired Difference of Gaussian filters and rank-order |
Probabilistic Online Event Downsampling | 2506.02547v1 | Gruel_2023_WACV | \cite{Gruel_2023_WACV} | Performance Comparison of DVS Data Spatial Downscaling Methods Using Spiking Neural Networks | null | null | true | false | Gruel, Am\'elie and Martinet, Jean and Linares-Barranco, Bernab\'e and Serrano-Gotarredona, Teresa | 2,023 | January | null | null | null | Performance Comparison of DVS Data Spatial Downscaling Methods Using Spiking Neural Networks | Performance comparison of DVS data spatial downscaling ... | https://openaccess.thecvf.com/content/WACV2023/supplemental/Gruel_Performance_Comparison_of_WACV_2023_supplemental.pdf | Performance comparison of DVS data spatial downscaling methods using Spiking Neural Networks. Supplementary Material. Amélie Gruel. CNRS, i3S, Université Côte |
Probabilistic Online Event Downsampling | 2506.02547v1 | ghosh2023insect | \cite{ghosh2023insect} | Insect-inspired Spatio-temporal Downsampling of Event-based Input | null | null | true | false | Ghosh, Anindya and Nowotny, Thomas and Knight, James C | 2,023 | null | null | null | null | Insect-inspired Spatio-temporal Downsampling of Event-based Input | Insect-inspired Spatio-temporal Downsampling of Event-based Input | https://dl.acm.org/doi/pdf/10.1145/3589737.3605994 | We show that our downsampled event streams achieve high fidelity with a hypothetical low-resolution event camera and improve classification performance on |
Probabilistic Online Event Downsampling | 2506.02547v1 | rizzo2023neuromorphic | \cite{rizzo2023neuromorphic} | Neuromorphic downsampling of event-based camera output | null | null | true | false | Rizzo, Charles P and Schuman, Catherine D and Plank, James S | 2,023 | null | null | null | null | Neuromorphic downsampling of event-based camera output | Neuromorphic Downsampling of Event-Based Camera Output | https://dl.acm.org/doi/10.1145/3584954.3584962 | We construct multiple neuromorphic networks that downsample the camera data so as to make training more effective. |
Probabilistic Online Event Downsampling | 2506.02547v1 | bisulco2020near | \cite{bisulco2020near} | Near-chip Dynamic Vision Filtering for Low-Bandwidth Pedestrian
Detection | http://arxiv.org/abs/2004.01689v1 | This paper presents a novel end-to-end system for pedestrian detection using
Dynamic Vision Sensors (DVSs). We target applications where multiple sensors
transmit data to a local processing unit, which executes a detection algorithm.
Our system is composed of (i) a near-chip event filter that compresses and
denoises th... | true | true | Bisulco, Anthony and Ojeda, Fernando Cladera and Isler, Volkan and Lee, Daniel Dongyuel | 2,020 | null | null | null | null | Near-chip Dynamic Vision Filtering for Low-Bandwidth Pedestrian
Detection | Near-Chip Dynamic Vision Filtering for Low-Bandwidth ... | https://ieeexplore.ieee.org/document/9155035/ | by A Bisulco · 2020 · Cited by 12 — This paper presents a novel end-to-end system for pedestrian detection using Dynamic Vision Sensors (DVSs). We target applications where multiple sensors |
Probabilistic Online Event Downsampling | 2506.02547v1 | bi2019graph | \cite{bi2019graph} | Graph-Based Object Classification for Neuromorphic Vision Sensing | http://arxiv.org/abs/1908.06648v1 | Neuromorphic vision sensing (NVS)\ devices represent visual information as
sequences of asynchronous discrete events (a.k.a., ``spikes'') in response to
changes in scene reflectance. Unlike conventional active pixel sensing (APS),
NVS allows for significantly higher event sampling rates at substantially
increased energ... | true | true | Bi, Yin and Chadha, Aaron and Abbas, Alhabib and Bourtsoulatze, Eirina and Andreopoulos, Yiannis | 2,019 | null | null | null | null | Graph-Based Object Classification for Neuromorphic Vision Sensing | Graph-based Object Classification for Neuromorphic Vision Sensing | https://github.com/PIX2NVS/NVS2Graph | Our goal is to represent the stream of spike events from neuromorphic vision sensors as a graph and perform convolution on the graph for object classification. |
Probabilistic Online Event Downsampling | 2506.02547v1 | gruel2023frugal | \cite{gruel2023frugal} | Frugal event data: how small is too small? A human performance assessment with shrinking data | null | null | true | false | Gruel, Am{\'e}lie and Carreras, Luc{\'\i}a Trillo and Garc{\'\i}a, Marina Bueno and Kupczyk, Ewa and Martinet, Jean | 2,023 | null | null | null | null | Frugal event data: how small is too small? A human performance assessment with shrinking data | Frugal event data: how small is too small? A human performance ... | https://ieeexplore.ieee.org/document/10208584/ | Frugal event data: how small is too small? A human performance assessment with shrinking data. Abstract: When designing embedded computer vision systems with |
Probabilistic Online Event Downsampling | 2506.02547v1 | araghi2024pushing | \cite{araghi2024pushing} | Pushing the boundaries of event subsampling in event-based video
classification using CNNs | http://arxiv.org/abs/2409.08953v1 | Event cameras offer low-power visual sensing capabilities ideal for
edge-device applications. However, their high event rate, driven by high
temporal details, can be restrictive in terms of bandwidth and computational
resources. In edge AI applications, determining the minimum amount of events
for specific tasks can al... | true | true | Araghi, Hesam and van Gemert, Jan and Tomen, Nergis | 2,024 | null | null | null | arXiv preprint arXiv:2409.08953 | Pushing the boundaries of event subsampling in event-based video
classification using CNNs | Pushing the Boundaries of Event Subsampling in Event-Based ... | https://link.springer.com/chapter/10.1007/978-3-031-92460-6_17 | In this paper, we study the effect of event subsampling on the accuracy of event data classification using convolutional neural network (CNN) models. |
Aligned Contrastive Loss for Long-Tailed Recognition | 2506.01071v1 | buda2018systematic | \cite{buda2018systematic} | A systematic study of the class imbalance problem in convolutional
neural networks | http://arxiv.org/abs/1710.05381v2 | In this study, we systematically investigate the impact of class imbalance on
classification performance of convolutional neural networks (CNNs) and compare
frequently used methods to address the issue. Class imbalance is a common
problem that has been comprehensively studied in classical machine learning,
yet very lim... | true | true | Buda, Mateusz and Maki, Atsuto and Mazurowski, Maciej A | 2,018 | null | null | null | Neural networks | A systematic study of the class imbalance problem in convolutional
neural networks | A systematic study of the class imbalance problem in convolutional ... | https://arxiv.org/abs/1710.05381 | In this study, we systematically investigate the impact of class imbalance on classification performance of convolutional neural networks (CNNs) |
Aligned Contrastive Loss for Long-Tailed Recognition | 2506.01071v1 | byrd2019effect | \cite{byrd2019effect} | What is the Effect of Importance Weighting in Deep Learning? | http://arxiv.org/abs/1812.03372v3 | Importance-weighted risk minimization is a key ingredient in many machine
learning algorithms for causal inference, domain adaptation, class imbalance,
and off-policy reinforcement learning. While the effect of importance weighting
is well-characterized for low-capacity misspecified models, little is known
about how it... | true | true | Byrd, Jonathon and Lipton, Zachary | 2,019 | null | null | null | null | What is the Effect of Importance Weighting in Deep Learning? | What is the Effect of Importance Weighting in Deep Learning? | http://arxiv.org/pdf/1812.03372v3 | Importance-weighted risk minimization is a key ingredient in many machine
learning algorithms for causal inference, domain adaptation, class imbalance,
and off-policy reinforcement learning. While the effect of importance weighting
is well-characterized for low-capacity misspecified models, little is known
about how it... |
Aligned Contrastive Loss for Long-Tailed Recognition | 2506.01071v1 | drummond2003c4 | \cite{drummond2003c4} | C4. 5, class imbalance, and cost sensitivity: why under-sampling beats over-sampling | null | null | true | false | Drummond, Chris and Holte, Robert C and others | 2,003 | null | null | null | null | C4. 5, class imbalance, and cost sensitivity: why under-sampling beats over-sampling | [PDF] C4.5, Class Imbalance, and Cost Sensitivity: Why Under-Sampling ... | https://citeseerx.ist.psu.edu/document?repid=rep1&type=pdf&doi=4a57c0ffeec2665caf8e11574ce5a9618304b979 | This paper shows that using C4. 5 with under- sampling establishes a reasonable standard for algorithmic comparison. |
Aligned Contrastive Loss for Long-Tailed Recognition | 2506.01071v1 | pouyanfar2018dynamic | \cite{pouyanfar2018dynamic} | Dynamic sampling in convolutional neural networks for imbalanced data classification | null | null | true | false | Pouyanfar, Samira and Tao, Yudong and Mohan, Anup and Tian, Haiman and Kaseb, Ahmed S and Gauen, Kent and Dailey, Ryan and Aghajanzadeh, Sarah and Lu, Yung-Hsiang and Chen, Shu-Ching and others | 2,018 | null | null | null | null | Dynamic sampling in convolutional neural networks for imbalanced data classification | Dynamic Sampling in Convolutional Neural Networks ... - IEEE Xplore | https://ieeexplore.ieee.org/document/8396983 | Dynamic Sampling in Convolutional Neural Networks for Imbalanced Data Classification | IEEE Conference Publication | IEEE Xplore * Download References) This paper presents a novel model based on the Convolutional Neural Networks (CNNs) to handle such imbalanced and heterogeneous data and successfully identifies the sem... |
Aligned Contrastive Loss for Long-Tailed Recognition | 2506.01071v1 | shen2016relay | \cite{shen2016relay} | Relay backpropagation for effective learning of deep convolutional neural networks | null | null | true | false | Shen, Li and Lin, Zhouchen and Huang, Qingming | 2,016 | null | null | null | null | Relay backpropagation for effective learning of deep convolutional neural networks | Relay Backpropagation for Effective Learning of Deep Convolutional Neural Networks | http://arxiv.org/pdf/1512.05830v2 | Learning deeper convolutional neural networks becomes a tendency in recent
years. However, many empirical evidences suggest that performance improvement
cannot be gained by simply stacking more layers. In this paper, we consider the
issue from an information theoretical perspective, and propose a novel method
Relay Bac... |
Aligned Contrastive Loss for Long-Tailed Recognition | 2506.01071v1 | cui2019class | \cite{cui2019class} | Class-Balanced Loss Based on Effective Number of Samples | http://arxiv.org/abs/1901.05555v1 | With the rapid increase of large-scale, real-world datasets, it becomes
critical to address the problem of long-tailed data distribution (i.e., a few
classes account for most of the data, while most classes are
under-represented). Existing solutions typically adopt class re-balancing
strategies such as re-sampling and ... | true | true | Cui, Yin and Jia, Menglin and Lin, Tsung-Yi and Song, Yang and Belongie, Serge | 2,019 | null | null | null | null | Class-Balanced Loss Based on Effective Number of Samples | [PDF] Class-Balanced Loss Based on Effective Number of Samples | https://openaccess.thecvf.com/content_CVPR_2019/papers/Cui_Class-Balanced_Loss_Based_on_Effective_Number_of_Samples_CVPR_2019_paper.pdf | Class-balanced loss uses the effective number of samples, calculated by (1-βn)/(1-β), to re-weight loss, addressing long-tailed data distribution. |
Aligned Contrastive Loss for Long-Tailed Recognition | 2506.01071v1 | huang2016learning | \cite{huang2016learning} | Learning deep representation for imbalanced classification | null | null | true | false | Huang, Chen and Li, Yining and Loy, Chen Change and Tang, Xiaoou | 2,016 | null | null | null | null | Learning deep representation for imbalanced classification | [PDF] Learning Deep Representation for Imbalanced Classification | https://openaccess.thecvf.com/content_cvpr_2016/papers/Huang_Learning_Deep_Representation_CVPR_2016_paper.pdf | In this paper, we conduct extensive and systematic experiments to validate the effectiveness of these classic schemes for representa- tion learning on class- |
Aligned Contrastive Loss for Long-Tailed Recognition | 2506.01071v1 | wang2017learning | \cite{wang2017learning} | Learning to model the tail | null | null | true | false | Wang, Yu-Xiong and Ramanan, Deva and Hebert, Martial | 2,017 | null | null | null | Advances in neural information processing systems | Learning to model the tail | Learning to Model the Tail | https://meta-learn.github.io/2017/papers/metalearn17_wang.pdf | by YX Wang · Cited by 850 — We describe an approach to learning from long-tailed, imbalanced datasets that are prevalent in real-world settings. Here, the challenge is to learn |
Aligned Contrastive Loss for Long-Tailed Recognition | 2506.01071v1 | khan2017cost | \cite{khan2017cost} | Cost Sensitive Learning of Deep Feature Representations from Imbalanced
Data | http://arxiv.org/abs/1508.03422v3 | Class imbalance is a common problem in the case of real-world object
detection and classification tasks. Data of some classes is abundant making
them an over-represented majority, and data of other classes is scarce, making
them an under-represented minority. This imbalance makes it challenging for a
classifier to appr... | true | true | Khan, Salman H and Hayat, Munawar and Bennamoun, Mohammed and Sohel, Ferdous A and Togneri, Roberto | 2,017 | null | null | null | IEEE transactions on neural networks and learning systems | Cost Sensitive Learning of Deep Feature Representations from Imbalanced
Data | Cost Sensitive Learning of Deep Feature Representations from ... | https://arxiv.org/abs/1508.03422 | In this work, we propose a cost sensitive deep neural network which can automatically learn robust feature representations for both the majority and minority |
Aligned Contrastive Loss for Long-Tailed Recognition | 2506.01071v1 | shu2019meta | \cite{shu2019meta} | Meta-weight-net: Learning an explicit mapping for sample weighting | null | null | true | false | Shu, Jun and Xie, Qi and Yi, Lixuan and Zhao, Qian and Zhou, Sanping and Xu, Zongben and Meng, Deyu | 2,019 | null | null | null | Advances in neural information processing systems | Meta-weight-net: Learning an explicit mapping for sample weighting | xjtushujun/meta-weight-net | https://github.com/xjtushujun/meta-weight-net | NeurIPS'19: Meta-Weight-Net: Learning an Explicit Mapping For Sample Weighting (Official Pytorch implementation for noisy labels).See more |
Aligned Contrastive Loss for Long-Tailed Recognition | 2506.01071v1 | kang2019decoupling | \cite{kang2019decoupling} | Decoupling Representation and Classifier for Long-Tailed Recognition | http://arxiv.org/abs/1910.09217v2 | The long-tail distribution of the visual world poses great challenges for
deep learning based classification models on how to handle the class imbalance
problem. Existing solutions usually involve class-balancing strategies, e.g.,
by loss re-weighting, data re-sampling, or transfer learning from head- to
tail-classes, ... | true | true | Kang, Bingyi and Xie, Saining and Rohrbach, Marcus and Yan, Zhicheng and Gordo, Albert and Feng, Jiashi and Kalantidis, Yannis | 2,019 | null | null | null | arXiv preprint arXiv:1910.09217 | Decoupling Representation and Classifier for Long-Tailed Recognition | [PDF] DECOUPLING REPRESENTATION AND CLASSIFIER - OpenReview | https://openreview.net/pdf?id=r1gRTCVFvB | We evaluate the performance of various sampling and classifier training strategies for long-tailed recognition under both joint and decoupled learning schemes. |
Aligned Contrastive Loss for Long-Tailed Recognition | 2506.01071v1 | zhou2020bbn | \cite{zhou2020bbn} | BBN: Bilateral-Branch Network with Cumulative Learning for Long-Tailed
Visual Recognition | http://arxiv.org/abs/1912.02413v4 | Our work focuses on tackling the challenging but natural visual recognition
task of long-tailed data distribution (i.e., a few classes occupy most of the
data, while most classes have rarely few samples). In the literature, class
re-balancing strategies (e.g., re-weighting and re-sampling) are the prominent
and effecti... | true | true | Zhou, Boyan and Cui, Quan and Wei, Xiu-Shen and Chen, Zhao-Min | 2,020 | null | null | null | null | BBN: Bilateral-Branch Network with Cumulative Learning for Long-Tailed
Visual Recognition | [PDF] BBN: Bilateral-Branch Network With Cumulative Learning for Long ... | https://openaccess.thecvf.com/content_CVPR_2020/papers/Zhou_BBN_Bilateral-Branch_Network_With_Cumulative_Learning_for_Long-Tailed_Visual_Recognition_CVPR_2020_paper.pdf | Our work focuses on tackling the challenging but natu- ral visual recognition task of long-tailed data distribution. (i.e., a few classes occupy most of the |
Aligned Contrastive Loss for Long-Tailed Recognition | 2506.01071v1 | zhang2023deep | \cite{zhang2023deep} | Deep Long-Tailed Learning: A Survey | http://arxiv.org/abs/2110.04596v2 | Deep long-tailed learning, one of the most challenging problems in visual
recognition, aims to train well-performing deep models from a large number of
images that follow a long-tailed class distribution. In the last decade, deep
learning has emerged as a powerful recognition model for learning high-quality
image repre... | true | true | Zhang, Yifan and Kang, Bingyi and Hooi, Bryan and Yan, Shuicheng and Feng, Jiashi | 2,023 | null | null | null | IEEE Transactions on Pattern Analysis and Machine Intelligence | Deep Long-Tailed Learning: A Survey | [2110.04596] Deep Long-Tailed Learning: A Survey | https://arxiv.org/abs/2110.04596 | by Y Zhang · 2021 · Cited by 819 — This paper aims to provide a comprehensive survey on recent advances in deep long-tailed learning. |
Aligned Contrastive Loss for Long-Tailed Recognition | 2506.01071v1 | nam2023decoupled | \cite{nam2023decoupled} | Decoupled Training for Long-Tailed Classification With Stochastic
Representations | http://arxiv.org/abs/2304.09426v1 | Decoupling representation learning and classifier learning has been shown to
be effective in classification with long-tailed data. There are two main
ingredients in constructing a decoupled learning scheme; 1) how to train the
feature extractor for representation learning so that it provides generalizable
representatio... | true | true | Nam, Giung and Jang, Sunguk and Lee, Juho | 2,023 | null | null | null | arXiv preprint arXiv:2304.09426 | Decoupled Training for Long-Tailed Classification With Stochastic
Representations | Decoupled Training for Long-Tailed Classification With ... | https://arxiv.org/abs/2304.09426 | by G Nam · 2023 · Cited by 20 — Abstract:Decoupling representation learning and classifier learning has been shown to be effective in classification with long-tailed data. |
Aligned Contrastive Loss for Long-Tailed Recognition | 2506.01071v1 | ren2020balanced | \cite{ren2020balanced} | Balanced meta-softmax for long-tailed visual recognition | null | null | true | false | Ren, Jiawei and Yu, Cunjun and Ma, Xiao and Zhao, Haiyu and Yi, Shuai and others | 2,020 | null | null | null | Advances in neural information processing systems | Balanced meta-softmax for long-tailed visual recognition | Balanced meta-softmax for long-tailed visual recognition | https://dl.acm.org/doi/10.5555/3495724.3496075 | In our experiments, we demonstrate that Balanced Meta-Softmax outperforms state-of-the-art long-tailed classification solutions on both visual recognition and |
Aligned Contrastive Loss for Long-Tailed Recognition | 2506.01071v1 | menon2020long | \cite{menon2020long} | Long-tail learning via logit adjustment | http://arxiv.org/abs/2007.07314v2 | Real-world classification problems typically exhibit an imbalanced or
long-tailed label distribution, wherein many labels are associated with only a
few samples. This poses a challenge for generalisation on such labels, and also
makes na\"ive learning biased towards dominant labels. In this paper, we
present two simple... | true | true | Menon, Aditya Krishna and Jayasumana, Sadeep and Rawat, Ankit Singh and Jain, Himanshu and Veit, Andreas and Kumar, Sanjiv | 2,020 | null | null | null | arXiv preprint arXiv:2007.07314 | Long-tail learning via logit adjustment | Long-tail learning via logit adjustment - OpenReview | https://openreview.net/forum?id=37nvvqkCo5 | This paper provides a statistical framework for long-tail learning by revisiting the idea of logit adjustment based on the label frequencies. |
Aligned Contrastive Loss for Long-Tailed Recognition | 2506.01071v1 | cui2021parametric | \cite{cui2021parametric} | Parametric Contrastive Learning | http://arxiv.org/abs/2107.12028v2 | In this paper, we propose Parametric Contrastive Learning (PaCo) to tackle
long-tailed recognition. Based on theoretical analysis, we observe supervised
contrastive loss tends to bias on high-frequency classes and thus increases the
difficulty of imbalanced learning. We introduce a set of parametric class-wise
learnabl... | true | true | Cui, Jiequan and Zhong, Zhisheng and Liu, Shu and Yu, Bei and Jia, Jiaya | 2,021 | null | null | null | null | Parametric Contrastive Learning | Parametric Contrastive Learning | http://arxiv.org/pdf/2107.12028v2 | In this paper, we propose Parametric Contrastive Learning (PaCo) to tackle
long-tailed recognition. Based on theoretical analysis, we observe supervised
contrastive loss tends to bias on high-frequency classes and thus increases the
difficulty of imbalanced learning. We introduce a set of parametric class-wise
learnabl... |
Aligned Contrastive Loss for Long-Tailed Recognition | 2506.01071v1 | cui2023generalized | \cite{cui2023generalized} | Generalized Parametric Contrastive Learning | http://arxiv.org/abs/2209.12400v2 | In this paper, we propose the Generalized Parametric Contrastive Learning
(GPaCo/PaCo) which works well on both imbalanced and balanced data. Based on
theoretical analysis, we observe that supervised contrastive loss tends to bias
high-frequency classes and thus increases the difficulty of imbalanced
learning. We intro... | true | true | Cui, Jiequan and Zhong, Zhisheng and Tian, Zhuotao and Liu, Shu and Yu, Bei and Jia, Jiaya | 2,023 | null | null | null | IEEE Transactions on Pattern Analysis and Machine Intelligence | Generalized Parametric Contrastive Learning | Generalized Parametric Contrastive Learning | http://arxiv.org/pdf/2209.12400v2 | In this paper, we propose the Generalized Parametric Contrastive Learning
(GPaCo/PaCo) which works well on both imbalanced and balanced data. Based on
theoretical analysis, we observe that supervised contrastive loss tends to bias
high-frequency classes and thus increases the difficulty of imbalanced
learning. We intro... |
Aligned Contrastive Loss for Long-Tailed Recognition | 2506.01071v1 | zhu2022balanced | \cite{zhu2022balanced} | Balanced Contrastive Learning for Long-Tailed Visual Recognition | http://arxiv.org/abs/2207.09052v3 | Real-world data typically follow a long-tailed distribution, where a few
majority categories occupy most of the data while most minority categories
contain a limited number of samples. Classification models minimizing
cross-entropy struggle to represent and classify the tail classes. Although the
problem of learning un... | true | true | Zhu, Jianggang and Wang, Zheng and Chen, Jingjing and Chen, Yi-Ping Phoebe and Jiang, Yu-Gang | 2,022 | null | null | null | null | Balanced Contrastive Learning for Long-Tailed Visual Recognition | Balanced Contrastive Learning for Long-Tailed Visual Recognition | https://arxiv.org/abs/2207.09052 | The proposed balanced contrastive learning (BCL) method satisfies the condition of forming a regular simplex and assists the optimization of cross-entropy. |
Aligned Contrastive Loss for Long-Tailed Recognition | 2506.01071v1 | suh2023long | \cite{suh2023long} | Long-Tailed Recognition by Mutual Information Maximization between
Latent Features and Ground-Truth Labels | http://arxiv.org/abs/2305.01160v3 | Although contrastive learning methods have shown prevailing performance on a
variety of representation learning tasks, they encounter difficulty when the
training dataset is long-tailed. Many researchers have combined contrastive
learning and a logit adjustment technique to address this problem, but the
combinations ar... | true | true | Suh, Min-Kook and Seo, Seung-Woo | 2,023 | null | null | null | arXiv preprint arXiv:2305.01160 | Long-Tailed Recognition by Mutual Information Maximization between
Latent Features and Ground-Truth Labels | Long-Tailed Recognition by Mutual Information ... | https://openreview.net/pdf?id=KqNX6VOqnJ | by MK Suh · Cited by 27 — Rather, we interpret the long-tailed recognition task as a mutual information maximization between latent features and ground-truth labels. This approach. |
Aligned Contrastive Loss for Long-Tailed Recognition | 2506.01071v1 | zhu2024generalized | \cite{zhu2024generalized} | Generalized Logit Adjustment: Calibrating Fine-tuned Models by Removing
Label Bias in Foundation Models | http://arxiv.org/abs/2310.08106v3 | Foundation models like CLIP allow zero-shot transfer on various tasks without
additional training data. Yet, the zero-shot performance is less competitive
than a fully supervised one. Thus, to enhance the performance, fine-tuning and
ensembling are also commonly adopted to better fit the downstream tasks.
However, we a... | true | true | Zhu, Beier and Tang, Kaihua and Sun, Qianru and Zhang, Hanwang | 2,024 | null | null | null | Advances in Neural Information Processing Systems | Generalized Logit Adjustment: Calibrating Fine-tuned Models by Removing
Label Bias in Foundation Models | Calibrating Fine-tuned Models by Removing Label Bias in ... - arXiv | https://arxiv.org/abs/2310.08106 | Missing: 04/08/2025 |
Aligned Contrastive Loss for Long-Tailed Recognition | 2506.01071v1 | he2020momentum | \cite{he2020momentum} | Momentum Contrast for Unsupervised Visual Representation Learning | http://arxiv.org/abs/1911.05722v3 | We present Momentum Contrast (MoCo) for unsupervised visual representation
learning. From a perspective on contrastive learning as dictionary look-up, we
build a dynamic dictionary with a queue and a moving-averaged encoder. This
enables building a large and consistent dictionary on-the-fly that facilitates
contrastive... | true | true | He, Kaiming and Fan, Haoqi and Wu, Yuxin and Xie, Saining and Girshick, Ross | 2,020 | null | null | null | null | Momentum Contrast for Unsupervised Visual Representation Learning | Momentum Contrast for Unsupervised Visual Representation Learning | http://arxiv.org/pdf/1911.05722v3 | We present Momentum Contrast (MoCo) for unsupervised visual representation
learning. From a perspective on contrastive learning as dictionary look-up, we
build a dynamic dictionary with a queue and a moving-averaged encoder. This
enables building a large and consistent dictionary on-the-fly that facilitates
contrastive... |
Aligned Contrastive Loss for Long-Tailed Recognition | 2506.01071v1 | chen2020simple | \cite{chen2020simple} | A Simple Framework for Contrastive Learning of Visual Representations | http://arxiv.org/abs/2002.05709v3 | This paper presents SimCLR: a simple framework for contrastive learning of
visual representations. We simplify recently proposed contrastive
self-supervised learning algorithms without requiring specialized architectures
or a memory bank. In order to understand what enables the contrastive
prediction tasks to learn use... | true | true | Chen, Ting and Kornblith, Simon and Norouzi, Mohammad and Hinton, Geoffrey | 2,020 | null | null | null | null | A Simple Framework for Contrastive Learning of Visual Representations | A Simple Framework for Contrastive Learning of Visual Representations | http://arxiv.org/pdf/2002.05709v3 | This paper presents SimCLR: a simple framework for contrastive learning of
visual representations. We simplify recently proposed contrastive
self-supervised learning algorithms without requiring specialized architectures
or a memory bank. In order to understand what enables the contrastive
prediction tasks to learn use... |
Aligned Contrastive Loss for Long-Tailed Recognition | 2506.01071v1 | grill2020bootstrap | \cite{grill2020bootstrap} | Bootstrap your own latent: A new approach to self-supervised Learning | http://arxiv.org/abs/2006.07733v3 | We introduce Bootstrap Your Own Latent (BYOL), a new approach to
self-supervised image representation learning. BYOL relies on two neural
networks, referred to as online and target networks, that interact and learn
from each other. From an augmented view of an image, we train the online
network to predict the target ne... | true | true | Grill, Jean-Bastien and Strub, Florian and Altch{\'e}, Florent and Tallec, Corentin and Richemond, Pierre and Buchatskaya, Elena and Doersch, Carl and Avila Pires, Bernardo and Guo, Zhaohan and Gheshlaghi Azar, Mohammad and others | 2,020 | null | null | null | Advances in neural information processing systems | Bootstrap your own latent: A new approach to self-supervised Learning | [PDF] Bootstrap Your Own Latent A New Approach to Self-Supervised ... | https://papers.nips.cc/paper/2020/file/f3ada80d5c4ee70142b17b8192b2958e-Paper.pdf | Richemond∗ ,1,2 Elena Buchatskaya1 , Carl Doersch1 , Bernardo Avila Pires1 , Zhaohan Daniel Guo1 Mohammad Gheshlaghi Azar1, Bilal Piot1, Koray Kavukcuoglu1 , Rémi Munos1 , Michal Valko1 1DeepMind 2Imperial College [jbgrill,fstrub,altche,corentint,richemond]@google.com Abstract We introduce Bootstrap Your Own Latent (BY... |
Aligned Contrastive Loss for Long-Tailed Recognition | 2506.01071v1 | chen2021exploring | \cite{chen2021exploring} | Exploring Simple Siamese Representation Learning | http://arxiv.org/abs/2011.10566v1 | Siamese networks have become a common structure in various recent models for
unsupervised visual representation learning. These models maximize the
similarity between two augmentations of one image, subject to certain
conditions for avoiding collapsing solutions. In this paper, we report
surprising empirical results th... | true | true | Chen, Xinlei and He, Kaiming | 2,021 | null | null | null | null | Exploring Simple Siamese Representation Learning | Exploring Simple Siamese Representation Learning | http://arxiv.org/pdf/2011.10566v1 | Siamese networks have become a common structure in various recent models for
unsupervised visual representation learning. These models maximize the
similarity between two augmentations of one image, subject to certain
conditions for avoiding collapsing solutions. In this paper, we report
surprising empirical results th... |
Aligned Contrastive Loss for Long-Tailed Recognition | 2506.01071v1 | khosla2020supervised | \cite{khosla2020supervised} | Supervised Contrastive Learning | http://arxiv.org/abs/2004.11362v5 | Contrastive learning applied to self-supervised representation learning has
seen a resurgence in recent years, leading to state of the art performance in
the unsupervised training of deep image models. Modern batch contrastive
approaches subsume or significantly outperform traditional contrastive losses
such as triplet... | true | true | Khosla, Prannay and Teterwak, Piotr and Wang, Chen and Sarna, Aaron and Tian, Yonglong and Isola, Phillip and Maschinot, Aaron and Liu, Ce and Krishnan, Dilip | 2,020 | null | null | null | Advances in neural information processing systems | Supervised Contrastive Learning | [PDF] Supervised Contrastive Learning | https://proceedings.neurips.cc/paper/2020/file/d89a66c7c80a29b1bdbab0f2a1a94af8-Paper.pdf | Supervised contrastive learning uses label information to pull together samples of the same class, unlike self-supervised which uses data augmentations. |
Aligned Contrastive Loss for Long-Tailed Recognition | 2506.01071v1 | du2024probabilistic | \cite{du2024probabilistic} | Probabilistic Contrastive Learning for Long-Tailed Visual Recognition | http://arxiv.org/abs/2403.06726v2 | Long-tailed distributions frequently emerge in real-world data, where a large
number of minority categories contain a limited number of samples. Such
imbalance issue considerably impairs the performance of standard supervised
learning algorithms, which are mainly designed for balanced training sets.
Recent investigatio... | true | true | Du, Chaoqun and Wang, Yulin and Song, Shiji and Huang, Gao | 2,024 | null | null | null | IEEE Transactions on Pattern Analysis and Machine Intelligence | Probabilistic Contrastive Learning for Long-Tailed Visual Recognition | LeapLabTHU/ProCo: [TPAMI 2024] Probabilistic ... | https://github.com/LeapLabTHU/ProCo | GitHub - LeapLabTHU/ProCo: [TPAMI 2024] Probabilistic Contrastive Learning for Long-Tailed Visual Recognition * GitHub Advanced Security Enterprise-grade security features Search code, repositories, users, issues, pull requests... Reload to refresh your session.You signed out in another tab or window. ProCo This repo... |
Aligned Contrastive Loss for Long-Tailed Recognition | 2506.01071v1 | zhang2022fairness | \cite{zhang2022fairness} | Fairness-aware contrastive learning with partially annotated sensitive attributes | null | null | true | false | Zhang, Fengda and Kuang, Kun and Chen, Long and Liu, Yuxuan and Wu, Chao and Xiao, Jun | 2,022 | null | null | null | null | Fairness-aware contrastive learning with partially annotated sensitive attributes | Fairness-aware Contrastive Learning with Partially Annotated... | https://openreview.net/forum?id=woa783QMul | The paper proposes a variation of contrastive learning that learns fair representation with partially annotated sensitive attribute labels. |
Aligned Contrastive Loss for Long-Tailed Recognition | 2506.01071v1 | hou2023subclass | \cite{hou2023subclass} | Subclass-balancing Contrastive Learning for Long-tailed Recognition | http://arxiv.org/abs/2306.15925v2 | Long-tailed recognition with imbalanced class distribution naturally emerges
in practical machine learning applications. Existing methods such as data
reweighing, resampling, and supervised contrastive learning enforce the class
balance with a price of introducing imbalance between instances of head class
and tail clas... | true | true | Hou, Chengkai and Zhang, Jieyu and Wang, Haonan and Zhou, Tianyi | 2,023 | null | null | null | null | Subclass-balancing Contrastive Learning for Long-tailed Recognition | Subclass-balancing Contrastive Learning for Long-tailed Recognition | https://arxiv.org/abs/2306.15925 | A novel subclass-balancing contrastive learning (SBCL) approach that clusters each head class into multiple subclasses of similar sizes as the tail classes. |
Aligned Contrastive Loss for Long-Tailed Recognition | 2506.01071v1 | kang2020exploring | \cite{kang2020exploring} | Exploring balanced feature spaces for representation learning | null | null | true | false | Kang, Bingyi and Li, Yu and Xie, Sa and Yuan, Zehuan and Feng, Jiashi | 2,020 | null | null | null | null | Exploring balanced feature spaces for representation learning | [PDF] EXPLORING BALANCED FEATURE SPACES FOR REP | https://openreview.net/pdf?id=OqtLIabPTit | (4) We develop a new method to explicitly pursue balanced feature spaces for representation learning and it outperforms the popular cross-entropy and |
Aligned Contrastive Loss for Long-Tailed Recognition | 2506.01071v1 | li2022targeted | \cite{li2022targeted} | Targeted Supervised Contrastive Learning for Long-Tailed Recognition | http://arxiv.org/abs/2111.13998v2 | Real-world data often exhibits long tail distributions with heavy class
imbalance, where the majority classes can dominate the training process and
alter the decision boundaries of the minority classes. Recently, researchers
have investigated the potential of supervised contrastive learning for
long-tailed recognition,... | true | true | Li, Tianhong and Cao, Peng and Yuan, Yuan and Fan, Lijie and Yang, Yuzhe and Feris, Rogerio S and Indyk, Piotr and Katabi, Dina | 2,022 | null | null | null | null | Targeted Supervised Contrastive Learning for Long-Tailed Recognition | [PDF] Targeted Supervised Contrastive Learning for Long-Tailed ... | https://openaccess.thecvf.com/content/CVPR2022/papers/Li_Targeted_Supervised_Contrastive_Learning_for_Long-Tailed_Recognition_CVPR_2022_paper.pdf | TSC is especially effec- tive on long-tailed recognition tasks, since for traditional methods based on supervised contrastive loss, classes with fewer training |
Aligned Contrastive Loss for Long-Tailed Recognition | 2506.01071v1 | chen2020big | \cite{chen2020big} | Big Self-Supervised Models are Strong Semi-Supervised Learners | http://arxiv.org/abs/2006.10029v2 | One paradigm for learning from few labeled examples while making best use of
a large amount of unlabeled data is unsupervised pretraining followed by
supervised fine-tuning. Although this paradigm uses unlabeled data in a
task-agnostic way, in contrast to common approaches to semi-supervised learning
for computer visio... | true | true | Chen, Ting and Kornblith, Simon and Swersky, Kevin and Norouzi, Mohammad and Hinton, Geoffrey E | 2,020 | null | null | null | Advances in neural information processing systems | Big Self-Supervised Models are Strong Semi-Supervised Learners | [2006.10029] Big Self-Supervised Models are Strong Semi ... | https://arxiv.org/abs/2006.10029 | by T Chen · 2020 · Cited by 2883 — We show that it is surprisingly effective for semi-supervised learning on ImageNet. A key ingredient of our approach is the use of big (deep and wide) networks. |
Aligned Contrastive Loss for Long-Tailed Recognition | 2506.01071v1 | chen2021empirical | \cite{chen2021empirical} | An Empirical Study of Training Self-Supervised Vision Transformers | http://arxiv.org/abs/2104.02057v4 | This paper does not describe a novel method. Instead, it studies a
straightforward, incremental, yet must-know baseline given the recent progress
in computer vision: self-supervised learning for Vision Transformers (ViT).
While the training recipes for standard convolutional networks have been highly
mature and robust,... | true | true | Chen, Xinlei and Xie, Saining and He, Kaiming | 2,021 | null | null | null | null | An Empirical Study of Training Self-Supervised Vision Transformers | [PDF] An Empirical Study of Training Self-Supervised Vision Transformers | https://openaccess.thecvf.com/content/ICCV2021/papers/Chen_An_Empirical_Study_of_Training_Self-Supervised_Vision_Transformers_ICCV_2021_paper.pdf | In summary, we believe that the evidence, challenges, and open questions in this study are worth knowing, if self- supervised Transformers will close the gap in |
Aligned Contrastive Loss for Long-Tailed Recognition | 2506.01071v1 | caron2020unsupervised | \cite{caron2020unsupervised} | Unsupervised Learning of Visual Features by Contrasting Cluster
Assignments | http://arxiv.org/abs/2006.09882v5 | Unsupervised image representations have significantly reduced the gap with
supervised pretraining, notably with the recent achievements of contrastive
learning methods. These contrastive methods typically work online and rely on a
large number of explicit pairwise feature comparisons, which is computationally
challengi... | true | true | Caron, Mathilde and Misra, Ishan and Mairal, Julien and Goyal, Priya and Bojanowski, Piotr and Joulin, Armand | 2,020 | null | null | null | Advances in neural information processing systems | Unsupervised Learning of Visual Features by Contrasting Cluster
Assignments | Unsupervised Learning of Visual Features by Contrasting ... | https://arxiv.org/abs/2006.09882 | Authors:Mathilde Caron, Ishan Misra, Julien Mairal, Priya Goyal, Piotr Bojanowski, Armand Joulin View a PDF of the paper titled Unsupervised Learning of Visual Features by Contrasting Cluster Assignments, by Mathilde Caron and 5 other authors Specifically, our method simultaneously clusters the data while enforcing con... |
Aligned Contrastive Loss for Long-Tailed Recognition | 2506.01071v1 | fort2021drawing | \cite{fort2021drawing} | Drawing Multiple Augmentation Samples Per Image During Training
Efficiently Decreases Test Error | http://arxiv.org/abs/2105.13343v2 | In computer vision, it is standard practice to draw a single sample from the
data augmentation procedure for each unique image in the mini-batch. However
recent work has suggested drawing multiple samples can achieve higher test
accuracies. In this work, we provide a detailed empirical evaluation of how the
number of a... | true | true | Fort, Stanislav and Brock, Andrew and Pascanu, Razvan and De, Soham and Smith, Samuel L | 2,021 | null | null | null | arXiv preprint arXiv:2105.13343 | Drawing Multiple Augmentation Samples Per Image During Training
Efficiently Decreases Test Error | Drawing Multiple Augmentation Samples Per Image During Training ... | https://www.semanticscholar.org/paper/efcafa65a6e69fa52ceba41d7b6356c17c241edc | It is demonstrated drawing multiple samples per image consistently enhances the test accuracy achieved for both small and large batch training, |
Self-supervised ControlNet with Spatio-Temporal Mamba for Real-world
Video Super-resolution | 2506.01037v1 | cao2021video | \cite{cao2021video} | Video Super-Resolution Transformer | http://arxiv.org/abs/2106.06847v3 | Video super-resolution (VSR), with the aim to restore a high-resolution video
from its corresponding low-resolution version, is a spatial-temporal sequence
prediction problem. Recently, Transformer has been gaining popularity due to
its parallel computing ability for sequence-to-sequence modeling. Thus, it
seems to be ... | true | true | Cao, Jiezhang and Li, Yawei and Zhang, Kai and Van Gool, Luc | 2,021 | null | null | null | arXiv preprint arXiv:2106.06847 | Video Super-Resolution Transformer | [2106.06847] Video Super-Resolution Transformer - arXiv | https://arxiv.org/abs/2106.06847 | Abstract:Video super-resolution (VSR), with the aim to restore a high-resolution video from its corresponding low-resolution version, |
Self-supervised ControlNet with Spatio-Temporal Mamba for Real-world
Video Super-resolution | 2506.01037v1 | chan2021basicvsr | \cite{chan2021basicvsr} | BasicVSR: The Search for Essential Components in Video Super-Resolution
and Beyond | http://arxiv.org/abs/2012.02181v2 | Video super-resolution (VSR) approaches tend to have more components than the
image counterparts as they need to exploit the additional temporal dimension.
Complex designs are not uncommon. In this study, we wish to untangle the knots
and reconsider some most essential components for VSR guided by four basic
functional... | true | true | Chan, Kelvin CK and Wang, Xintao and Yu, Ke and Dong, Chao and Loy, Chen Change | 2,021 | null | null | null | null | BasicVSR: The Search for Essential Components in Video Super-Resolution
and Beyond | [PDF] BasicVSR: The Search for Essential Components in Video Super ... | https://openaccess.thecvf.com/content/CVPR2021/papers/Chan_BasicVSR_The_Search_for_Essential_Components_in_Video_Super-Resolution_and_CVPR_2021_paper.pdf | BasicVSR is a video super-resolution pipeline using Propagation, Alignment, Aggregation, and Upsampling, with bidirectional propagation and optical flow for |
Self-supervised ControlNet with Spatio-Temporal Mamba for Real-world
Video Super-resolution | 2506.01037v1 | chan2022basicvsr++ | \cite{chan2022basicvsr++} | Basicvsr++: Improving video super-resolution with enhanced propagation and alignment | null | null | true | false | Chan, Kelvin CK and Zhou, Shangchen and Xu, Xiangyu and Loy, Chen Change | 2,022 | null | null | null | null | Basicvsr++: Improving video super-resolution with enhanced propagation and alignment | ckkelvinchan/BasicVSR_PlusPlus: Official repository of " ... | https://github.com/ckkelvinchan/BasicVSR_PlusPlus | GitHub - ckkelvinchan/BasicVSR_PlusPlus: Official repository of "BasicVSR++: Improving Video Super-Resolution with Enhanced Propagation and Alignment" | .dev_scripts/github | .dev_scripts/github | [Feature] Add relevant code (#6) | Apr 18, 2022 | | .github/workflows | .github/workflows | [Feature] Add relevant code (#6... |
Self-supervised ControlNet with Spatio-Temporal Mamba for Real-world
Video Super-resolution | 2506.01037v1 | isobe2020video | \cite{isobe2020video} | Video Super-Resolution with Recurrent Structure-Detail Network | http://arxiv.org/abs/2008.00455v1 | Most video super-resolution methods super-resolve a single reference frame
with the help of neighboring frames in a temporal sliding window. They are less
efficient compared to the recurrent-based methods. In this work, we propose a
novel recurrent video super-resolution method which is both effective and
efficient in ... | true | true | Isobe, Takashi and Jia, Xu and Gu, Shuhang and Li, Songjiang and Wang, Shengjin and Tian, Qi | 2,020 | null | null | null | null | Video Super-Resolution with Recurrent Structure-Detail Network | Video Super-Resolution with Recurrent Structure-Detail Network | https://arxiv.org/abs/2008.00455 | We propose a novel recurrent video super-resolution method which is both effective and efficient in exploiting previous frames to super-resolve the current |
Self-supervised ControlNet with Spatio-Temporal Mamba for Real-world
Video Super-resolution | 2506.01037v1 | isobe2020video2 | \cite{isobe2020video2} | Video Super-resolution with Temporal Group Attention | http://arxiv.org/abs/2007.10595v1 | Video super-resolution, which aims at producing a high-resolution video from
its corresponding low-resolution version, has recently drawn increasing
attention. In this work, we propose a novel method that can effectively
incorporate temporal information in a hierarchical way. The input sequence is
divided into several ... | true | true | Isobe, Takashi and Li, Songjiang and Jia, Xu and Yuan, Shanxin and Slabaugh, Gregory and Xu, Chunjing and Li, Ya-Li and Wang, Shengjin and Tian, Qi | 2,020 | null | null | null | null | Video Super-resolution with Temporal Group Attention | [PDF] Video Super-Resolution With Temporal Group Attention | https://openaccess.thecvf.com/content_CVPR_2020/papers/Isobe_Video_Super-Resolution_With_Temporal_Group_Attention_CVPR_2020_paper.pdf | For video super- resolution, both spatial information across positions and temporal information across frames can be used to enhance details for an LR frame. |
Self-supervised ControlNet with Spatio-Temporal Mamba for Real-world
Video Super-resolution | 2506.01037v1 | isobe2020revisiting | \cite{isobe2020revisiting} | Revisiting temporal modeling for video super-resolution | null | null | true | false | Isobe, Takashi and Zhu, Fang and Jia, Xu and Wang, Shengjin | 2,020 | null | null | null | arXiv preprint arXiv:2008.05765 | Revisiting temporal modeling for video super-resolution | Revisiting Temporal Modeling for Video Super-resolution | https://www.bmvc2020-conference.com/assets/papers/0033.pdf | In this work, we carefully study and compare three temporal modeling methods (2D CNN with early fusion, 3D CNN with slow fusion and Recurrent Neural Network) |
Self-supervised ControlNet with Spatio-Temporal Mamba for Real-world
Video Super-resolution | 2506.01037v1 | jo2018deep | \cite{jo2018deep} | Deep video super-resolution network using dynamic upsampling filters without explicit motion compensation | null | null | true | false | Jo, Younghyun and Oh, Seoung Wug and Kang, Jaeyeon and Kim, Seon Joo | 2,018 | null | null | null | null | Deep video super-resolution network using dynamic upsampling filters without explicit motion compensation | yhjo09/VSR-DUF | https://github.com/yhjo09/VSR-DUF | Deep Video Super-Resolution Network Using Dynamic Upsampling Filters Without Explicit Motion Compensation. This is a tensorflow implementation of the paper. |
Self-supervised ControlNet with Spatio-Temporal Mamba for Real-world
Video Super-resolution | 2506.01037v1 | liang2024vrt | \cite{liang2024vrt} | VRT: A Video Restoration Transformer | http://arxiv.org/abs/2201.12288v2 | Video restoration (e.g., video super-resolution) aims to restore high-quality
frames from low-quality frames. Different from single image restoration, video
restoration generally requires to utilize temporal information from multiple
adjacent but usually misaligned video frames. Existing deep methods generally
tackle w... | true | true | Liang, Jingyun and Cao, Jiezhang and Fan, Yuchen and Zhang, Kai and Ranjan, Rakesh and Li, Yawei and Timofte, Radu and Van Gool, Luc | 2,024 | null | null | null | IEEE Transactions on Image Processing | VRT: A Video Restoration Transformer | VRT: A Video Restoration Transformer | http://arxiv.org/pdf/2201.12288v2 | Video restoration (e.g., video super-resolution) aims to restore high-quality
frames from low-quality frames. Different from single image restoration, video
restoration generally requires to utilize temporal information from multiple
adjacent but usually misaligned video frames. Existing deep methods generally
tackle w... |
Self-supervised ControlNet with Spatio-Temporal Mamba for Real-world
Video Super-resolution | 2506.01037v1 | liang2022recurrent | \cite{liang2022recurrent} | Recurrent Video Restoration Transformer with Guided Deformable Attention | http://arxiv.org/abs/2206.02146v3 | Video restoration aims at restoring multiple high-quality frames from
multiple low-quality frames. Existing video restoration methods generally fall
into two extreme cases, i.e., they either restore all frames in parallel or
restore the video frame by frame in a recurrent way, which would result in
different merits and... | true | true | Liang, Jingyun and Fan, Yuchen and Xiang, Xiaoyu and Ranjan, Rakesh and Ilg, Eddy and Green, Simon and Cao, Jiezhang and Zhang, Kai and Timofte, Radu and Gool, Luc V | 2,022 | null | null | null | Advances in Neural Information Processing Systems | Recurrent Video Restoration Transformer with Guided Deformable Attention | Recurrent Video Restoration Transformer with Guided Deformable Attention | http://arxiv.org/pdf/2206.02146v3 | Video restoration aims at restoring multiple high-quality frames from
multiple low-quality frames. Existing video restoration methods generally fall
into two extreme cases, i.e., they either restore all frames in parallel or
restore the video frame by frame in a recurrent way, which would result in
different merits and... |
Self-supervised ControlNet with Spatio-Temporal Mamba for Real-world
Video Super-resolution | 2506.01037v1 | wang2019edvr | \cite{wang2019edvr} | EDVR: Video Restoration with Enhanced Deformable Convolutional Networks | http://arxiv.org/abs/1905.02716v1 | Video restoration tasks, including super-resolution, deblurring, etc, are
drawing increasing attention in the computer vision community. A challenging
benchmark named REDS is released in the NTIRE19 Challenge. This new benchmark
challenges existing methods from two aspects: (1) how to align multiple frames
given large ... | true | true | Wang, Xintao and Chan, Kelvin CK and Yu, Ke and Dong, Chao and Change Loy, Chen | 2,019 | null | null | null | null | EDVR: Video Restoration with Enhanced Deformable Convolutional Networks | EDVR: Video Restoration with Enhanced Deformable Convolutional Networks | http://arxiv.org/pdf/1905.02716v1 | Video restoration tasks, including super-resolution, deblurring, etc, are
drawing increasing attention in the computer vision community. A challenging
benchmark named REDS is released in the NTIRE19 Challenge. This new benchmark
challenges existing methods from two aspects: (1) how to align multiple frames
given large ... |
Self-supervised ControlNet with Spatio-Temporal Mamba for Real-world
Video Super-resolution | 2506.01037v1 | xue2019video | \cite{xue2019video} | Video Enhancement with Task-Oriented Flow | http://arxiv.org/abs/1711.09078v3 | Many video enhancement algorithms rely on optical flow to register frames in
a video sequence. Precise flow estimation is however intractable; and optical
flow itself is often a sub-optimal representation for particular video
processing tasks. In this paper, we propose task-oriented flow (TOFlow), a
motion representati... | true | true | Xue, Tianfan and Chen, Baian and Wu, Jiajun and Wei, Donglai and Freeman, William T | 2,019 | null | null | null | International Journal of Computer Vision | Video Enhancement with Task-Oriented Flow | Video Enhancement with Task-Oriented Flow | http://toflow.csail.mit.edu/ | In this paper, we propose task-oriented flow (TOFlow), a flow representation tailored for specific video processing tasks. |
Self-supervised ControlNet with Spatio-Temporal Mamba for Real-world
Video Super-resolution | 2506.01037v1 | liu2013bayesian | \cite{liu2013bayesian} | On Bayesian adaptive video super resolution | null | null | true | false | Liu, Ce and Sun, Deqing | 2,013 | null | null | null | IEEE transactions on pattern analysis and machine intelligence | On Bayesian adaptive video super resolution | [PDF] On Bayesian Adaptive Video Super Resolution - People | https://people.csail.mit.edu/celiu/pdfs/TPAMI13-VSR.pdf | In this paper, we propose a Bayesian approach to adaptive video super resolution via simultaneously estimating underlying motion, blur kernel and noise level |
Self-supervised ControlNet with Spatio-Temporal Mamba for Real-world
Video Super-resolution | 2506.01037v1 | nah2019ntire | \cite{nah2019ntire} | Ntire 2019 challenge on video deblurring and super-resolution: Dataset and study | null | null | true | false | Nah, Seungjun and Baik, Sungyong and Hong, Seokil and Moon, Gyeongsik and Son, Sanghyun and Timofte, Radu and Mu Lee, Kyoung | 2,019 | null | null | null | null | Ntire 2019 challenge on video deblurring and super-resolution: Dataset and study | [PDF] NTIRE 2019 Challenge on Video Deblurring and Super-Resolution | https://openaccess.thecvf.com/content_CVPRW_2019/papers/NTIRE/Nah_NTIRE_2019_Challenge_on_Video_Deblurring_and_Super-Resolution_Dataset_and_CVPRW_2019_paper.pdf | This paper introduces a novel large dataset for video de- blurring, video super-resolution and studies the state-of- the-art as emerged from the NTIRE 2019 |
Self-supervised ControlNet with Spatio-Temporal Mamba for Real-world
Video Super-resolution | 2506.01037v1 | yi2019progressive | \cite{yi2019progressive} | Progressive fusion video super-resolution network via exploiting non-local spatio-temporal correlations | null | null | true | false | Yi, Peng and Wang, Zhongyuan and Jiang, Kui and Jiang, Junjun and Ma, Jiayi | 2,019 | null | null | null | null | Progressive fusion video super-resolution network via exploiting non-local spatio-temporal correlations | Progressive Fusion Video Super-Resolution Network via Exploiting ... | https://github.com/psychopa4/PFNL | GitHub - psychopa4/PFNL: Progressive Fusion Video Super-Resolution Network via Exploiting Non-Local Spatio-Temporal Correlations * GitHub Copilot Write better code with AI * Why GitHub * The ReadME Project GitHub community articles * GitHub Advanced Security Enterprise-grade security features Search code, repos... |
Self-supervised ControlNet with Spatio-Temporal Mamba for Real-world
Video Super-resolution | 2506.01037v1 | li2020mucan | \cite{li2020mucan} | MuCAN: Multi-Correspondence Aggregation Network for Video
Super-Resolution | http://arxiv.org/abs/2007.11803v1 | Video super-resolution (VSR) aims to utilize multiple low-resolution frames
to generate a high-resolution prediction for each frame. In this process,
inter- and intra-frames are the key sources for exploiting temporal and spatial
information. However, there are a couple of limitations for existing VSR
methods. First, o... | true | true | Li, Wenbo and Tao, Xin and Guo, Taian and Qi, Lu and Lu, Jiangbo and Jia, Jiaya | 2,020 | null | null | null | null | MuCAN: Multi-Correspondence Aggregation Network for Video
Super-Resolution | Multi-Correspondence Aggregation Network for Video Super ... - arXiv | https://arxiv.org/abs/2007.11803 | We build an effective multi-correspondence aggregation network (MuCAN) for VSR. Our method achieves state-of-the-art results on multiple benchmark datasets. |
Self-supervised ControlNet with Spatio-Temporal Mamba for Real-world
Video Super-resolution | 2506.01037v1 | realvsr | \cite{realvsr} | Real-world video super-resolution: A benchmark dataset and a decomposition based learning scheme | null | null | true | false | Yang, Xi and Xiang, Wangmeng and Zeng, Hui and Zhang, Lei | 2,021 | null | null | null | null | Real-world video super-resolution: A benchmark dataset and a decomposition based learning scheme | IanYeung/RealVSR: Dataset and Code for ICCV 2021 paper "Real ... | https://github.com/IanYeung/RealVSR | Dataset and Code for ICCV 2021 paper "Real-world Video Super-resolution: A Benchmark Dataset and A Decomposition based Learning Scheme" |
Self-supervised ControlNet with Spatio-Temporal Mamba for Real-world
Video Super-resolution | 2506.01037v1 | realbasicvsr | \cite{realbasicvsr} | Investigating Tradeoffs in Real-World Video Super-Resolution | http://arxiv.org/abs/2111.12704v1 | The diversity and complexity of degradations in real-world video
super-resolution (VSR) pose non-trivial challenges in inference and training.
First, while long-term propagation leads to improved performance in cases of
mild degradations, severe in-the-wild degradations could be exaggerated through
propagation, impairi... | true | true | Chan, Kelvin CK and Zhou, Shangchen and Xu, Xiangyu and Loy, Chen Change | 2,022 | null | null | null | null | Investigating Tradeoffs in Real-World Video Super-Resolution | [PDF] Investigating Tradeoffs in Real-World Video Super-Resolution | https://openaccess.thecvf.com/content/CVPR2022/papers/Chan_Investigating_Tradeoffs_in_Real-World_Video_Super-Resolution_CVPR_2022_paper.pdf | Figure 1. Results on a Real-World Video. In this work, we investigate various tradeoffs caused by the complex and diverse degradations in real-world VSR. |
Self-supervised ControlNet with Spatio-Temporal Mamba for Real-world
Video Super-resolution | 2506.01037v1 | xie2023mitigating | \cite{xie2023mitigating} | Mitigating Artifacts in Real-World Video Super-Resolution Models | http://arxiv.org/abs/2212.07339v1 | The recurrent structure is a prevalent framework for the task of video
super-resolution, which models the temporal dependency between frames via
hidden states. When applied to real-world scenarios with unknown and complex
degradations, hidden states tend to contain unpleasant artifacts and propagate
them to restored fr... | true | true | Xie, Liangbin and Wang, Xintao and Shi, Shuwei and Gu, Jinjin and Dong, Chao and Shan, Ying | 2,023 | null | null | null | null | Mitigating Artifacts in Real-World Video Super-Resolution Models | [PDF] Mitigating Artifacts in Real-World Video Super-resolution Models | https://ojs.aaai.org/index.php/AAAI/article/view/25398/25170 | Artifacts in video super-resolution are mitigated by replacing hidden states with a cleaner one using a Hidden State Attention (HSA) module, which uses cheap |
Self-supervised ControlNet with Spatio-Temporal Mamba for Real-world
Video Super-resolution | 2506.01037v1 | S4 | \cite{S4} | Efficiently modeling long sequences with structured state spaces | null | null | true | false | Gu, Albert and Goel, Karan and R{\'e}, Christopher | 2,021 | null | null | null | arXiv preprint arXiv:2111.00396 | Efficiently modeling long sequences with structured state spaces | Efficiently Modeling Long Sequences with Structured State Spaces | http://arxiv.org/pdf/2111.00396v3 | A central goal of sequence modeling is designing a single principled model
that can address sequence data across a range of modalities and tasks,
particularly on long-range dependencies. Although conventional models including
RNNs, CNNs, and Transformers have specialized variants for capturing long
dependencies, they s... |
Self-supervised ControlNet with Spatio-Temporal Mamba for Real-world
Video Super-resolution | 2506.01037v1 | variant1 | \cite{variant1} | Long Movie Clip Classification with State-Space Video Models | http://arxiv.org/abs/2204.01692v3 | Most modern video recognition models are designed to operate on short video
clips (e.g., 5-10s in length). Thus, it is challenging to apply such models to
long movie understanding tasks, which typically require sophisticated
long-range temporal reasoning. The recently introduced video transformers
partially address thi... | true | true | Islam, Md Mohaiminul and Bertasius, Gedas | 2,022 | null | null | null | null | Long Movie Clip Classification with State-Space Video Models | Long Movie Clip Classification with State-Space Video ... | https://arxiv.org/abs/2204.01692 | by MM Islam · 2022 · Cited by 137 — We propose ViS4mer, an efficient long-range video model that combines the strengths of self-attention and the recently introduced structured state-space |
Self-supervised ControlNet with Spatio-Temporal Mamba for Real-world
Video Super-resolution | 2506.01037v1 | variant2 | \cite{variant2} | S4ND: Modeling Images and Videos as Multidimensional Signals Using State
Spaces | http://arxiv.org/abs/2210.06583v2 | Visual data such as images and videos are typically modeled as
discretizations of inherently continuous, multidimensional signals. Existing
continuous-signal models attempt to exploit this fact by modeling the
underlying signals of visual (e.g., image) data directly. However, these models
have not yet been able to achi... | true | true | Nguyen, Eric and Goel, Karan and Gu, Albert and Downs, Gordon and Shah, Preey and Dao, Tri and Baccus, Stephen and R{\'e}, Christopher | 2,022 | null | null | null | Advances in neural information processing systems | S4ND: Modeling Images and Videos as Multidimensional Signals Using State
Spaces | [PDF] S4ND: Modeling Images and Videos as Multidimensional Signals ... | https://proceedings.neurips.cc/paper_files/paper/2022/file/13388efc819c09564c66ab2dc8463809-Paper-Conference.pdf | S4 investigated state space models, which are linear time-invariant systems that map signals u(t) 7! y(t) and can be represented either as a linear ODE ( |
Self-supervised ControlNet with Spatio-Temporal Mamba for Real-world
Video Super-resolution | 2506.01037v1 | variant3 | \cite{variant3} | Selective structured state-spaces for long-form video understanding | null | null | true | false | Wang, Jue and Zhu, Wentao and Wang, Pichao and Yu, Xiang and Liu, Linda and Omar, Mohamed and Hamid, Raffay | 2,023 | null | null | null | null | Selective structured state-spaces for long-form video understanding | [PDF] Selective Structured State-Spaces for Long-Form Video ... | https://openaccess.thecvf.com/content/CVPR2023/papers/Wang_Selective_Structured_State-Spaces_for_Long-Form_Video_Understanding_CVPR_2023_paper.pdf | We present extensive comparative results using three challenging long-form video understanding datasets. (LVU, COIN and Breakfast), demonstrating that our ap-. |
Self-supervised ControlNet with Spatio-Temporal Mamba for Real-world
Video Super-resolution | 2506.01037v1 | variant4 | \cite{variant4} | Diagonal State Spaces are as Effective as Structured State Spaces | http://arxiv.org/abs/2203.14343v3 | Modeling long range dependencies in sequential data is a fundamental step
towards attaining human-level performance in many modalities such as text,
vision, audio and video. While attention-based models are a popular and
effective choice in modeling short-range interactions, their performance on
tasks requiring long ra... | true | true | Gupta, Ankit and Gu, Albert and Berant, Jonathan | 2,022 | null | null | null | Advances in Neural Information Processing Systems | Diagonal State Spaces are as Effective as Structured State Spaces | Diagonal State Spaces are as Effective as Structured State Spaces | http://arxiv.org/pdf/2203.14343v3 | Modeling long range dependencies in sequential data is a fundamental step
towards attaining human-level performance in many modalities such as text,
vision, audio and video. While attention-based models are a popular and
effective choice in modeling short-range interactions, their performance on
tasks requiring long ra... |
Self-supervised ControlNet with Spatio-Temporal Mamba for Real-world
Video Super-resolution | 2506.01037v1 | variant5 | \cite{variant5} | Simplified State Space Layers for Sequence Modeling | http://arxiv.org/abs/2208.04933v3 | Models using structured state space sequence (S4) layers have achieved
state-of-the-art performance on long-range sequence modeling tasks. An S4 layer
combines linear state space models (SSMs), the HiPPO framework, and deep
learning to achieve high performance. We build on the design of the S4 layer
and introduce a new... | true | true | Smith, Jimmy TH and Warrington, Andrew and Linderman, Scott W | 2,022 | null | null | null | arXiv preprint arXiv:2208.04933 | Simplified State Space Layers for Sequence Modeling | Simplified State Space Layers for Sequence Modeling | http://arxiv.org/pdf/2208.04933v3 | Models using structured state space sequence (S4) layers have achieved
state-of-the-art performance on long-range sequence modeling tasks. An S4 layer
combines linear state space models (SSMs), the HiPPO framework, and deep
learning to achieve high performance. We build on the design of the S4 layer
and introduce a new... |
Self-supervised ControlNet with Spatio-Temporal Mamba for Real-world
Video Super-resolution | 2506.01037v1 | mamba | \cite{mamba} | Mamba: Linear-Time Sequence Modeling with Selective State Spaces | http://arxiv.org/abs/2312.00752v2 | Foundation models, now powering most of the exciting applications in deep
learning, are almost universally based on the Transformer architecture and its
core attention module. Many subquadratic-time architectures such as linear
attention, gated convolution and recurrent models, and structured state space
models (SSMs) ... | true | true | Gu, Albert and Dao, Tri | 2,023 | null | null | null | arXiv preprint arXiv:2312.00752 | Mamba: Linear-Time Sequence Modeling with Selective State Spaces | Mamba: Linear-Time Sequence Modeling with Selective State Spaces | https://openreview.net/forum?id=tEYskw1VY2 | This paper proposes Mamba, a linear-time sequence model with an intra-layer combination of Selective S4D, Short Convolution and Gated Linear Unit. The paper |
Self-supervised ControlNet with Spatio-Temporal Mamba for Real-world
Video Super-resolution | 2506.01037v1 | domain1 | \cite{domain1} | Mamba-nd: Selective state space modeling for multi-dimensional data | null | null | true | false | Li, Shufan and Singh, Harkanwar and Grover, Aditya | 2,024 | null | null | null | arXiv preprint arXiv:2402.05892 | Mamba-nd: Selective state space modeling for multi-dimensional data | Mamba-ND: Selective State Space Modeling for Multi-Dimensional ... | https://arxiv.org/abs/2402.05892 | In this work, we present Mamba-ND, a generalized design extending the Mamba architecture to arbitrary multi-dimensional data. |
Self-supervised ControlNet with Spatio-Temporal Mamba for Real-world
Video Super-resolution | 2506.01037v1 | domain2 | \cite{domain2} | PointMamba: A Simple State Space Model for Point Cloud Analysis | http://arxiv.org/abs/2402.10739v5 | Transformers have become one of the foundational architectures in point cloud
analysis tasks due to their excellent global modeling ability. However, the
attention mechanism has quadratic complexity, making the design of a linear
complexity method with global modeling appealing. In this paper, we propose
PointMamba, tr... | true | true | Liang, Dingkang and Zhou, Xin and Wang, Xinyu and Zhu, Xingkui and Xu, Wei and Zou, Zhikang and Ye, Xiaoqing and Bai, Xiang | 2,024 | null | null | null | arXiv preprint arXiv:2402.10739 | PointMamba: A Simple State Space Model for Point Cloud Analysis | PointMamba: A Simple State Space Model for Point Cloud Analysis | http://arxiv.org/pdf/2402.10739v5 | Transformers have become one of the foundational architectures in point cloud
analysis tasks due to their excellent global modeling ability. However, the
attention mechanism has quadratic complexity, making the design of a linear
complexity method with global modeling appealing. In this paper, we propose
PointMamba, tr... |
Self-supervised ControlNet with Spatio-Temporal Mamba for Real-world
Video Super-resolution | 2506.01037v1 | domain3 | \cite{domain3} | Vision Mamba: Efficient Visual Representation Learning with
Bidirectional State Space Model | http://arxiv.org/abs/2401.09417v3 | Recently the state space models (SSMs) with efficient hardware-aware designs,
i.e., the Mamba deep learning model, have shown great potential for long
sequence modeling. Meanwhile building efficient and generic vision backbones
purely upon SSMs is an appealing direction. However, representing visual data
is challenging... | true | true | Zhu, Lianghui and Liao, Bencheng and Zhang, Qian and Wang, Xinlong and Liu, Wenyu and Wang, Xinggang | 2,024 | null | null | null | arXiv preprint arXiv:2401.09417 | Vision Mamba: Efficient Visual Representation Learning with
Bidirectional State Space Model | Vision Mamba: Efficient Visual Representation Learning with ... - arXiv | https://arxiv.org/abs/2401.09417 | Title:Vision Mamba: Efficient Visual Representation Learning with Bidirectional State Space Model View a PDF of the paper titled Vision Mamba: Efficient Visual Representation Learning with Bidirectional State Space Model, by Lianghui Zhu and 5 other authors In this paper, we show that the reliance on self-attention for... |
Self-supervised ControlNet with Spatio-Temporal Mamba for Real-world
Video Super-resolution | 2506.01037v1 | self-supervised_task1 | \cite{self-supervised_task1} | Unsupervised Learning of Visual Features by Contrasting Cluster
Assignments | http://arxiv.org/abs/2006.09882v5 | Unsupervised image representations have significantly reduced the gap with
supervised pretraining, notably with the recent achievements of contrastive
learning methods. These contrastive methods typically work online and rely on a
large number of explicit pairwise feature comparisons, which is computationally
challengi... | true | true | Caron, Mathilde and Misra, Ishan and Mairal, Julien and Goyal, Priya and Bojanowski, Piotr and Joulin, Armand | 2,020 | null | null | null | Advances in neural information processing systems | Unsupervised Learning of Visual Features by Contrasting Cluster
Assignments | Unsupervised Learning of Visual Features by Contrasting ... | https://arxiv.org/abs/2006.09882 | Authors:Mathilde Caron, Ishan Misra, Julien Mairal, Priya Goyal, Piotr Bojanowski, Armand Joulin View a PDF of the paper titled Unsupervised Learning of Visual Features by Contrasting Cluster Assignments, by Mathilde Caron and 5 other authors Specifically, our method simultaneously clusters the data while enforcing con... |
Self-supervised ControlNet with Spatio-Temporal Mamba for Real-world
Video Super-resolution | 2506.01037v1 | self-supervised_task2 | \cite{self-supervised_task2} | Emerging Properties in Self-Supervised Vision Transformers | http://arxiv.org/abs/2104.14294v2 | In this paper, we question if self-supervised learning provides new
properties to Vision Transformer (ViT) that stand out compared to convolutional
networks (convnets). Beyond the fact that adapting self-supervised methods to
this architecture works particularly well, we make the following observations:
first, self-sup... | true | true | Caron, Mathilde and Touvron, Hugo and Misra, Ishan and J{\'e}gou, Herv{\'e} and Mairal, Julien and Bojanowski, Piotr and Joulin, Armand | 2,021 | null | null | null | null | Emerging Properties in Self-Supervised Vision Transformers | [PDF] Emerging Properties in Self-Supervised Vision Transformers | https://openaccess.thecvf.com/content/ICCV2021/papers/Caron_Emerging_Properties_in_Self-Supervised_Vision_Transformers_ICCV_2021_paper.pdf | Self-supervised ViT features contain semantic segmentation, scene layout, object boundaries, and perform well with k-NN classifiers, unlike supervised ViTs or |
Self-supervised ControlNet with Spatio-Temporal Mamba for Real-world
Video Super-resolution | 2506.01037v1 | self-supervised_task3 | \cite{self-supervised_task3} | A Simple Framework for Contrastive Learning of Visual Representations | http://arxiv.org/abs/2002.05709v3 | This paper presents SimCLR: a simple framework for contrastive learning of
visual representations. We simplify recently proposed contrastive
self-supervised learning algorithms without requiring specialized architectures
or a memory bank. In order to understand what enables the contrastive
prediction tasks to learn use... | true | true | Chen, Ting and Kornblith, Simon and Norouzi, Mohammad and Hinton, Geoffrey | 2,020 | null | null | null | null | A Simple Framework for Contrastive Learning of Visual Representations | A Simple Framework for Contrastive Learning of Visual Representations | http://arxiv.org/pdf/2002.05709v3 | This paper presents SimCLR: a simple framework for contrastive learning of
visual representations. We simplify recently proposed contrastive
self-supervised learning algorithms without requiring specialized architectures
or a memory bank. In order to understand what enables the contrastive
prediction tasks to learn use... |
Self-supervised ControlNet with Spatio-Temporal Mamba for Real-world
Video Super-resolution | 2506.01037v1 | self-supervised_task4 | \cite{self-supervised_task4} | SSR: An Efficient and Robust Framework for Learning with Unknown Label
Noise | http://arxiv.org/abs/2111.11288v2 | Despite the large progress in supervised learning with neural networks, there
are significant challenges in obtaining high-quality, large-scale and
accurately labelled datasets. In such a context, how to learn in the presence
of noisy labels has received more and more attention. As a relatively complex
problem, in orde... | true | true | Feng, Chen and Tzimiropoulos, Georgios and Patras, Ioannis | 2,021 | null | null | null | arXiv preprint arXiv:2111.11288 | SSR: An Efficient and Robust Framework for Learning with Unknown Label
Noise | [PDF] SSR: An Efficient and Robust Framework for Learning with Unknown ... | https://bmvc2022.mpi-inf.mpg.de/0372.pdf | In this paper, we consider a novel problem setting, Learning with Unknown. Label Noise (LULN), that is, learning when both the degree and the type of noise are. |
Self-supervised ControlNet with Spatio-Temporal Mamba for Real-world
Video Super-resolution | 2506.01037v1 | self-supervised_task5 | \cite{self-supervised_task5} | Self-supervised representation learning with cross-context learning between global and hypercolumn features | null | null | true | false | Gao, Zheng and Feng, Chen and Patras, Ioannis | 2,024 | null | null | null | null | Self-supervised representation learning with cross-context learning between global and hypercolumn features | [PDF] Self-Supervised Representation Learning With Cross-Context ... | https://openaccess.thecvf.com/content/WACV2024/papers/Gao_Self-Supervised_Representation_Learning_With_Cross-Context_Learning_Between_Global_and_Hypercolumn_WACV_2024_paper.pdf | This leads to a novel self-supervised framework–cross-context learn- ing between global and hypercolumn features (CGH)–that learns representations by |
Self-supervised ControlNet with Spatio-Temporal Mamba for Real-world
Video Super-resolution | 2506.01037v1 | self-supervised_task6 | \cite{self-supervised_task6} | Momentum Contrast for Unsupervised Visual Representation Learning | http://arxiv.org/abs/1911.05722v3 | We present Momentum Contrast (MoCo) for unsupervised visual representation
learning. From a perspective on contrastive learning as dictionary look-up, we
build a dynamic dictionary with a queue and a moving-averaged encoder. This
enables building a large and consistent dictionary on-the-fly that facilitates
contrastive... | true | true | He, Kaiming and Fan, Haoqi and Wu, Yuxin and Xie, Saining and Girshick, Ross | 2,020 | null | null | null | null | Momentum Contrast for Unsupervised Visual Representation Learning | Momentum Contrast for Unsupervised Visual Representation Learning | http://arxiv.org/pdf/1911.05722v3 | We present Momentum Contrast (MoCo) for unsupervised visual representation
learning. From a perspective on contrastive learning as dictionary look-up, we
build a dynamic dictionary with a queue and a moving-averaged encoder. This
enables building a large and consistent dictionary on-the-fly that facilitates
contrastive... |
Self-supervised ControlNet with Spatio-Temporal Mamba for Real-world
Video Super-resolution | 2506.01037v1 | self-supervised_task7 | \cite{self-supervised_task7} | Masked Autoencoders Are Scalable Vision Learners | http://arxiv.org/abs/2111.06377v3 | This paper shows that masked autoencoders (MAE) are scalable self-supervised
learners for computer vision. Our MAE approach is simple: we mask random
patches of the input image and reconstruct the missing pixels. It is based on
two core designs. First, we develop an asymmetric encoder-decoder architecture,
with an enco... | true | true | He, Kaiming and Chen, Xinlei and Xie, Saining and Li, Yanghao and Doll{\'a}r, Piotr and Girshick, Ross | 2,022 | null | null | null | null | Masked Autoencoders Are Scalable Vision Learners | Masked Autoencoders Are Scalable Vision Learners | http://arxiv.org/pdf/2111.06377v3 | This paper shows that masked autoencoders (MAE) are scalable self-supervised
learners for computer vision. Our MAE approach is simple: we mask random
patches of the input image and reconstruct the missing pixels. It is based on
two core designs. First, we develop an asymmetric encoder-decoder architecture,
with an enco... |
Self-supervised ControlNet with Spatio-Temporal Mamba for Real-world
Video Super-resolution | 2506.01037v1 | self-supervised_task8 | \cite{self-supervised_task8} | SimMIM: A Simple Framework for Masked Image Modeling | http://arxiv.org/abs/2111.09886v2 | This paper presents SimMIM, a simple framework for masked image modeling. We
simplify recently proposed related approaches without special designs such as
block-wise masking and tokenization via discrete VAE or clustering. To study
what let the masked image modeling task learn good representations, we
systematically st... | true | true | Xie, Zhenda and Zhang, Zheng and Cao, Yue and Lin, Yutong and Bao, Jianmin and Yao, Zhuliang and Dai, Qi and Hu, Han | 2,022 | null | null | null | null | SimMIM: A Simple Framework for Masked Image Modeling | SimMIM: A Simple Framework for Masked Image Modeling | http://arxiv.org/pdf/2111.09886v2 | This paper presents SimMIM, a simple framework for masked image modeling. We
simplify recently proposed related approaches without special designs such as
block-wise masking and tokenization via discrete VAE or clustering. To study
what let the masked image modeling task learn good representations, we
systematically st... |
Self-supervised ControlNet with Spatio-Temporal Mamba for Real-world
Video Super-resolution | 2506.01037v1 | cl1 | \cite{cl1} | Masked Siamese Networks for Label-Efficient Learning | http://arxiv.org/abs/2204.07141v1 | We propose Masked Siamese Networks (MSN), a self-supervised learning
framework for learning image representations. Our approach matches the
representation of an image view containing randomly masked patches to the
representation of the original unmasked image. This self-supervised
pre-training strategy is particularly ... | true | true | Assran, Mahmoud and Caron, Mathilde and Misra, Ishan and Bojanowski, Piotr and Bordes, Florian and Vincent, Pascal and Joulin, Armand and Rabbat, Mike and Ballas, Nicolas | 2,022 | null | null | null | null | Masked Siamese Networks for Label-Efficient Learning | Masked Siamese Networks for Label-Efficient Learning | https://www.ecva.net/papers/eccv_2022/papers_ECCV/papers/136910442.pdf | by M Assran · Cited by 421 — We propose Masked Siamese Networks (MSNs), a self-supervised learning frame- work that leverages the idea of mask-denoising while avoiding pixel and token-level. |
Self-supervised ControlNet with Spatio-Temporal Mamba for Real-world
Video Super-resolution | 2506.01037v1 | cl2 | \cite{cl2} | Adaptive Soft Contrastive Learning | http://arxiv.org/abs/2207.11163v1 | Self-supervised learning has recently achieved great success in
representation learning without human annotations. The dominant method -- that
is contrastive learning, is generally based on instance discrimination tasks,
i.e., individual samples are treated as independent categories. However,
presuming all the samples ... | true | true | Feng, Chen and Patras, Ioannis | 2,022 | null | null | null | null | Adaptive Soft Contrastive Learning | Adaptive Soft Contrastive Learning | http://arxiv.org/pdf/2207.11163v1 | Self-supervised learning has recently achieved great success in
representation learning without human annotations. The dominant method -- that
is contrastive learning, is generally based on instance discrimination tasks,
i.e., individual samples are treated as independent categories. However,
presuming all the samples ... |
Self-supervised ControlNet with Spatio-Temporal Mamba for Real-world
Video Super-resolution | 2506.01037v1 | cl3 | \cite{cl3} | MaskCon: Masked Contrastive Learning for Coarse-Labelled Dataset | http://arxiv.org/abs/2303.12756v1 | Deep learning has achieved great success in recent years with the aid of
advanced neural network structures and large-scale human-annotated datasets.
However, it is often costly and difficult to accurately and efficiently
annotate large-scale datasets, especially for some specialized domains where
fine-grained labels a... | true | true | Feng, Chen and Patras, Ioannis | 2,023 | null | null | null | null | MaskCon: Masked Contrastive Learning for Coarse-Labelled Dataset | Masked Contrastive Learning for Coarse-Labelled Dataset | https://ieeexplore.ieee.org/iel7/10203037/10203050/10203131.pdf | by C Feng · 2023 · Cited by 17 — MaskCon is a contrastive learning method for coarse-labeled datasets, generating soft labels based on sample distances to learn fine-grained representations. |
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