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Class Balanced Adaptive Pseudo Labeling for Federated Semi-Supervised Learning

Class Balanced Adaptive Pseudo Labeling for Federated Semi-S...

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ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Li, Ming Li, Qingli Wang, Yan East China Normal Univ Shanghai Key Lab Multidimens Informat Proc Shanghai Peoples R China

ISBN: (纸本)9798350301298

This paper focuses on federated semi-supervised learning (FSSL), assuming that few clients have fully labeled data (labeled clients) and the training datasets in other clients are fully unlabeled (unlabeled clients). Existing methods attempt to deal with the challenges caused by not independent and identically distributed data (Non-IID) setting. Though methods such as sub-consensus models have been proposed, they usually adopt standard pseudo labeling or consistency regularization on unlabeled clients which can be easily influenced by imbalanced class distribution. Thus, problems in FSSL are still yet to be solved. To seek for a fundamental solution to this problem, we present Class Balanced Adaptive Pseudo Labeling (CBAFed), to study FSSL from the perspective of pseudo labeling. In CBAFed, the first key element is a fixed pseudo labeling strategy to handle the catastrophic forgetting problem, where we keep a fixed set by letting pass information of unlabeled data at the beginning of the unlabeled client training in each communication round. The second key element is that we design class balanced adaptive thresholds via considering the empirical distribution of all training data in local clients, to encourage a balanced training process. To make the model reach a better optimum, we further propose a residual weight connection in local supervised training and global model aggregation. Extensive experiments on five datasets demonstrate the superiority of CBAFed. Code will be available at https://***/minglllli/CBAFed.

关键词： vision applications and systems

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Speed up Object Detection on Gigapixel-level Images with Patch Arrangement

Speed up Object Detection on Gigapixel-level Images with Pat...

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ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Fan, Jiahao Liu, Huabin Yang, Wenjie See, John Zhang, Aixin Lin, Weiyao Shanghai Jiao Tong Univ Shanghai Peoples R China Heriot Watt Univ Putrajaya Malaysia

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

With the appearance of super high-resolution (e.g., gigapixel-level) images, performing efficient object detection on such images becomes an important issue. Most existing works for efficient object detection on high-resolution images focus on generating local patches where objects may exist, and then every patch is detected independently. However, when the image resolution reaches gigapixel-level, they will suffer from a huge time cost for detecting numerous patches. Different from them, we devise a novel patch arrangement framework for fast object detection on gigapixel-level images. Under this framework, a Patch Arrangement Network (PAN) is proposed to accelerate the detection by determining which patches could be packed together into a compact canvas. Specifically, PAN consists of (1) a Patch Filter Module (PFM) (2) a Patch Packing Module (PPM). PFM filters patch candidates by learning to select patches between two granularities. Subsequently, from the remaining patches, PPM determines how to pack these patches together into a smaller number of canvases. Meanwhile, it generates an ideal layout of patches on canvas. These canvases are fed to the detector to get final results. Experiments show that our method could improve the inference speed on gigapixel-level images by 5x while maintaining great performance.

关键词： computer vision Image resolution Image recognition Costs Layout Object detection Reinforcement learning

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Combining Semantic Guidance and Deep Reinforcement Learning For Generating Human Level Paintings

Combining Semantic Guidance and Deep Reinforcement Learning ...

引用

ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Singh, Jaskirat Zheng, Liang Australian Natl Univ Canberra ACT Australia

ISBN: (纸本)9781665445092

Generation of stroke-based non-photorealistic imagery, is an important problem in the computer vision community. As an endeavor in this direction, substantial recent research efforts have been focused on teaching machines "how to paint", in a manner similar to a human painter. However, the applicability of previous methods has been limited to datasets with little variation in position, scale and saliency of the foreground object. As a consequence, we find that these methods struggle to cover the granularity and diversity possessed by real world images. To this end, we propose a Semantic Guidance pipeline with 1) a bi-level painting procedure for learning the distinction between foreground and background brush strokes at training time. 2) We also introduce invariance to the position and scale of the foreground object through a neural alignment model, which combines object localization and spatial transformer networks in an end to end manner, to zoom into a particular semantic instance. 3) The distinguishing features of the in-focus object are then amplified by maximizing a novel guided backpropagation based focus reward. The proposed agent does not require any supervision on human stroke-data and successfully handles variations in foreground object attributes, thus, producing much higher quality canvases for the CUB-200 Birds [29] and Stanford Cars-196 [17] datasets. Finally, we demonstrate the further efficacy of our method on complex datasets with multiple foreground object instances by evaluating an extension of our method on the challenging Virtual-KITTI [2] dataset. Source code and models are available at https://***/1jsingh/semantic-guidance.

关键词： Backpropagation Training computer vision Semantics Pipelines Reinforcement learning Stroke (medical condition)

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Accelerating vision-Language Pretraining with Free Language Modeling

Accelerating Vision-Language Pretraining with Free Language ...

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ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Wang, Teng Ge, Yixiao Zheng, Feng Cheng, Ran Shan, Ying Qie, Xiaohu Luo, Ping Southern Univ Sci & Technol Shenzhen Peoples R China Univ Hong Kong Hong Kong Peoples R China ARC Lab Shenzhen Peoples R China Tencent PCG Shenzhen Peoples R China Peng Cheng Lab Shenzhen Peoples R China Shanghai AI Lab Shanghai Peoples R China

ISBN: (纸本)9798350301298

The state of the arts in vision-language pretraining (VLP) achieves exemplary performance but suffers from high training costs resulting from slow convergence and long training time, especially on large-scale web datasets. An essential obstacle to training efficiency lies in the entangled prediction rate (percentage of tokens for reconstruction) and corruption rate (percentage of corrupted tokens) in masked language modeling (MLM), that is, a proper corruption rate is achieved at the cost of a large portion of output tokens being excluded from prediction loss. To accelerate the convergence of VLP, we propose a new pretraining task, namely, free language modeling (FLM), that enables a 100% prediction rate with arbitrary corruption rates. FLM successfully frees the prediction rate from the tie-up with the corruption rate while allowing the corruption spans to be customized for each token to be predicted. FLM-trained models are encouraged to learn better and faster given the same GPU time by exploiting bidirectional contexts more flexibly. Extensive experiments show FLM could achieve an impressive 2.5x pretraining time reduction in comparison to the MLM-based methods, while keeping competitive performance on both vision-language understanding and generation tasks. Code will be public at https://***/TencentARC/FLM.

关键词： and reasoning language vision

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Online Depth Learning against Forgetting in Monocular Videos

Online Depth Learning against Forgetting in Monocular Videos

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ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Zhang, Zhenyu Lathuiliere, Stephane Ricci, Elisa Sebe, Nicu Yan, Yan Yang, Jian Nanjing Univ Sci & Technol PCA Lab Key Lab Intelligent Percept & Syst High Dimens In Minist Educ Nanjing Jiangsu Peoples R China Univ Trento DISI Via Sommar 14 Povo TN Italy Fdn Bruno Kessler Technol Vis Via Sommar 18 Povo TN Italy Telecom Paris Multimedia Grp Paris France Nanjing Univ Sci & Technol Jiangsu Key Lab Image & Video Understanding Socia Sch Comp Sci & Engn Nanjing Peoples R China

ISBN: (纸本)9781728171685

Online depth learning is the problem of consistently adapting a depth estimation model to handle a continuously changing environment. This problem is challenging due to the network easily overfits on the current environment and forgets its past experiences. To address such problem, this paper presents a novel Learning to Prevent Forgetting (LPF) method for online mono-depth adaptation to new target domains in unsupervised manner. Instead of updating the universal parameters, LPF learns adapter modules to efficiently adjust the feature representation and distribution without losing the pre-learned knowledge in online condition. Specifically, to adapt temporal-continuous depth patterns in videos, we introduce a novel meta-learning approach to learn adapter modules by combining online adaptation process into the learning objective. To further avoid overfitting, we propose a novel temporal-consistent regularization to harmonize the gradient descent procedure at each online learning step. Extensive evaluations on real-world datasets demonstrate that the proposed method, with very limited parameters, significantly improves the estimation quality.

关键词： computer vision

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Ingredient-oriented Multi-Degradation Learning for Image Restoration

Ingredient-oriented Multi-Degradation Learning for Image Res...

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ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Zhang, Jinghao Huang, Jie Yao, Mingde Yang, Zizheng Yu, Hu Zhou, Man Zhao, Feng Univ Sci & Technol China Hefei Peoples R China

ISBN: (纸本)9798350301298

Learning to leverage the relationship among diverse image restoration tasks is quite beneficial for unraveling the intrinsic ingredients behind the degradation. Recent years have witnessed the flourish of various All-in-one methods, which handle multiple image degradations within a single model. In practice, however, few attempts have been made to excavate task correlations in that exploring the underlying fundamental ingredients of various image degradations, resulting in poor scalability as more tasks are involved. In this paper, we propose a novel perspective to delve into the degradation via an ingredients-oriented rather than previous task-oriented manner for scalable learning. Specifically, our method, named Ingredients-oriented Degradation Reformulation framework (IDR), consists of two stages, namely task-oriented knowledge collection and ingredients-oriented knowledge integration. In the first stage, we conduct ad hoc operations on different degradations according to the underlying physics principles, and establish the corresponding prior hubs for each type of degradation. While the second stage progressively reformulates the preceding task-oriented hubs into single ingredients-oriented hub via learnable Principal Component Analysis (PCA), and employs a dynamic routing mechanism for probabilistic unknown degradation removal. Extensive experiments on various image restoration tasks demonstrate the effectiveness and scalability of our method. More importantly, our IDR exhibits the favorable generalization ability to unknown downstream tasks.

关键词： Low-level vision

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Enriching ImageNet with Human Similarity Judgments and Psychological Embeddings

Enriching ImageNet with Human Similarity Judgments and Psych...

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ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Roads, Brett D. Love, Bradley C. UCL Dept Expt Psychol London England

ISBN: (纸本)9781665445092

Advances in supervised learning approaches to object recognition flourished in part because of the availability of high-quality datasets and associated benchmarks. However, these benchmarks-such as ILSVRC-are relatively task-specific, focusing predominately on predicting class labels. We introduce a publicly-available dataset that embodies the task-general capabilities of human perception and reasoning. The Human Similarity Judgments extension to ImageNet (ImageNet-HSJ) is composed of a large set of human similarity judgments that supplements the existing ILSVRC validation set. The new dataset supports a range of task and performance metrics, including evaluation of unsupervised algorithms. We demonstrate two methods of assessment: using the similarity judgments directly and using a psychological embedding trained on the similarity judgments. This embedding space contains an order of magnitude more points (i.e., images) than previous efforts based on human judgments. We were able to scale to the MI 50,000 image ILSVRC validation set through a selective sampling process that used variational Bayesian inference and model ensembles to sample aspects of the embedding space that were most uncertain. To demonstrate the utility of ImageNet-HSJ, we used the similarity ratings and the embedding space to evaluate how well several popular models conform to human similarity judgments. One finding is that the more complex models that perform better on task-specific benchmarks do not better conform to human semantic judgments. In addition to the human similarity judgments, pre-trained psychological embeddings and code for inferring variational embeddings are made publicly available. ImageNet-HSJ supports the appraisal of internal representations and the development of more humanlike models.

关键词： Measurement computer vision Supervised learning Semantics Psychology Focusing Benchmark testing

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Visually Informed Binaural Audio Generation without Binaural Audios

Visually Informed Binaural Audio Generation without Binaural...

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ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Xu, Xudong Zhou, Hang Liu, Ziwei Dai, Bo Wang, Xiaogang Lin, Dahua Chinese Univ Hong Kong CUHK SenseTime Joint Lab Hong Kong Peoples R China Nanyang Technol Univ S Lab Singapore Singapore

ISBN: (纸本)9781665445092

Stereophonic audio, especially binaural audio, plays an essential role in immersive viewing environments. Recent research has explored generating visually guided stereophonic audios supervised by multi-channel audio collections. However, due to the requirement of professional recording devices, existing datasets are limited in scale and variety, which impedes the generalization of supervised methods in real-world scenarios. In this work, we propose PseudoBinaural, an effective pipeline that is free of binaural recordings. The key insight is to carefully build pseudo visual-stereo pairs with mono data for training. Specifically, we leverage spherical harmonic decomposition and head-related impulse response (HRIR) to identify the relationship between spatial locations and received binaural audios. Then in the visual modality, corresponding visual cues of the mono data are manually placed at sound source positions to form the pairs. Compared to fully-supervised paradigms, our binaural-recording-free pipeline shows great stability in cross-dataset evaluation and achieves comparable performance under subjective preference. Moreover, combined with binaural recordings, our method is able to further boost the performance of binaural audio generation under supervised settings(1).

关键词： Training Visualization computer vision Computational modeling Pipelines Audio recording Harmonic analysis

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MAD: A Scalable Dataset for Language Grounding in Videos from Movie Audio Descriptions

MAD: A Scalable Dataset for Language Grounding in Videos fro...

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ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Soldan, Mattia Pardo, Alejandro Alcazar, Juan Leon Heilbron, Fabian Caba Zhao, Chen Giancola, Silvio Ghanem, Bernard King Abdullah Univ Sci & Technol KAUST Thuwal Saudi Arabia Adobe Res San Jose CA USA

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

The recent and increasing interest in video-language research has driven the development of large-scale datasets that enable data-intensive machine learning techniques. In comparison, limited effort has been made at assessing the fitness of these datasets for the video-language grounding task. Recent works have begun to discover significant limitations in these datasets, suggesting that state-ofthe-art techniques commonly overfit to hidden dataset biases. In this work, we present MAD (Movie Audio Descriptions), a novel benchmark that departs from the paradigm of augmenting existing video datasets with text annotations and focuses on crawling and aligning available audio descriptions of mainstream movies. MAD contains over 384;000 natural language sentences grounded in over 1;200 hours of videos and exhibits a significant reduction in the currently diagnosed biases for video-language grounding datasets. MAD's collection strategy enables a novel and more challenging version of video-language grounding, where short temporal moments (typically seconds long) must be accurately grounded in diverse long-form videos that can last up to three hours. We have released MAD's data and baselines code at https:// github. com/ Soldelli/MAD.

关键词： computer vision Grounding Annotations Pipelines Natural languages Machine learning Benchmark testing

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Towards Fast and Accurate Real-World Depth Super-Resolution: Benchmark Dataset and Baseline

Towards Fast and Accurate Real-World Depth Super-Resolution:...

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ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： He, Lingzhi Zhu, Hongguang Li, Feng Bai, Huihui Cong, Runmin Zhang, Chunjie Lin, Chunyu Liu, Meiqin Zhao, Yao Beijing Jiaotong Univ Inst Informat Sci Beijing Key Lab Adv Informat Sci & Network Beijing 100044 Peoples R China

ISBN: (纸本)9781665445092

Depth maps obtained by commercial depth sensors are always in low-resolution, making it difficult to be used in various computer vision tasks. Thus, depth map super-resolution (SR) is a practical and valuable task, which upscales the depth map into high-resolution (HR) space. However, limited by the lack of real-world paired low-resolution (LR) and HR depth maps, most existing methods use downsampling to obtain paired training samples. To this end, we first construct a large-scale dataset named "RGB-D-D", which can greatly promote the study of depth map SR and even more depth-related real-world tasks. The "D-D" in our dataset represents the paired LR and HR depth maps captured from mobile phone and Lucid Helios respectively ranging from indoor scenes to challenging outdoor scenes. Besides, we provide a fast depth map super-resolution (FDSR) baseline, in which the high-frequency component adaptively decomposed from RGB image to guide the depth map SR. Extensive experiments on existing public datasets demonstrate the effectiveness and efficiency of our network compared with the state-of-the-art methods. Moreover, for the real-world LR depth maps, our algorithm can produce more accurate HR depth maps with clearer boundaries and to some extent correct the depth value errors.

关键词： Training computer vision Superresolution Focusing Estimation Benchmark testing Mobile handsets

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