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Incremental Few-Shot Instance Segmentation

Incremental Few-Shot Instance Segmentation

引用

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

作者： Ganea, Dan Andrei Boom, Bas Poppe, Ronald Univ Utrecht Utrecht Netherlands Cyclomedia Technol Zaltbommel Netherlands

ISBN: (纸本)9781665445092

Few-shot instance segmentation methods are promising when labeled training data for novel classes is scarce. However, current approaches do not facilitate flexible addition of novel classes. They also require that examples of each class are provided at train and test time, which is memory intensive. In this paper, we address these limitations by presenting the first incremental approach to few-shot instance segmentation: iMTFA. We learn discriminative embeddings for object instances that are merged into class representatives. Storing embedding vectors rather than images effectively solves the memory overhead problem. We match these class embeddings at the RoI-level using cosine similarity. This allows us to add new classes without the need for further training or access to previous training data. In a series of experiments, we consistently outperform the current state-of-the-art. Moreover, the reduced memory requirements allow us to evaluate, for the first time, few-shot instance segmentation performance on all classes in COCO jointly(1).

关键词： Location awareness Training Image segmentation computer vision Memory management Training data Transfer functions

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4D Hyperspectral Photoacoustic Data Restoration with Reliability Analysis

4D Hyperspectral Photoacoustic Data Restoration with Reliabi...

引用

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

作者： Liao, Weihang Subpa-Asa, Art Zheng, Yinqiang Sato, Imari Tokyo Inst Technol Tokyo Japan Natl Inst Informat Tokyo Japan Univ Tokyo Tokyo Japan

ISBN: (纸本)9781665445092

Hyperspectral photoacoustic (HSPA) spectroscopy is an emerging bi-modal imaging technology that is able to show the wavelength-dependent absorption distribution of the interior of a 3D volume. However, HSPA devices have to scan an object exhaustively in the spatial and spectral domains;and the acquired data tend to suffer from complex noise. This time-consuming scanning process and noise severely affects the usability of HSPA. It is therefore critical to examine the feasibility of 4D HSPA data restoration from an incomplete and noisy observation. In this work, we present a data reliability analysis for the depth and spectral domain. On the basis of this analysis, we explore the inherent data correlations and develop a restoration algorithm to recover 4D HSPA cubes. Experiments on real data verify that the proposed method achieves satisfactory restoration results.

关键词： Legged locomotion Spectroscopy Correlation Three-dimensional displays pattern recognition Reliability Noise measurement

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The Blessings of Unlabeled Background in Untrimmed Videos

The Blessings of Unlabeled Background in Untrimmed Videos

引用

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

作者： Liu, Yuan Chen, Jingyuan Chen, Zhenfang Deng, Bing Huang, Jianqiang Zhang, Hanwang Alibaba Grp Hangzhou Peoples R China Univ Hong Kong Hong Kong Peoples R China Nanyang Technol Univ Singapore Singapore

ISBN: (纸本)9781665445092

Weakly-supervised Temporal Action Localization (WTAL) aims to detect the action segments with only video-level action labels in training. The key challenge is how to distinguish the action of interest segments from the background, which is unlabelled even on the video-level. While previous works treat the background as "curses", we consider it as "blessings". Specifically, we first use causal analysis to point out that the common localization errors are due to the unobserved confounder that resides ubiquitously in visual recognition. Then, we propose a Temporal Smoothing PCA-based (TS-PCA) deconfounder, which exploits the unlabelled background to model an observed substitute for the unobserved confounder, to remove the confounding effect. Note that the proposed deconfounder is model-agnostic and non-intrusive, and hence can be applied in any WTAL method without model re-designs. Through extensive experiments on four state-of-the-art WTAL methods, we show that the deconfounder can improve all of them on the public datasets: THUMOS-14 and ActivityNet-1.3(1).

关键词： Location awareness Training Visualization computer vision Smoothing methods Computational modeling pattern recognition

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DAP: Detection-Aware Pre-training with Weak Supervision

DAP: Detection-Aware Pre-training with Weak Supervision

引用

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

作者： Zhong, Yuanyi Wang, Jianfeng Wang, Lijuan Peng, Jian Wang, Yu-Xiong Zhang, Lei Univ Illinois Urbana IL 61801 USA Microsoft Redmond WA USA

ISBN: (纸本)9781665445092

This paper presents a detection-aware pre-training (DAP) approach, which leverages only weakly-labeled classification-style datasets (e.g., ImageNet) for pre-training, but is specifically tailored to benefit object de- tection tasks. In contrast to the widely used image classification-based pre-training (e.g., on ImageNet), which does not include any location-related training tasks, we transform a classification dataset into a detection dataset through a weakly supervised object localization method based on Class Activation Maps to directly pre-train a detector, making the pre-trained model location-aware and capable of predicting bounding boxes. We show that DAP can outperform the traditional classification pre-training in terms of both sample efficiency and convergence speed in downstream detection tasks including VOC and COCO. In particular, DAP boosts the detection accuracy by a large margin when the number of examples in the downstream task is small.

关键词： Training Location awareness computer vision Transforms Object detection Detectors Predictive models

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Learning Deep Latent Variable Models by Short-Run MCMC Inference with Optimal Transport Correction

Learning Deep Latent Variable Models by Short-Run MCMC Infer...

引用

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

作者： An, Dongsheng Xie, Jianwen Li, Ping Baidu Res Cognit Comp Lab 10900 NE 8th St Bellevue WA 98004 USA

ISBN: (纸本)9781665445092

Learning latent variable models with deep top-down architectures typically requires inferring the latent variables for each training example based on the posterior distribution of these latent variables. The inference step typically relies on either time-consuming long-run Markov chain Monte Carlo (MCMC) sampling or a separate inference model for variational learning. In this paper, we propose to use a short-run MCMC, such as a short-run Langevin dynamics, as an approximate flow-based inference engine. The bias existing in the output distribution of the non-convergent short-run Langevin dynamics is corrected by the optimal transport (OT), which aims at transforming the biased distribution produced by the finite-step MCMC to the prior distribution with a minimum transport cost. Our experiments not only verify the effectiveness of the OT correction for the short-run MCMC, but also demonstrate that the latent variable model trained by the proposed strategy performs better than the variational auto-encoder (VAE) in terms of image reconstruction/generation and anomaly detection.

关键词： Training Monte Carlo methods Costs Image synthesis Markov processes pattern recognition Task analysis

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A Hybrid ANN-SNN Architecture for Low-Power and Low-Latency Visual Perception

A Hybrid ANN-SNN Architecture for Low-Power and Low-Latency ...

引用

ieee computer Society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Asude Aydin Mathias Gehrig Daniel Gehrig Davide Scaramuzza Robotics and Perception Group University of Zurich Switzerland

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Spiking Neural Networks (SNNs) are a class of bio-inspired neural networks that promise to bring low-power and low-latency inference to edge-devices through the use of asynchronous and sparse processing. However, being temporal models, SNNs depend heavily on expressive states to generate predictions on par with classical artificial neural networks (ANNs). These states converge only after long transient time periods, and quickly decay in the absence of input data, leading to higher latency, power consumption, and lower accuracy. In this work, we address this issue by initializing the state with an auxiliary ANN running at a low rate. The SNN then uses the state to generate predictions with high temporal resolution until the next initialization phase. Our hybrid ANN-SNN model thus combines the best of both worlds: It does not suffer from long state transients and state decay thanks to the ANN, and can generate predictions with high temporal resolution, low latency, and low power thanks to the SNN. We show for the task of event-based 2D and 3D human pose estimation that our method consumes 88% less power with only a 4% decrease in performance compared to its fully ANN counterparts when run at the same inference rate. Moreover, when compared to SNNs, our method achieves a 74% lower error. This research thus provides a new understanding of how ANNs and SNNs can be used to maximize their respective benefits. Code: https://***/uzh-rpg/hybrid_ann_snn

关键词： Accuracy Three-dimensional displays Pose estimation computer architecture Spiking neural networks Predictive models Low latency communication

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Learning Graphs for Knowledge Transfer with Limited Labels

Learning Graphs for Knowledge Transfer with Limited Labels

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

作者： Ghosh, Pallabi Saini, Nirat Davis, Larry S. Shrivastava, Abhinav Univ Maryland College Pk MD 20742 USA

ISBN: (纸本)9781665445092

Fixed input graphs are a mainstay in approaches that utilize Graph Convolution Networks (GCNs) for knowledge transfer. The standard paradigm is to utilize relationships in the input graph to transfer information using GCNs from training to testing nodes in the graph;for example, the semi-supervised, zero-shot, and few-shot learning setups. We propose a generalized framework for learning and improving the input graph as part of the standard GCN-based learning setup. Moreover, we use additional constraints between similar and dissimilar neighbors for each node in the graph by applying triplet loss on the intermediate layer output. We present results of semi-supervised learning on Citeseer, Cora, and Pubmed benchmarking datasets, and zero/few-shot action recognition on UCF101 and HMDB51 datasets, significantly outperforming current approaches. We also present qualitative results visualizing the graph connections that our approach learns to update.

关键词： Training Visualization computer vision Convolution Semisupervised learning Benchmark testing pattern recognition

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Semi-Supervised Semantic Segmentation with Cross Pseudo Supervision

Semi-Supervised Semantic Segmentation with Cross Pseudo Supe...

引用

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

作者： Chen, Xiaokang Yuan, Yuhui Zeng, Gang Wang, Jingdong Peking Univ Key Lab Machine Percept MOE Beijing Peoples R China Microsoft Res Asia Beijing Peoples R China Microsoft Res Beijing Peoples R China

ISBN: (纸本)9781665445092

In this paper, we study the semi-supervised semantic segmentation problem via exploring both labeled data and extra unlabeled data. We propose a novel consistency regularization approach, called cross pseudo supervision (CPS). Our approach imposes the consistency on two segmentation networks perturbed with different initialization for the same input image. The pseudo one-hot label map, output from one perturbed segmentation network, is used to supervise the other segmentation network with the standard cross-entropy loss, and vice versa. The CPS consistency has two roles: encourage high similarity between the predictions of two perturbed networks for the same input image, and expand training data by using the unlabeled data with pseudo labels. Experiment results show that our approach achieves the state-of-the-art semi-supervised segmentation performance on Cityscapes and PASCAL VOC 2012.

关键词： Training Image segmentation computer vision Semantics Training data pattern recognition Standards

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DER: Dynamically Expandable Representation for Class Incremental Learning

DER: Dynamically Expandable Representation for Class Increme...

引用

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

作者： Yan, Shipeng Xie, Jiangwei He, Xuming ShanghaiTech Univ Sch Informat Sci & Technol Shanghai Peoples R China Shanghai Engn Res Ctr Intelligent Vis & Imaging Shanghai Peoples R China Chinese Acad Sci Shanghai Inst Microsyst & Informat Technol Shanghai Peoples R China Univ Chinese Acad Sci Beijing Peoples R China

ISBN: (纸本)9781665445092

We address the problem of class incremental learning, which is a core step towards achieving adaptive vision intelligence. In particular, we consider the task setting of incremental learning with limited memory and aim to achieve better stability-plasticity trade-off. To this end, we propose a novel two-stage learning approach that utilizes a dynamically expandable representation for more effective incremental concept modeling. Specifically, at each incremental step, we freeze the previously learned representation and augment it with additional feature dimensions from a new learnable feature extractor. This enables us to integrate new visual concepts with retaining learned knowledge. We dynamically expand the representation according to the complexity of novel concepts by introducing a channel-level mask-based pruning strategy. Moreover, we introduce an auxiliary loss to encourage the model to learn diverse and discriminate features for novel concepts. We conduct extensive experiments on the three class incremental learning benchmarks and our method consistently outperforms other methods with a large margin.(1)

关键词： Visualization computer vision Adaptation models Benchmark testing Feature extraction pattern recognition Complexity theory

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SelfAugment: Automatic Augmentation Policies for Self-Supervised Learning

SelfAugment: Automatic Augmentation Policies for Self-Superv...

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

作者： Reed, Colorado J. Metzger, Sean Srinivas, Aravind Darrell, Trevor Keutzer, Kurt Univ Calif Berkeley BAIR Dept Comp Sci Berkeley CA 94720 USA Weill Neurosci Inst Grad Grp Bioengn Berkeley UCSF San Francisco CA USA UCSF Neurol Surg San Francisco CA USA

ISBN: (纸本)9781665445092

A common practice in unsupervised representation learning is to use labeled data to evaluate the quality of the learned representations. This supervised evaluation is then used to guide critical aspects of the training process such as selecting the data augmentation policy. However, guiding an unsupervised training process through supervised evaluations is not possible for real-world data that does not actually contain labels (which may be the case, for example, in privacy sensitive fields such as medical imaging). Therefore, in this work we show that evaluating the learned representations with a self-supervised image rotation task is highly correlated with a standard set of supervised evaluations (rank correlation > 0.94). We establish this correlation across hundreds of augmentation policies, training settings, and network architectures and provide an algorithm (SelfAugment) to automatically and efficiently select augmentation policies without using supervised evaluations. Despite not using any labeled data, the learned augmentation policies perform comparably with augmentation policies that were determined using exhaustive supervised evaluations.

关键词： Training computer vision Data privacy Correlation Image recognition Network architecture pattern recognition

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