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Panoptic-PolarNet: Proposal-free LiDAR Point Cloud Panoptic Segmentation

Panoptic-PolarNet: Proposal-free LiDAR Point Cloud Panoptic ...

引用

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

作者： Zhou, Zixiang Zhang, Yang Foroosh, Hassan Univ Cent Florida Dept Comp Sci Orlando FL 32816 USA Waymo LLC Mountain View CA USA

ISBN: (纸本)9781665445092

Panoptic segmentation presents a new challenge in exploiting the merits of both detection and segmentation, with the aim of unifying instance segmentation and semantic segmentation in a single framework. However, an efficient solution for panoptic segmentation in the emerging domain of LiDAR point cloud is still an open research problem and is very much under-explored. In this paper, we present a fast and robust LiDAR point cloud panoptic segmentation framework, referred to as Panoptic-PolarNet. We learn both semantic segmentation and class-agnostic instance clustering in a single inference network using a polar Bird's Eye View (BEV) representation, enabling us to circumvent the issue of occlusion among instances in urban street scenes. To improve our network's learnability, we also propose an adapted instance augmentation technique and a novel adversarial point cloud pruning method. Our experiments show that Panoptic-PolarNet outperforms the baseline methods on SemanticKITTI and nuScenes datasets with an almost real-time inference speed. Panoptic-PolarNet achieved 54.1% PQ in the public SemanticKITTI panoptic segmentation leaderboard and leading performance for the validation set of nuScenes.

关键词： computer vision Laser radar Semantics Real-time systems pattern recognition Complexity theory

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Dynamic Conceptional Contrastive Learning for Generalized Category Discovery

Dynamic Conceptional Contrastive Learning for Generalized Ca...

引用

conference on computer vision and pattern recognition (cvpr)

作者： Nan Pu Zhun Zhong Nicu Sebe The Department of Information Engineering and Computer Science University of Trento Trento Italy

Generalized category discovery (GCD) is a recently proposed open-world problem, which aims to automatically cluster partially labeled data. The main challenge is that the unlabeled data contain instances that are not only from known categories of the labeled data but also from novel categories. This leads traditional novel category discovery (NCD) methods to be incapacitated for GCD, due to their assumption of unlabeled data are only from novel categories. One effective way for GCD is applying self-supervised learning to learn discriminate representation for unlabeled data. However, this manner largely ignores underlying relationships between instances of the same concepts (e.g., class, super-class, and sub-class), which results in inferior representation learning. In this paper, we propose a Dynamic Conceptional Contrastive Learning (DCCL)framework, which can effectively improve clustering accuracy by alternately estimating underlying visual conceptions and learning conceptional representation. In addition, we design a dynamic conception generation and update mechanism, which is able to ensure consistent conception learning and thus further facilitate the optimization of DCCL. Extensive experiments show that DCCL achieves new state-of-the-art performances on six generic and fine-grained visual recognition datasets, especially on fine-grained ones. For example, our method significantly surpasses the best competitor by 16.2% on the new classes for the CUB-200 dataset. Code is available at https://***/TPCD/DCCL

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CompositeTasking: Understanding Images by Spatial Composition of Tasks

CompositeTasking: Understanding Images by Spatial Compositio...

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

作者： Popovic, Nikola Paudel, Danda Pani Probst, Thomas Sun, Guolei Van Gool, Luc Swiss Fed Inst Technol Comp Vis Lab Zurich Switzerland Katholieke Univ Leuven ESAT PSI VISICS Leuven Belgium

ISBN: (纸本)9781665445092

We define the concept of CompositeTasking as the fusion of multiple, spatially distributed tasks, for various aspects of image understanding. Learning to perform spatially distributed tasks is motivated by the frequent availability of only sparse labels across tasks, and the desire for a compact multi-tasking network. To facilitate CompositeTasking, we introduce a novel task conditioning model - a single encoder-decoder network that performs multiple, spatially varying tasks at once. The proposed network takes an image and a set of pixel-wise dense task requests as inputs, and performs the requested prediction task for each pixel. Moreover, we also learn the composition of tasks that needs to be performed according to some CompositeTasking rules, which includes the decision of where to apply which task. It not only offers us a compact network for multi-tasking, but also allows for task-editing. Another strength of the proposed method is demonstrated by only having to supply sparse supervision per task. The obtained results are on par with our baselines that use dense supervision and a multi-headed multi-tasking design. The source code will be made publicly available at ***/nikola3794/composite-tasking.

关键词： computer vision Codes Multitasking pattern recognition Task analysis

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Research on Radar PRI Modulation pattern recognition Based on Recurrent Neural Network 4

Research on Radar PRI Modulation Pattern Recognition Based o...

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4th International conference on computer vision, Image and Deep Learning, CVIDL 2023

作者： Qu, Wei Yao, Gang Meng, Lei Space Engineering University Department of Electronics and Optics Beijing China Beijing Institute of Tracking and Telecommunications Technology Beijing China Beijing Remote Sensing Information Research Instituteline Beijing China

ISBN: (纸本)9798350326444

In this paper, the Recurrent Neural Network (RNN) model is introduced into the identification of radar emitters, which verifies the feasibility of RNN application to radar emitter identification, and the design process is simplified and the radar emitter identification method is easy to apply in engineering. Based on the radar emission source recognition model, the Pulse Repetition Interval(PRI) modulation pattern recognition research based on recurrent neural network was carried out. Firstly, aiming at the problems of gradient vanishing and gradient explosion in RNN, a PRI modulation pattern recognition method based on LSTM is proposed, and the recognition method process is introduced in detail. According to the characteristics of pulse train data, a data preprocessing method suitable for recurrent neural network is proposed. Finally, simulation verifies the effectiveness of the proposed method and tests the performance of the algorithm under different interference conditions. © 2023 ieee.

关键词： Long short-term memory

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Reconsidering Representation Alignment for Multi-view Clustering

Reconsidering Representation Alignment for Multi-view Cluste...

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

作者： Trosten, Daniel J. Lokse, Sigurd Jenssen, Robert Kampffmeyer, Michael UiT Arctic Univ Norway Dept Phys & Technol Tromso Norway UiT Machine Learning Grp Tromso Norway

ISBN: (纸本)9781665445092

Aligning distributions of view representations is a core component of today's state of the art models for deep multi-view clustering. However, we identify several drawbacks with naively aligning representation distributions. We demonstrate that these drawbacks both lead to less separable clusters in the representation space, and inhibit the model's ability to prioritize views. Based on these observations, we develop a simple baseline model for deep multi-view clustering. Our baseline model avoids representation alignment altogether, while performing similar to, or better than, the current state of the art. We also expand our baseline model by adding a contrastive learning component. This introduces a selective alignment procedure that preserves the model's ability to prioritize views. Our experiments show that the contrastive learning component enhances the baseline model, improving on the current state of the art by a large margin on several datasets(1).

关键词： computer vision Computational modeling Adversarial machine learning pattern recognition

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Generative Hierarchical Features from Synthesizing Images

Generative Hierarchical Features from Synthesizing Images

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

作者： Xu, Yinghao Shen, Yujun Zhu, Jiapeng Yang, Ceyuan Zhou, Bolei Chinese Univ Hong Kong Hong Kong Peoples R China

ISBN: (纸本)9781665445092

Generative Adversarial Networks (GANs) have recently advanced image synthesis by learning the underlying distribution of the observed data. However, how the features learned from solving the task of image generation are applicable to other vision tasks remains seldom explored. In this work, we show that learning to synthesize images can bring remarkable hierarchical visual features that are generalizable across a wide range of applications. Specifically, we consider the pre-trained StyleGAN generator as a learned loss function and utilize its layer-wise representation to train a novel hierarchical encoder. The visual feature produced by our encoder, termed as Generative Hierarchical Feature (GH-Feat), has strong transferability to both generative and discriminative tasks, including image editing, image harmonization, image classification, face verification, landmark detection, and layout prediction. Extensive qualitative and quantitative experimental results demonstrate the appealing performance of GH-Feat.

关键词： Visualization computer vision Image synthesis Face recognition Layout Generative adversarial networks Feature extraction

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Animating Pictures with Eulerian Motion Fields

Animating Pictures with Eulerian Motion Fields

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

作者： Holynski, Aleksander Curless, Brian Seitz, Steven M. Szeliski, Richard Univ Washington Seattle WA 98195 USA

ISBN: (纸本)9781665445092

In this paper, we demonstrate a fully automatic method for converting a still image into a realistic animated looping video. We target scenes with continuous fluid motion, such as flowing water and billowing smoke. Our method relies on the observation that this type of natural motion can be convincingly reproduced from a static Eulerian motion description, i.e. a single, temporally constant flow field that defines the immediate motion of a particle at a given 2D location. We use an image-to-image translation network to encode motion priors of natural scenes collected from online videos, so that for a new photo, we can synthesize a corresponding motion field. The image is then animated using the generated motion through a deep warping technique: pixels are encoded as deep features, those features are warped via Eulerian motion, and the resulting warped feature maps are decoded as images. In order to produce continuous, seamlessly looping video textures, we propose a novel video looping technique that flows features both forward and backward in time and then blends the results. We demonstrate the effectiveness and robustness of our method by applying it to a large collection of examples including beaches, waterfalls, and flowing rivers.

关键词： computer vision Fluids Animation Robustness Rivers pattern recognition

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PixMatch: Unsupervised Domain Adaptation via Pixelwise Consistency Training

PixMatch: Unsupervised Domain Adaptation via Pixelwise Consi...

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

作者： Melas-Kyriazi, Luke Manrai, Arjun K. Harvard Univ Cambridge MA 02138 USA Harvard Univ Boston Childrens Hosp Boston MA 02115 USA

ISBN: (纸本)9781665445092

Unsupervised domain adaptation is a promising technique for semantic segmentation and other computer vision tasks for which large-scale data annotation is costly and time-consuming. In semantic segmentation, it is attractive to train models on annotated images from a simulated (source) domain and deploy them on real (target) domains. In this work, we present a novel framework for unsupervised domain adaptation based on the notion of target-domain consistency training. Intuitively, our work is based on the idea that in order to perform well on the target domain, a model's output should be consistent with respect to small perturbations of inputs in the target domain. Specifically, we introduce a new loss term to enforce pixelwise consistency between the model's predictions on a target image and a perturbed version of the same image. In comparison to popular adversarial adaptation methods, our approach is simpler, easier to implement, and more memory-efficient during training. Experiments and extensive ablation studies demonstrate that our simple approach achieves remarkably strong results on two challenging synthetic-to-real benchmarks, GTA5-to-Cityscapes and SYNTHIA-to-Cityscapes.

关键词： Training Adaptation models Image segmentation computer vision Perturbation methods Computational modeling Semantics

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Anchor-Free Person Search

Anchor-Free Person Search

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

作者： Yan, Yichao Li, Jinpeng Qin, Jie Bai, Song Liao, Shengcai Liu, Li Zhu, Fan Shao, Ling Incept Inst Artificial Intelligence IIAI Abu Dhabi U Arab Emirates Univ Oxford Oxford England

ISBN: (纸本)9781665445092

Person search aims to simultaneously localize and identify a query person from realistic, uncropped images, which can be regarded as the unified task of pedestrian detection and person re-identification (re-id). Most existing works employ two-stage detectors like Faster-RCNN, yielding encouraging accuracy but with high computational overhead. In this work, we present the Feature-Aligned Person Search Network (AlignPS), the first anchor-free framework to efficiently tackle this challenging task AlignPS explicitly addresses the major challenges, which we summarize as the misalignment issues in different levels (i.e., scale, region, and task), when accommodating an anchor-free detector for this task More specifically, we propose an aligned feature aggregation module to generate more discriminative and robust feature embeddings by following a "re-id first" principle. Such a simple design directly improves the baseline anchor-free model on CUHK-SYSU by more than 20% in mAR Moreover AlignPS outperforms state-of-the-art two-stage methods, with a higher speed. The code is available at https://***/daodaofr/AlignPS.

关键词： computer vision Codes Search methods Detectors Feature extraction pattern recognition Task analysis

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Holistic 3D Human and Scene Mesh Estimation from Single View Images

Holistic 3D Human and Scene Mesh Estimation from Single View...

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

作者： Weng, Zhenzhen Yeung, Serena Stanford Univ Stanford CA 94305 USA

ISBN: (纸本)9781665445092

The 3D world limits the human body pose and the human body pose conveys information about the surrounding objects. Indeed, from a single image of a person placed in an indoor scene, we as humans are adept at resolving ambiguities of the human pose and room layout through our knowledge of the physical laws and prior perception of the plausible object and human poses. However, few computer vision models fully leverage this fact. In this work, we propose a holistically trainable model that perceives the 3D scene from a single RGB image, estimates the camera pose and the room layout, and reconstructs both human body and object meshes. By imposing a set of comprehensive and sophisticated losses on all aspects of the estimations, we show that our model outperforms existing human body mesh methods and indoor scene reconstruction methods. To the best of our knowledge, this is the first model that outputs both object and human predictions at the mesh level, and performs joint optimization on the scene and human poses.

关键词： Measurement Solid modeling computer vision Three-dimensional displays Computational modeling Biological system modeling Pose estimation

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