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共 11423 条记录，以下是211-220 订阅

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PSD: Principled Synthetic-to-Real Dehazing Guided by Physical Priors

PSD: Principled Synthetic-to-Real Dehazing Guided by Physica...

引用

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

作者： Chen, Zeyuan Wang, Yangchao Yang, Yang Liu, Dong Univ Sci & Technol China Hefei Peoples R China Univ Elect Sci & Technol China Chengdu Peoples R China

ISBN: (纸本)9781665445092

Deep learning-based methods have achieved remarkable performance for image dehazing. However, previous studies are mostly focused on training models with synthetic hazy images, which incurs performance drop when the models are used for real-world hazy images. We propose a Principled Synthetic-to-real Dehazing (PSD) framework to improve the generalization performance of dehazing. Starting from a dehazing model backbone that is pre-trained on synthetic data, PSD exploits real hazy images to fine-tune the model in an unsupervised fashion. For the fine-tuning, we leverage several well-grounded physical priors and combine them into a prior loss committee. PSD allows for most of the existing dehazing models as its backbone, and the combination of multiple physical priors boosts dehazing significantly. Through extensive experiments, we demonstrate that our PSD framework establishes the new state-of-the-art performance for real-world dehazing, in terms of visual quality assessed by no-reference quality metrics as well as subjective evaluation and downstream task performance indicator.

关键词： Training Measurement Learning systems Visualization computer vision Data models pattern recognition

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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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Video Object Segmentation Using Global and Instance Embedding Learning

Video Object Segmentation Using Global and Instance Embeddin...

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

作者： Ge, Wenbin Lu, Xiankai Shen, Jianbing Beijing Inst Technol Beijing Peoples R China Shandong Univ Sch Software Jinan Peoples R China Incept Inst Artificial Intelligence Beijing Peoples R China

ISBN: (纸本)9781665445092

In this paper, we propose a feature embedding based video object segmentation (VOS) method which is simple, fast and effective. The current VOS task involves two main challenges: object instance differentiation and cross-frame instance alignment. Most state-of-the-art matching based VOS methods simplify this task into a binary segmentation task and tackle each instance independently. In contrast, we decompose the VOS task into two subtasks: global embedding learning that segments foreground objects of each frame in a pixel-to-pixel manner, and instance feature embedding learning that separates instances. The outputs of these two subtasks are fused to obtain the final instance masks quickly and accurately. Through using the relation among different instances per-frame as well as temporal relation across different frames, the proposed network learns to differentiate multiple instances and associate them properly in one feed-forward manner. Extensive experimental results on the challenging DAVIS[34] and YoutubeVOS [57] datasets show that our method achieves better performances than most counterparts in each case.

关键词： computer vision Annotations Object segmentation pattern recognition Task analysis

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MOS: Towards Scaling Out-of-distribution Detection for Large Semantic Space

MOS: Towards Scaling Out-of-distribution Detection for Large...

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

作者： Huang, Rui Li, Yixuan Univ Wisconsin Madison Dept Comp Sci Madison WI 53706 USA

ISBN: (纸本)9781665445092

Detecting out-of-distribution (OOD) inputs is a central challenge for safely deploying machine learning models in the real world. Existing solutions are mainly driven by small datasets, with low resolution and very few class labels (e.g., CIFAR). As a result, OOD detection for large-scale image classification tasks remains largely unexplored. In this paper, we bridge this critical gap by proposing a group-based OOD detection framework, along with a novel OOD scoring function termed MOS. Our key idea is to decompose the large semantic space into smaller groups with similar concepts, which allows simplifying the decision boundaries between in- vs. out-of-distribution data for effective OOD detection. Our method scales substantially better for high-dimensional class space than previous approaches. We evaluate models trained on ImageNet against four carefully curated OOD datasets, spanning diverse semantics. MOS establishes state-of-the-art performance, reducing the average FPR95 by 14.33% while achieving 6x speedup in inference compared to the previous best method.

关键词： Bridges computer vision Image resolution Semantics Machine learning Benchmark testing pattern recognition

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Using Shape to Categorize: Low-Shot Learning with an Explicit Shape Bias

Using Shape to Categorize: Low-Shot Learning with an Explici...

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

作者： Stojanov, Stefan Anh Thai Rehg, James M. Georgia Inst Technol Atlanta GA 30332 USA

ISBN: (纸本)9781665445092

It is widely accepted that reasoning about object shape is important for object recognition. However, the most powerful object recognition methods today do not explicitly make use of object shape during learning. In this work, motivated by recent developments in low-shot learning, findings in developmental psychology, and the increased use of synthetic data in computer vision research, we investigate how reasoning about 3D shape can be used to improve low-shot learning methods' generalization performance. We propose a new way to improve existing low-shot learning approaches by learning a discriminative embedding space using 3D object shape, and using this embedding by learning how to map images into it. Our new approach improves the performance of image-only low-shot learning approaches on multiple datasets. We also introduce Toys4K, a 3D object dataset with the largest number of object categories currently available, which supports low-shot learning.(1)

关键词： computer vision Three-dimensional displays Shape Psychology Cognition pattern recognition Object 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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Semantic-aware Knowledge Distillation for Few-Shot Class-Incremental Learning

Semantic-aware Knowledge Distillation for Few-Shot Class-Inc...

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

作者： Cheraghian, Ali Rahman, Shafin Fang, Pengfei Roy, Soumava Kumar Petersson, Lars Harandi, Mehrtash Australian Natl Univ Canberra ACT Australia Data61 CSIRO Sydney NSW Australia North South Univ Dhaka Bangladesh Monash Univ Melbourne Vic Australia

ISBN: (纸本)9781665445092

Few-shot class incremental learning (FSCIL) portrays the problem of learning new concepts gradually, where only a few examples per concept are available to the learner. Due to the limited number of examples for training, the techniques developed for standard incremental learning cannot be applied verbatim to FSCIL. In this work, we introduce a distillation algorithm to address the problem of FSCIL and propose to make use of semantic information during training. To this end, we make use of word embeddings as semantic information which is cheap to obtain and which facilitate the distillation process. Furthermore, we propose a method based on an attention mechanism on multiple parallel embeddings of visual data to align visual and semantic vectors, which reduces issues related to catastrophic forgetting. Via experiments on MiniImageNet, CUB200, and CIFAR100 dataset, we establish new state-of-the-art results by outperforming existing approaches.

关键词： Training Visualization computer vision Semantics Training data Power capacitors pattern recognition

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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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CGA-Net: Category Guided Aggregation for Point Cloud Semantic Segmentation

CGA-Net: Category Guided Aggregation for Point Cloud Semanti...

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

作者： Lu, Tao Wang, Limin Wu, Gangshan Nanjing Univ State Key Lab Novel Software Technol Nanjing Peoples R China

ISBN: (纸本)9781665445092

Previous point cloud semantic segmentation networks use the same process to aggregate features from neighbors of the same category and different categories. However, the joint area between two objects usually only occupies a small percentage in the whole scene. Thus the networks are well-trained for aggregating features from the same category point while not fully trained on aggregating points of different categories. To address this issue, this paper proposes to utilize different aggregation strategies between the same category and different categories. Specifically, it presents a customized module, termed as Category Guided Aggregation (CGA), where it first identifies whether the neighbors belong to the same category with the center point or not, and then handles the two types of neighbors with two carefully-designed modules. Our CGA presents a general network module and could be leveraged in any existing semantic segmentation network. Experiments on three different backbones demonstrate the effectiveness of our method.

关键词： computer vision Aggregates Semantics computer architecture pattern recognition

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RAFT-3D: Scene Flow using Rigid-Motion Embeddings

RAFT-3D: Scene Flow using Rigid-Motion Embeddings

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

作者： Teed, Zachary Deng, Jia Princeton Univ Princeton NJ 08544 USA

ISBN: (纸本)9781665445092

We address the problem of scene flow: given a pair of stereo or RGB-D video frames, estimate pixelwise 3D motion. We introduce RAFT-3D, a new deep architecture for scene flow. RAFT-3D is based on the RAFT model developed for optical flow but iteratively updates a dense field of pixelwise SE3 motion instead of 2D motion. A key innovation of RAFT-3D is rigid-motion embeddings, which represent a soft grouping of pixels into rigid objects. Integral to rigid-motion embeddings is Dense-SE3, a differentiable layer that enforces geometric consistency of the embeddings. Experiments show that RAFT-3D achieves state-of-the-art performance. On FlyingThings3D, under the twoview evaluation, we improved the best published accuracy (delta < 0.05) from 34.3% to 83.7%. On KITTI, we achieve an error of 5.77, outperforming the best published method (6.31), despite using no object instance supervision.

关键词： Technological innovation computer vision Three-dimensional displays Deep architecture pattern recognition Optical flow

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