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Deep Occlusion-Aware Instance Segmentation with Overlapping BiLayers

Deep Occlusion-Aware Instance Segmentation with Overlapping ...

引用

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

作者： Ke, Lei Tai, Yu-Wing Tang, Chi-Keung Hong Kong Univ Sci & Technol Hong Kong Peoples R China Kuaishou Technol Beijing Peoples R China

ISBN: (数字)9781665445092

ISBN: (纸本)9781665445092

Segmenting highly-overlapping objects is challenging, because typically no distinction is made between real object contours and occlusion boundaries. Unlike previous two-stage instance segmentation methods, we model image formation as composition of two overlapping layers, and propose Bilayer Convolutional Network (BCNet), where the top GCN layer detects the occluding objects (occluder) and the bottom GCN layer infers partially occluded instance (occludee). The explicit modeling of occlusion relationship with bilayer structure naturally decouples the boundaries of both the occluding and occluded instances, and considers the interaction between them during mask regression. We validate the efficacy of bilayer decoupling on both one-stage and two-stage object detectors with different backbones and network layer choices. Despite its simplicity, extensive experiments on COCO and KINS show that our occlusion-aware BCNet achieves large and consistent performance gain especially for heavy occlusion cases. Code is available at https://***/lkeab/BCNet.

关键词： Convolutional codes Image segmentation computer vision Computational modeling Detectors Performance gain pattern recognition

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Disentangled Cycle Consistency for Highly-realistic Virtual Try-On

Disentangled Cycle Consistency for Highly-realistic Virtual ...

引用

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

作者： Ge, Chongjian Song, Yibing Ge, Yuying Yang, Han Liu, Wei Luo, Ping Univ Hong Kong Hong Kong Peoples R China Tencent AI Lab Bellevue WA 98004 USA Swiss Fed Inst Technol Zurich Switzerland Tencent Data Platform Bellevue WA USA

ISBN: (纸本)9781665445092

Image virtual try-on replaces the clothes on a person image with a desired in-shop clothes image. It is challenging because the person and the in-shop clothes are unpaired. Existing methods formulate virtual try-on as either in-painting or cycle consistency. Both of these two formulations encourage the generation networks to reconstruct the input image in a self-supervised manner. However, existing methods do not differentiate clothing and non-clothing regions. A straightforward generation impedes the virtual try-on quality because of the heavily coupled image contents. In this paper, we propose a Disentangled Cycle-consistency Try-On Network (DCTON). The DCTON is able to produce highly-realistic try-on images by disentangling important components of virtual try-on including clothes warping, skin synthesis, and image composition. Moreover, DCTON can be naturally trained in a self-supervised manner following cycle consistency learning. Extensive experiments on challenging benchmarks show that DCTON outperforms state-of-the-art approaches favorably.

关键词： computer vision Clothing Benchmark testing Skin pattern recognition Image reconstruction

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

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

引用

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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Animating General Image with Large Visual Motion Model

Animating General Image with Large Visual Motion Model

引用

conference on computer vision and pattern recognition (CVPR)

作者： Dengsheng Chen Xiaoming Wei Xiaolin Wei Meituan Beijing China

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

We present the pioneering Large Visual Motion Model (LVMM), meticulously engineered to analyze the intrinsic dynamics encapsulated within real-world imagery. Our model, fortified with a wealth of prior knowledge extracted from billions of image pairs, demonstrates promising results in predicting a diverse spectrum of scene dynamics. As a result, it can infuse any generic image with authentic dynamic effects, enhancing its visual allure. Project page: https://***/densechen/LVMM.

关键词： Visualization computer vision Analytical models Computational modeling Dynamics Predictive models pattern recognition

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SceneGen: Learning to Generate Realistic Traffic Scenes

SceneGen: Learning to Generate Realistic Traffic Scenes

引用

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

作者： Tan, Shuhan Wong, Kelvin Wang, Shenlong Manivasagam, Sivabalan Ren, Mengye Urtasun, Raquel Uber Adv Technol Grp Beijing Peoples R China Sun Yat Sen Univ Guangzhou Peoples R China Univ Toronto Toronto ON Canada

ISBN: (纸本)9781665445092

We consider the problem of generating realistic traffic scenes automatically. Existing methods typically insert actors into the scene according to a set of hand-crafted heuristics and are limited in their ability to model the true complexity and diversity of real traffic scenes, thus inducing a content gap between synthesized traffic scenes versus real ones. As a result, existing simulators lack the fidelity necessary to train and test self-driving vehicles. To address this limitation, we present SceneGen-a neural autoregressive model of traffic scenes that eschews the need for rules and heuristics. In particular, given the ego-vehicle state and a high definition map of surrounding area, SceneGen inserts actors of various classes into the scene and synthesizes their sizes, orientations, and velocities. We demonstrate on two large-scale datasets SceneGen's ability to faithfully model distributions of real traffic scenes. Moreover, we show that SceneGen coupled with sensor simulation can be used to train perception models that generalize to the real world.

关键词： computer vision Computational modeling Complexity theory pattern recognition

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TransFill: Reference-guided Image Inpainting by Merging Multiple Color and Spatial Transformations

TransFill: Reference-guided Image Inpainting by Merging Mult...

引用

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

作者： Zhou, Yuqian Barnes, Connelly Shechtman, Eli Amirghodsi, Sohrab UIUC IFP Champaign IL 61820 USA Adobe Res San Jose CA USA

ISBN: (纸本)9781665445092

Image inpainting is the task of plausibly restoring missing pixels within a hole region that is to be removed from a target image. Most existing technologies exploit patch similarities within the image, or leverage large-scale training data to fill the hole using learned semantic and texture information. However, due to the ill-posed nature of the inpainting task, such methods struggle to complete larger holes containing complicated scenes. In this paper, we propose TransFill, a multi-homography transformed fusion method to fill the hole by referring to another source image that shares scene contents with the target image. We first align the source image to the target image by estimating multiple homographies guided by different depth levels. We then learn to adjust the color and apply a pixel-level warping to each homography-warped source image to make it more consistent with the target. Finally, a pixel-level fusion module is learned to selectively merge the different proposals. Our method achieves state-of-the-art performance on pairs of images across a variety of wide baselines and color differences, and generalizes to user-provided image pairs.

关键词： computer vision Image color analysis Semantics Merging Training data pattern recognition Image restoration

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Panoramic Image Reflection Removal

Panoramic Image Reflection Removal

引用

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

作者： Hong, Yuchen Zheng, Qian Zhao, Lingran Jiang, Xudong Kot, Alex C. Shi, Boxin Peking Univ Dept Comp Sci & Technol NELVT Beijing Peoples R China Nanyang Technol Univ Sch Elect & Elect Engn Singapore Singapore Peking Univ Inst Artificial Intelligence Beijing Peoples R China Peng Cheng Lab Shenzhen Peoples R China

ISBN: (纸本)9781665445092

This paper studies the problem of panoramic image reflection removal, aiming at reliving the content ambiguity between reflection and transmission scenes. Although a partial view of the reflection scene is included in the panoramic image, it cannot be utilized directly due to its misalignment with the reflection-contaminated image. We propose a two-step approach to solve this problem, by first accomplishing geometric and photometric alignment for the reflection scene via a coarse-to-fine strategy, and then restoring the transmission scene via a recovery network. The proposed method is trained with a synthetic dataset and verified quantitatively with a real panoramic image dataset. The effectiveness of the proposed method is validated by the significant performance advantage over single image-based reflection removal methods and generalization capacity to limited-FoV scenarios captured by conventional camera or mobile phone users.

关键词： computer vision Cameras Reflection Mobile handsets pattern recognition Image restoration

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SetVAE: Learning Hierarchical Composition for Generative Modeling of Set-Structured Data

SetVAE: Learning Hierarchical Composition for Generative Mod...

引用

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

作者： Kim, Jinwoo Yoo, Jaehoon Lee, Juho Hong, Seunghoon Korea Adv Inst Sci & Technol Daejeon South Korea

ISBN: (纸本)9781665445092

Generative modeling of set-structured data, such as point clouds, requires reasoning over local and global structures at various scales. However, adopting multi-scale frameworks for ordinary sequential data to a set-structured data is nontrivial as it should be invariant to the permutation of its elements. In this paper, we propose SetVAE, a hierarchical variational autoencoder for sets. Motivated by recent progress in set encoding, we build SetVAE upon attentive modules that first partition the set and project the partition back to the original cardinality. Exploiting this module, our hierarchical VAE learns latent variables at multiple scales, capturing coarse-to-fine dependency of the set elements while achieving permutation invariance. We evaluate our model on point cloud generation task and achieve competitive performance to the prior arts with substantially smaller model capacity. We qualitatively demonstrate that our model generalizes to unseen set sizes and learns interesting subset relations without supervision.

关键词： computer vision Art Data models Cognition Encoding pattern recognition Task analysis

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Brain Controlled Communication system using Machine Learning techniques of computer vision 5

Brain Controlled Communication system using Machine Learning...

引用

5th ieee International conference on Advances in Computing, Communication Control and Networking, ICAC3N 2023

作者： Sabharwal, Munish Sultania, Shantam Jindal, Harsh Galgotias University Greater Noida India Wissen Technology Banglore India Chandigarh University Dept. Of Computer Science And Engineering Mohali India

ISBN: (纸本)9798350330861

Nowadays computers have become a necessity for all computers have made a great leap for us and with the help of that we are able to move to a golden age of Artificial Intelligence. Artificial Intelligence or A.I has huge advantages and potentials for making the lives easy and more accessible. However, we as a society have to keep on innovating and developing new innovations and products to help different aspects of the community. The proposed system uses a brain-controlled interface, eye tracking along with a camera and screen for communication and training. The system requires input in the form of an image captured with the help of a camera. After the image is taken as an input, it will be used to perform eye-tracking and eye-blink detection. Both of these actions will be performed by employing a machine learning algorithm. The algorithm will process a morse code entered by the user, and then will enable the speech functionality using built-in speakers. Thus, rendering the system speak out whatever the user wanted to convey. The proposed system follows a coordinated approach for eye pattern detection and mapping. It detects the gazing spot of the eyes, and calculates the midpoint of the eye gaze. As a result, it predicts the eye blink pattern. This eye pattern recognition system is proposed to help out people with disabilities who find it difficult to communicate. © 2023 ieee.

关键词： Cameras

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Learning to Segment Rigid Motions from Two Frames

Learning to Segment Rigid Motions from Two Frames

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

作者： Yang, Gengshan Ramanan, Deva Carnegie Mellon Univ Pittsburgh PA 15213 USA Argo AI Pittsburgh PA USA

ISBN: (纸本)9781665445092

Appearance-based detectors achieve remarkable performance on common scenes, benefiting from high-capacity models and massive annotated data, but tend to fail for scenarios that lack training data. Geometric motion segmentation algorithms, however, generalize to novel scenes, but have yet to achieve comparable performance to appearance-based ones, due to noisy motion estimations and degenerate motion configurations. To combine the best of both worlds, we propose a modular network, whose architecture is motivated by a geometric analysis of what independent object motions can be recovered from an egomotion field. It takes two consecutive frames as input and predicts segmentation masks for the background and multiple rigidly moving objects, which are then parameterized by 3D rigid transformations. Our method achieves state-of-the-art performance for rigid motion segmentation on KITTI and Sintel. The inferred rigid motions lead to a significant improvement for depth and scene flow estimation.

关键词： computer vision Three-dimensional displays Motion segmentation Motion estimation Estimation Training data computer architecture

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