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检索条件"任意字段=IEEE Conference on Computer Vision and Pattern Recognition"

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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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MaX-DeepLab: End-to-End Panoptic Segmentation with Mask Transformers

MaX-DeepLab: End-to-End Panoptic Segmentation with Mask Tran...

引用

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

作者： Wang, Huiyu Zhu, Yukun Adam, Hartwig Yuille, Alan Chen, Liang-Chieh Johns Hopkins Univ Baltimore MD 21218 USA Google Res Mountain View CA USA Google Mountain View CA 94043 USA

ISBN: (纸本)9781665445092

We present MaX-DeepLab, the first end-to-end model for panoptic segmentation. Our approach simplifies the current pipeline that depends heavily on surrogate sub-tasks and hand-designed components, such as box detection, non-maximum suppression, thing-stuff merging, etc. Although these sub-tasks are tackled by area experts, they fail to comprehensively solve the target task. By contrast, our MaX-DeepLab directly predicts class-labeled masks with a mask transformer, and is trained with a panoptic quality inspired loss via bipartite matching. Our mask transformer employs a dual-path architecture that introduces a global memory path in addition to a CNN path, allowing direct communication with any CNN layers. As a result, MaX-DeepLab shows a significant 7.1% PQ gain in the box-free regime on the challenging COCO dataset, closing the gap between box-based and box-free methods for the first time. A small variant of MaX-DeepLab improves 3.0% PQ over DETR with similar parameters and M-Adds. Furthermore, MaX-DeepLab, without test time augmentation, achieves new state-of-the-art 51.3% PQ on COCO test-dev set.

关键词： computer vision Merging Pipelines computer architecture Transformers pattern recognition Task analysis

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From Activation to Initialization: Scaling Insights for Optimizing Neural Fields

From Activation to Initialization: Scaling Insights for Opti...

引用

conference on computer vision and pattern recognition (CVPR)

作者： Hemanth Saratchandran Sameera Ramasinghe Simon Lucey Australian Institute of Machine Learning University of Adelaide Australia Amazon Australia

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

In the realm of computer vision, Neural Fields have gained prominence as a contemporary tool harnessing neural networks for signal representation. Despite the remarkable progress in adapting these networks to solve a variety of problems, the field still lacks a comprehensive theoretical framework. This article aims to address this gap by delving into the intricate interplay between initialization and activation, providing a foundational basis for the robust optimization of Neural Fields. Our theoretical insights reveal a deep-seated connection among network initialization, architectural choices, and the optimization process, emphasizing the need for a holistic approach when designing cutting-edge Neural Fields.

关键词： computer vision Scalability Neural networks pattern recognition Signal representation Optimization Convergence

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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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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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Source-Free Domain Adaptation for Semantic Segmentation

Source-Free Domain Adaptation for Semantic Segmentation

引用

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

作者： Liu, Yuang Zhang, Wei Wang, Jun East China Normal Univ Shanghai Peoples R China

ISBN: (纸本)9781665445092

Unsupervised Domain Adaptation (UDA) can tackle the challenge that convolutional neural network (CNN)-based approaches for semantic segmentation heavily rely on the pixel-level annotated data, which is labor-intensive. However, existing UDA approaches in this regard inevitably require the full access to source datasets to reduce the gap between the source and target domains during model adaptation, which are impractical in the real scenarios where the source datasets are private, and thus cannot be released along with the well-trained source models. To cope with this issue, we propose a source-free domain adaptation framework for semantic segmentation, namely SFDA, in which only a well-trained source model and an unlabeled target domain dataset are available for adaptation. SFDA not only enables to recover and preserve the source domain knowledge from the source model via knowledge transfer during model adaptation, but also distills valuable information from the target domain for self-supervised learning. The pixel- and patch-level optimization objectives tailored for semantic segmentation are seamlessly integrated in the framework. The extensive experimental results on numerous benchmark datasets highlight the effectiveness of our framework against the existing UDA approaches relying on source data.

关键词： Adaptation models Image segmentation computer vision Semantics Benchmark testing pattern recognition Convolutional neural networks

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Differentiable Diffusion for Dense Depth Estimation from Multi-view Images

Differentiable Diffusion for Dense Depth Estimation from Mul...

引用

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

作者： Khan, Numair Kim, Min H. Tompkin, James Brown Univ Providence RI 02912 USA Korea Adv Inst Sci & Technol Daejeon South Korea

ISBN: (纸本)9781665445092

We present a method to estimate dense depth by optimizing a sparse set of points such that their diffusion into a depth map minimizes a multi-view repmjection error from RGB supervision. We optimize point positions, depths, and weights with respect to the loss by differential splatting that models points as Gaussians with analytic transmittance. Further;we develop an efficient optimization routine that can simultaneously optimize the 50k+ points required for complex scene reconstruction. We validate our routine using ground truth data and show high reconstruction quality. Then, we apply this to light field and wider baseline images via self supervision, and show improvements in both average and outlier error for depth maps diffused from inaccurate sparse points. Finally, we compare qualitative and quantitative results to image processing and deep learning methods.

关键词： Deep learning computer vision Analytical models Estimation Rendering (computer graphics) Propagation losses Light fields

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Learning Position and Target Consistency for Memory-based Video Object Segmentation

Learning Position and Target Consistency for Memory-based Vi...

引用

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

作者： Hu, Li Zhang, Peng Zhang, Bang Pan, Pan Xu, Yinghui Jin, Rong Alibaba Grp Machine Intelligence Technol Lab Hangzhou Peoples R China

ISBN: (纸本)9781665445092

This paper studies the problem of semi-supervised video object segmentation(VOS). Multiple works have shown that memory-based approaches can be effective for video object segmentation. They are mostly based on pixel-level matching, both spatially and temporally. The main shortcoming of memory-based approaches is that they do not take into account the sequential order among frames and do not exploit object-level knowledge from the target. To address this limitation, we propose to Learn position and target Consistency framework for Memory-based video object segmentation, termed as LCM. It applies the memory mechanism to retrieve pixels globally, and meanwhile learns position consistency for more reliable segmentation. The learned location response promotes a better discrimination between target and distractors. Besides, LCM introduces an object-level relationship from the target to maintain target consistency, making LCM more robust to error drifting. Experiments show that our LCM achieves state-of-the-art performance on both DAVIS and Youtube-VOS benchmark. And we rank the 1st in the DAVIS 2020 challenge semi-supervised VOS task.

关键词： computer vision Object segmentation Benchmark testing pattern recognition Reliability Task analysis

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Firing pattern of Cells in the Hippocampus and Basolateral Amygdala during Novelty recognition 46

Firing Pattern of Cells in the Hippocampus and Basolateral A...

引用

46th Annual International conference of the ieee Engineering in Medicine and Biology Society, EMBC 2024

作者： Ren, Xiaoxin Wang, Yimeng Wang, Ling Yang, Jiajia Zheng, Chenguang Tianjin University Academy of Medical Engineering and Translational Medicine Medical College Tianjin China Tianjin Key Laboratory of Brain Science and Neuroengineering Tianjin China Haihe Laboratory of Brain-Computer Interaction and Human-Machine Integration Tianjin China

ISBN: (纸本)9798350371499

Novelty recognition is essential for exploring and collecting unknown but interesting information, in which multiple brain regions related to cognitive memory are involved. Dorsal hippocampus (dHPC) is revealed to be closely associated with spatial memory and basolateral amygdala (BLA) is reported recently to be related to state-dependent encoding of exploratory behavior. However, how the firing pattern of cells in these two brain regions responds to novelty dynamically remains unclear. In this study, we observed that the representations of cells in BLA exhibited shift towards novelty, whereas dHPC cells maintained strong stability after the introduction of novelty, which are both engaged in the execution of novel recognition. Our study may provide new evidence for underlying the internal mechanisms of perception of novelty in the brain. © 2024 ieee.

关键词： Brain

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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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