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

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Grounding Everything: Emerging Localization Properties in vision-Language Transformers

Grounding Everything: Emerging Localization Properties in Vi...

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

作者： Bousselham, Walid Petersen, Felix Ferrari, Vittorio Kuehne, Hilde Univ Bonn Bonn Germany Goethe Univ Frankfurt Frankfurt Germany Stanford Univ Stanford CA 94305 USA Synthesia Io London England MIT IBM Watson AI Lab Cambridge MA USA

ISBN: (纸本)9798350353013;9798350353006

vision-language foundation models have shown remarkable performance in various zero-shot settings such as image retrieval, classification, or captioning. But so far, those models seem to fall behind when it comes to zero-shot localization of referential expressions and objects in images. As a result, they need to be fine-tuned for this task. In this paper, we show that pretrained vision-language (VL) models allow for zero-shot open-vocabulary object localization without any fine-tuning. To leverage those capabilities, we propose a Grounding Everything Module (GEM) that generalizes the idea of value-value attention introduced by CLIPSurgery [17] to a self-self attention path. We show that the concept of self-self attention corresponds to clustering, thus enforcing groups of tokens arising from the same object to be similar while preserving the alignment with the language space. To further guide the group formation, we propose a set of regularizations that allows the model to finally generalize across datasets and backbones. We evaluate the proposed GEM framework on various benchmark tasks and datasets for semantic segmentation. GEM not only outperforms other training-free open-vocabulary localization methods, but also achieves state-of-the-art results on the recently proposed OpenImagesV7 large-scale segmentation benchmark. (1)

关键词： CLIP open-vocabulary zero-shot segmentation vision-language model

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Residual Regression with Semantic Prior for Crowd Counting 32

Residual Regression with Semantic Prior for Crowd Counting

引用

32nd IEEE/CVF conference on computer vision and pattern recognition (CVPR)

作者： Wan, Jia Luo, Wenhan Wu, Baoyuan Chan, Antoni B. Liu, Wei City Univ Hong Kong Dept Comp Sci Hong Kong Peoples R China Tencent AI Lab Bellevue WA 98004 USA

ISBN: (纸本)9781728132938

Crowd counting is a challenging task due to factors such as large variations in crowdedness and severe occlusions. Although recent deep learning based counting algorithms have achieved a great progress, the correlation knowledge among samples and the semantic prior have not yet been fully exploited. In this paper, a residual regression framework is proposed for crowd counting harnessing the correlation information among samples. By incorporating such information into our network, we discover that more intrinsic characteristics can be learned by the network which thus generalizes better to unseen scenarios. Besides, we show how to effectively leverage the semantic prior to improve the performance of crowd counting. We also observe that the adversarial loss can be used to improve the quality of predicted density maps, thereby leading to an improvement in crowd counting. Experiments on public datasets demonstrate the effectiveness and generalization ability of the proposed method.

关键词： Scene Analysis and Understanding vision Applications and Systems

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3D Point Cloud Instance Segmentation of Lettuce Based on PartNet

3D Point Cloud Instance Segmentation of Lettuce Based on Par...

引用

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

作者： Wang, Luhan Zheng, Lihua Wang, Minjuan China Agr Univ Key Lab Smart Agr Syst Minist Educ Beijing 100083 Peoples R China

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Organ level instance segmentation (e.g., individual leaves) based on computer vision techniques is a key step in the measurement of plant phenotypes. Since plant organs, especially leaves, are self-occluded and emerged-occluded, single-view images affect the acquisition of some effective information. However, 3D global images contain much more plant morphological information than single-view images, and it is of great significance for plant phenotype research. In this paper, lettuce was taken as the research object, its 3D point cloud images were obtained and instance segmentation was carried out based on the deep learning method. The result showed that the 3D point cloud of each leaf was segmented and identified accurately. Specifically, we constructed a lettuce point cloud dataset consisting of 620 real and synthetic point clouds and fused them together to train a 3D instance segmentation network-PartNet, which directly takes 3D point clouds as input and its output is the instance segmentation results of leaves. The experimental results showed that, when tested with 40 point clouds in the validation set, the metric Average Precision (%) with IoU threshold being 0.25 reached 97.2%, and with IoU threshold being 0.5 reached 92.4% respectively, indicating that the constructed PartNet network has the potential to accurately segment the 3D point cloud leaf instances for lettuce.

关键词： Point cloud compression Training Image segmentation Solid modeling computer vision Three-dimensional displays Pipelines

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Nighttime Smartphone Reflective Flare Removal Using Optical Center Symmetry Prior

Nighttime Smartphone Reflective Flare Removal Using Optical ...

引用

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

作者： Dai, Yuekun Luo, Yihang Zhou, Shangchen Li, Chongyi Loy, Chen Change Nanyang Technol Univ S Lab Singapore Singapore

ISBN: (纸本)9798350301298

Reflective flare is a phenomenon that occurs when light reflects inside lenses, causing bright spots or a "ghosting effect" in photos, which can impact their quality. Eliminating reflective flare is highly desirable but challenging. Many existing methods rely on manually designed features to detect these bright spots, but they often fail to identify reflective flares created by various types of light and may even mistakenly remove the light sources in scenarios with multiple light sources. To address these challenges, we propose an optical center symmetry prior, which suggests that the reflective flare and light source are always symmetrical around the lens's optical center. This prior helps to locate the reflective flare's proposal region more accurately and can be applied to most smartphone cameras. Building on this prior, we create the first reflective flare removal dataset called BracketFlare, which contains diverse and realistic reflective flare patterns. We use continuous bracketing to capture the reflective flare pattern in the underexposed image and combine it with a normally exposed image to synthesize a pair of flare-corrupted and flare-free images. With the dataset, neural networks can be trained to remove the reflective flares effectively. Extensive experiments demonstrate the effectiveness of our method on both synthetic and real-world datasets.

关键词： Low-level vision

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DN-DETR: Accelerate DETR Training by Introducing Query DeNoising

DN-DETR: Accelerate DETR Training by Introducing Query DeNoi...

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

作者： Li, Feng Zhang, Hao Liu, Shilong Guo, Jian Ni, Lionel M. Zhang, Lei Hong Kong Univ Sci & Technol Hong Kong Peoples R China Int Digital Econ Acad IDEA Shenzhen Peoples R China Tsinghua Univ Beijing Peoples R China Hong Kong Univ Sci & Technol Guangzhou Peoples R China IDEA Shenzhen Peoples R China

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

We present in this paper a novel denoising training method to speedup DETR (DEtection TRansformer) training and offer a deepened understanding of the slow convergence issue of DETR-like methods. We show that the slow convergence results from the instability of bipartite graph matching which causes inconsistent optimization goals in early training stages. To address this issue, except for the Hungarian loss, our method additionally feeds ground-truth bounding boxes with noises into Transformer decoder and trains the model to reconstruct the original boxes, which effectively reduces the bipartite graph matching difficulty and leads to a faster convergence. Our method is universal and can be easily plugged into any DETR-like methods by adding dozens of lines of code to achieve a remarkable improvement. As a result, our DN-DETR results in a remarkable improvement (+1.9AP) under the same setting and achieves the best result (AP 43.4 and 48.6 with 12 and 50 epochs of training respectively) among DETR-like methods with ResNet-50 backbone. Compared with the baseline under the same setting, DN-DETR achieves comparable performance with 50% training epochs. Code is available at https://***/FengLi-ust/DN-DETR.

关键词： Training Codes Machine vision Noise reduction Transformers pattern recognition Decoding

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End-to-End Learnable Geometric vision by Backpropagating PnP Optimization

End-to-End Learnable Geometric Vision by Backpropagating PnP...

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

作者： Chen, Bo Parra, Alvaro Cao, Jiewei Li, Nan Chin, Tat-Jun Univ Adelaide Adelaide SA Australia Shenzhen Univ Shenzhen Peoples R China

ISBN: (纸本)9781728171685

Deep networks excel in learning patterns from large amounts of data. On the other hand, many geometric vision tasks are specified as optimization problems. To seamlessly combine deep learning and geometric vision, it is vital to perform learning and geometric optimization end-to-end. Towards this aim, we present BPnP, a novel network module that backpropagates gradients through a Perspective-nPoints (PnP) solver to guide parameter updates of a neural network. Based on implicit differentiation, we show that the gradients of a "self-contained" PnP solver can be derived accurately and efficiently, as if the optimizer block were a differentiable function. We validate BPnP by incorporating it in a deep model that can learn camera intrinsics, camera extrinsics (poses) and 3D structure from training datasets. Further, we develop an end-to-end trainable pipeline for object pose estimation, which achieves greater accuracy by combining feature-based heatmap losses with 2D-3D reprojection errors. Since our approach can be extended to other optimization problems, our work helps to pave the way to perform learnable geometric vision in a principled manner. Our PyTorch implementation of BPnP is available on http://***/BoChenYS/BPnP.

关键词： Cameras

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Semantic Channels for Fast Pedestrian Detection 29

Semantic Channels for Fast Pedestrian Detection

引用

2016 IEEE conference on computer vision and pattern recognition (CVPR)

作者： Costea, Arthur Daniel Nedevschi, Sergiu Tech Univ Cluj Napoca Image Proc & Pattern Recognit Res Ctr Cluj Napoca Romania

ISBN: (纸本)9781467388511

Pedestrian detection and semantic segmentation are high potential tasks for many real-time applications. However most of the top performing approaches provide state of art results at high computational costs. In this work we propose a fast solution for achieving state of art results for both pedestrian detection and semantic segmentation. As baseline for pedestrian detection we use sliding windows over cost efficient multiresolution filtered LUV+HOG channels. We use the same channels for classifying pixels into eight semantic classes. Using short range and long range multiresolution channel features we achieve more robust segmentation results compared to traditional codebook based approaches at much lower computational costs. The resulting segmentations are used as additional semantic channels in order to achieve a more powerful pedestrian detector. To also achieve fast pedestrian detection we employ a multiscale detection scheme based on a single flexible pedestrian model and a single image scale. The proposed solution provides competitive results on both pedestrian detection and semantic segmentation benchmarks at 8 FPS on CPU and at 15 FPS on GPU, being the fastest top performing approach.

关键词： Semantics

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Tri-Modal Biometric Fusion for Human Authentication by Tracking Differential Code pattern 5

Tri-Modal Biometric Fusion for Human Authentication by Track...

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5th National conference on computer vision, pattern recognition, Image Processing and Graphics (NCVPRIPG)

作者： Nigam, Aditya Gupta, Phalguni Indian Inst Technol Mandi Sch Comp & Elect Engn Mandi 175001 India NITTTR CSE Dept Kolkata 700106 India

ISBN: (纸本)9781467385640

Human authentication can now be seen as a crucial social problem. In this paper a multimodal authentication system is presented which is highly reliable and fuses iris, finger-knuckleprint and palmprint image matching scores. Segmented ROI are preprocessed using DCP (Differential Code pattern) to obtain robust corner features. Later they are matched using the GOF (Global Optical Flow) based dissimilarity measure. The proposed system has been tested on Casia Interval and Lamp iris, PolyU finger-knuckle-print and PolyU and Casia palmprint, public databases. The proposed system has shown good performance over all unimodal databases while over multimodal (fusion of all three) databases it has shown perfect performance (i:e: CRR = 100% with EER = 0%).

关键词： Database systems

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Semi-automatic 3D Object Keypoint Annotation and Detection for the Masses 26

Semi-automatic 3D Object Keypoint Annotation and Detection f...

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26th International conference on pattern recognition / 8th International Workshop on Image Mining - Theory and Applications (IMTA)

作者： Blomqvist, Kenneth Chung, Jen Jen Ott, Lionel Siegwart, Roland Swiss Fed Inst Technol Autonomous Syst Lab Zurich Switzerland

ISBN: (数字)9781665490627

ISBN: (纸本)9781665490627

Creating computer vision datasets requires careful planning and lots of time and effort. In robotics research, we often have to use standardized objects, such as the YCB object set, for tasks such as object tracking, pose estimation, grasping and manipulation, as there are datasets and pre-learned methods available for these objects. This limits the impact of our research since learning-based computer vision methods can only be used in scenarios that are supported by existing datasets. In this work, we present a full object keypoint tracking toolkit, encompassing the entire process from data collection, labeling, model learning and evaluation. We present a semi-automatic way of collecting and labeling datasets using a wrist mounted camera on a standard robotic arm. Using our toolkit and method, we are able to obtain a working 3D object keypoint detector and go through the whole process of data collection, annotation and learning in just a couple hours of active time.

关键词： Wrist computer vision Three-dimensional displays Annotations Robot vision systems Pose estimation Data collection

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IMDeception: Grouped Information Distilling Super-Resolution Network

IMDeception: Grouped Information Distilling Super-Resolution...

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

作者： Ayazoglu, Mustafa Aselsan Res Ankara Turkey

ISBN: (纸本)9781665487399

Single-Image-Super-Resolution (SISR) is a classical computer vision problem that has benefited from the recent advancements in deep learning methods, especially the advancements of convolutional neural networks (CNN). Although state-of-the-art methods improve the performance of SISR on several datasets, direct application of these networks for practical use is still an issue due to heavy computational load. For this purpose, recently, researchers have focused on more efficient and high-performing network structures. Information multi-distilling network (IMDN) is one of the highly efficient SISR networks with high performance and low computational load. IMDN achieves this efficiency with various mechanisms such as Intermediate Information Collection (IIC), working in a global setting, Progressive Refinement Module (PRM), and Contrast Aware Channel Attention (CCA), employed in a local setting. These mechanisms, however, do not equally contribute to the efficiency and performance of IMDN. In this work, we propose the Global Progressive Refinement Module (GPRM) as a less parameter-demanding alternative to the IIC module for feature aggregation. To further decrease the number of parameters and floating point operations per second (FLOPS), we also propose Grouped Information Distilling Blocks (GIDB). Using the proposed structures, we design an efficient SISR network called IMDeception. Experiments reveal that the proposed network performs on par with state-of-the-art models despite having a limited number of parameters and FLOPS. Furthermore, using grouped convolutions as a building block of GIDB increases room for further optimization during deployment. To show its potential, the proposed model was deployed on NVIDIA Jetson Xavier AGX and it has been shown that it can run in real-time on this edge device.

关键词： Deep learning computer vision conferences Superresolution Real-time systems Hardware Timing

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