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检索条件"任意字段=2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2023"

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Noisy Correspondence Learning with Meta Similarity Correction

Noisy Correspondence Learning with Meta Similarity Correctio...

引用

conference on computer vision and pattern recognition (CVPR)

作者： Haochen Han Kaiyao Miao Qinghua Zheng Minnan Luo School of Computer Science and Technology Xi'an Jiaotong University China School of Cyber Science and Engineering Xi'an Jiaotong University China

Despite the success of multimodal learning in crossmodal retrieval task, the remarkable progress relies on the correct correspondence among multimedia data. However, collecting such ideal data is expensive and time-consuming. In practice, most widely used datasets are harvested from the Internet and inevitably contain mismatched pairs. Training on such noisy correspondence datasets causes performance degradation because the cross-modal retrieval methods can wrongly enforce the mismatched data to be similar. To tackle this problem, we propose a Meta Similarity Correction Network (MSCN) to provide reliable similarity scores. We view a binary classification task as the meta-process that encourages the MSCN to learn discrimination from positive and negative meta-data. To further alleviate the influence of noise, we design an effective data purification strategy using meta-data as prior knowledge to remove the noisy samples. Extensive experiments are conducted to demonstrate the strengths of our method in both synthetic and real-world noises, including Flickr30K, MS-COCO, and Conceptual Captions. Our code is publicly available. 1 1 https://***/hhc1997/MSCN

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BEVHeight: A Robust Framework for vision-based Roadside 3D Object Detection

BEVHeight: A Robust Framework for Vision-based Roadside 3D O...

引用

conference on computer vision and pattern recognition (CVPR)

作者： Lei Yang Kaicheng Yu Tao Tang Jun Li Kun Yuan Li Wang Xinyu Zhang Peng Chen State Key Laboratory of Automotive Safety and Energy Tsinghua University Autonomous Driving Lab Alibaba Group Shenzhen Campus Sun Yat-sen University Center for Machine Learning Research Peking University

While most recent autonomous driving system focuses on developing perception methods on ego-vehicle sensors, people tend to overlook an alternative approach to leverage intelligent roadside cameras to extend the perception ability beyond the visual range. We discover that the state-of-the-art vision-centric bird's eye view detection methods have inferior performances on roadside cameras. This is because these methods mainly focus on recovering the depth regarding the camera center, where the depth difference between the car and the ground quickly shrinks while the distance increases. In this paper, we propose a simple yet effective approach, dubbed BEVHeight, to address this issue. In essence, instead of predicting the pixel-wise depth, we regress the height to the ground to achieve a distance-agnostic formulation to ease the optimization process of camera-only perception methods. On popular 3D detection benchmarks of roadside cameras, our method surpasses all previous vision-centric methods by a significant margin. The code is available at https://***/ADLab-AutoDrive/BEVHeight.

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Residual Feature Pyramid Network for Enhancement of Vascular patterns

Residual Feature Pyramid Network for Enhancement of Vascular...

引用

ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Ketan Kotwal Sé bastien Marcel Idiap Research Institute Switzerland University of Lausanne Switzerland

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487405

The accuracy of finger vein recognition systems gets degraded due to low and uneven contrast between veins and surroundings, often resulting in poor detection of vein patterns. We propose a finger-vein enhancement technique, ResFPN (Residual Feature Pyramid Network), as a generic preprocessing method agnostic to the recognition pipeline. A bottom-up pyramidal architecture using the novel Structure Detection block (SDBlock) facilitates extraction of veins of varied widths. Using a feature aggregation module (FAM), we combine these vein-structures, and train the proposed ResFPN for detection of veins across scales. With enhanced presentations, our experiments indicate a reduction upto 5% in the average recognition errors for commonly used recognition pipeline over two publicly available datasets. These improvements are persistent even in cross-dataset scenario where the dataset used to train the ResFPN is different from the one used for recognition.

关键词： Histograms Protocols Veins Pipelines Fingers computer architecture Network architecture

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Hot-started NAS for Task-specific Embedded Applications

Hot-started NAS for Task-specific Embedded Applications

引用

ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Lotte Hendrickx Wiebe Van Ranst Toon Goedemé EAVISE-PSI-ESAT KU Leuven Sint-Katelijne-Waver Belgium

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487405

Neural architecture search (NAS) has proven its worth in discovering new neural networks. Combining the possibility to satisfy multiple objectives in one search, it is especially useful for getting the most out of embedded devices with limited resources. However, research into small and efficient neural networks precedes NAS. We investigate the influence of combining this pre-existing knowledge with NAS techniques, for which we propose to hot-start the NAS search with a human-designed optimal network. Our experiments show that doing so speeds up the NAS process significantly, but the resulting optimal model at the end is only marginally better. Since embedded devices are often used for a specific task, we also explore the impact of using a task-specific dataset in the NAS process. Our experiments demonstrate that for a constrained problem, a smaller network can be found as compared to a general problem.

关键词： Knowledge engineering computer vision conferences Neural networks Size measurement Search problems pattern recognition

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ProxyFormer: Proxy Alignment Assisted Point Cloud Completion with Missing Part Sensitive Transformer

ProxyFormer: Proxy Alignment Assisted Point Cloud Completion...

引用

conference on computer vision and pattern recognition (CVPR)

作者： Shanshan Li Pan Gao Xiaoyang Tan Mingqiang Wei College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics

Problems such as equipment defects or limited view-points will lead the captured point clouds to be incomplete. Therefore, recovering the complete point clouds from the partial ones plays an vital role in many practical tasks, and one of the keys lies in the prediction of the missing part. In this paper, we propose a novel point cloud completion approach namely ProxyFormer that divides point clouds into existing (input) and missing (to be predicted) parts and each part communicates information through its proxies. Specifically, we fuse information into point proxy via feature and position extractor, and generate features for missing point proxies from the features of existing point proxies. Then, in order to better perceive the position of missing points, we design a missing part sensitive transformer, which converts random normal distribution into reasonable position information, and uses proxy alignment to refine the missing proxies. It makes the predicted point proxies more sensitive to the features and positions of the missing part, and thus makes these proxies more suitable for subsequent coarse-to-fine processes. Experimental results show that our method outperforms state-of-the-art completion networks on several benchmark datasets and has the fastest inference speed.

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Deep Fair Clustering via Maximizing and Minimizing Mutual Information: Theory, Algorithm and Metric

Deep Fair Clustering via Maximizing and Minimizing Mutual In...

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conference on computer vision and pattern recognition (CVPR)

作者： Pengxin Zeng Yunfan Li Peng Hu Dezhong Peng Jiancheng Lv Xi Peng College of Computer Science Sichuan University China

Fair clustering aims to divide data into distinct clusters while preventing sensitive attributes (e.g., gender, race, RNA sequencing technique) from dominating the clustering. Although a number of works have been conducted and achieved huge success recently, most of them are heuristical, and there lacks a unified theory for algorithm design. In this work, we fill this blank by developing a mutual information theory for deep fair clustering and accordingly designing a novel algorithm, dubbed FCMI. In brief, through maximizing and minimizing mutual information, FCMI is designed to achieve four characteristics highly expected by deep fair clustering, i.e., compact, balanced, and fair clusters, as well as informative features. Besides the contributions to theory and algorithm, another contribution of this work is proposing a novel fair clustering metric built upon information theory as well. Unlike existing evaluation metrics, our metric measures the clustering quality and fairness as a whole instead of separate manner. To verify the effectiveness of the proposed FCMI, we conduct experiments on six benchmarks including a single-cell RNA-seq atlas compared with 11 state-of-the-art methods in terms of five metrics. The code could be accessed from https://***.

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IterativePFN: True Iterative Point Cloud Filtering

IterativePFN: True Iterative Point Cloud Filtering

引用

conference on computer vision and pattern recognition (CVPR)

作者： Dasith de Silva Edirimuni Xuequan Lu Zhiwen Shao Gang Li Antonio Robles-Kelly Ying He School of Information Technology Deakin University School of Computer Science and Technology China University of Mining and Technology Defense Science and Technology Group Australia School of Computer Science and Engineering Nanyang Technological University

The quality of point clouds is often limited by noise introduced during their capture process. Consequently, a fundamental 3D vision task is the removal of noise, known as point cloud filtering or denoising. State-of-the-art learning based methods focus on training neural networks to infer filtered displacements and directly shift noisy points onto the underlying clean surfaces. In high noise conditions, they iterate the filtering process. However, this iterative filtering is only done at test time and is less effective at ensuring points converge quickly onto the clean surfaces. We propose IterativePFN (iterative point cloud filtering network), which consists of multiple IterationModules that model the true iterative filtering process internally, within a single network. We train our IterativePFn network using a novel loss function that utilizes an adaptive ground truth target at each iteration to capture the relationship between intermediate filtering results during training. This ensures that the filtered results converge faster to the clean surfaces. Our method is able to obtain better performance compared to state-of-the-art methods. The source code can be found at: https://***/ddsediri/IterativePFN

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SOFEA: A Non-iterative and Robust Optical Flow Estimation Algorithm for Dynamic vision Sensors

SOFEA: A Non-iterative and Robust Optical Flow Estimation Al...

引用

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

作者： Low, Weng Fei Gao, Zhi Xiang, Cheng Ramesh, Bharath Natl Univ Singapore 1 Inst Hlth Singapore Singapore

ISBN: (纸本)9781728193601

We introduce the single-shot optical flow estimation algorithm (SOFEA) to non-iteratively compute the continuous-time flow information of events produced from bio-inspired cameras such as the dynamic vision sensor (DVS). The output of a DVS is a stream of asynchronous spikes ("events"), transmitted at very minimal latency (110 mu s), caused by local brightness changes. Due to this unconventional output, a continuous representation of events over time is invaluable to most applications using the DVS. To this end, SOFEA consolidates the spatio-temporal information on the surface of active events for flow estimation in a single-shot manner, as opposed to iterative methods in the literature. In contrast to previous works, this is also the first principled method towards finding locally optimal set of neighboring events for plane fitting using an adaptation of Prim's algorithm. Consequently, SOFEA produces flow estimates that are more accurate across a wide variety of scenes compared to state-of-the-art methods. A direct application of such flow estimation is rendering sharp event images using the set of active events at a given time, which is further demonstrated and compared to existing works (source code will be made available at our homepage after the review process).

关键词： Iterative methods

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Moire pattern Removal via Attentive Fractal Network

Moire Pattern Removal via Attentive Fractal Network

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

作者： Xu, Dejia Chu, Yihao Sun, Qingyan Peking Univ Beijing Peoples R China Beijing Univ Posts & Telecommun Beijing Peoples R China Beijing Jiaotong Univ Beijing Peoples R China

ISBN: (纸本)9781728193601

Moire patterns are commonly seen artifacts when taking photos of screens and other objects with high-frequency textures. It's challenging to remove the moire patterns considering its complex color and shape. In this work, we propose an Attentive Fractal Network to effectively solve this problem. First, we construct each Attentive Fractal Block with progressive feature fusion and channel-wise attention guidance. The network is then fractally stacked with the block on each of its levels. Second, to further boost the performance, we adopt a two-stage augmented refinement strategy. With these designs, our method wins the burst demoireing track and achieves second place in single image demoireing and single image deblurring tracks in NTIRE20 Challenges. Extensive experiments demonstrate the superiority of our method for moire pattern removal compared to existing state-of-the-art methods, and prove the effectiveness of its each component. We will publicly release our code and trained weights on https://***/ir1d/AFN.

关键词： Textures

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DualVector: Unsupervised Vector Font Synthesis with Dual-Part Representation

DualVector: Unsupervised Vector Font Synthesis with Dual-Par...

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conference on computer vision and pattern recognition (CVPR)

作者： Ying-Tian Liu Zhifei Zhang Yuan-Chen Guo Matthew Fisher Zhaowen Wang Song-Hai Zhang Department of Computer Science and Technology BNRist Tsinghua University Adobe Research

Automatic generation of fonts can be an important aid to typeface design. Many current approaches regard glyphs as pixelated images, which present artifacts when scaling and inevitable quality losses after vectorization. On the other hand, existing vector font synthesis methods either fail to represent the shape concisely or require vector supervision during training. To push the quality of vector font synthesis to the next level, we propose a novel dual-part representation for vector glyphs, where each glyph is modeled as a collection of closed “positive” and “negative” path pairs. The glyph contour is then obtained by boolean operations on these paths. We first learn such a representation only from glyph images and devise a subsequent contour refinement step to align the contour with an image representation to further enhance details. Our method, named DualVector, outperforms state-of-the-art methods in vector font synthesis both quantitatively and qualitatively. Our synthesized vector fonts can be easily converted to common digital font formats like TrueType Font for practical use. The code is released at https://***/thuliu-yt16/dualvector.

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