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Comprehensive Visual Features and Pseudo Labeling for Robust Natural Language-based Vehicle Retrieval

Comprehensive Visual Features and Pseudo Labeling for Robust...

引用

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

作者： Bach Hoang Ngo Dat Thanh Nguyen Nhat-Tuong Do-Tran Phuc Pham Huy Thien Minh-Hung An Tuan-Ngoc Nguyen Loi Nguyen Hoang Vinh Dinh Nguyen Quang-Vinh Dinh AI Vietnam Research Vietnam FPT Telecom Vietnam Faculty of Computer Science and Engineering Ho Chi Minh City University of Technology School of Computing and Information Technology Eastern International University Vietnam

Vehicle retrieval has become crucial for public transportation and intelligent transportation systems due to the exponential development of large-scale transportation videos. Vehicle re-identification and vehicle tracking are the main components of most vision-based vehicle recovery systems. Unfortunately, the limited amount of information provided by traffic video feeds limits the vision-based vehicle retrieval algorithm’s efficacy. Therefore, this article proposes a contrastive cross-modal vehicle retrieval approach to maximize the complementarity of natural language and visual representations. An efficient method to fuse multiple input image features is also proposed to extract comprehensive information from various vehicles along with pseudo labeling and efficient post-processing techniques to enhance retrieval accuracy. The proposed method achieved the 3rd ranking of Mean Reciprocal Rank (MRR) score of 0.4795 on the test set for the Challenge Track 2: Tracked-Vehicle Retrieval by Natural Language Descriptions 2023. Source code for the proposed approaches is openly accessible at https://***/anminhhung/AICity-2023-Track2.

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CSCO: Connectivity Search of Convolutional Operators

CSCO: Connectivity Search of Convolutional Operators

引用

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

作者： Tunhou Zhang Shiyu Li Hsin-Pai Cheng Feng Yan Hai Li Yiran Chen ECE Department Duke University Durham NC Qualcomm AI Research San Diego CA Department of Computer Science University of Houston Houston TX

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Exploring dense connectivity of convolutional operators establishes critical "synapses" to communicate feature vectors from different levels and enriches the set of transformations on computer vision applications. Yet, even with heavy-machinery approaches such as Neural Architecture Search (NAS), discovering effective connectivity patterns requires tremendous efforts due to either constrained connectivity design space or a sub-optimal exploration process induced by an unconstrained search space. In this paper, we propose CSCO, a novel paradigm that fabricates effective connectivity of convolutional operators with minimal utilization of existing design motifs and further utilizes the discovered wiring to construct high-performing ConvNets. CSCO guides the exploration via a neural predictor as a surrogate of the ground-truth performance. We introduce Graph Isomorphism as data augmentation to improve sample efficiency and propose a Metropolis-Hastings Evolutionary Search (MH-ES) to evade locally optimal architectures and advance search quality. Results on ImageNet show ∼ 0.6% performance improvement over hand-crafted and NAS-crafted dense connectivity. Our code is publicly available here.

关键词： Convolutional codes Wiring computer vision Architecture Buildings computer architecture Data augmentation

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DGBD: Depth Guided Branched Diffusion for Comprehensive Controllability in Multi-View Generation

DGBD: Depth Guided Branched Diffusion for Comprehensive Cont...

引用

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

作者： Hovhannes Margaryan Daniil Hayrapetyan Wenyan Cong Zhangyang Wang Humphrey Shi Picsart AI Research (PAIR) UT Austin SHI Labs @ Georgia Tech Oregon & UIUC

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

This paper presents an innovative approach to multi-view generation that can be comprehensively controlled over both perspectives (viewpoints) and non-perspective attributes (such as depth maps). Our controllable dual-branch pipeline, named Depth Guided Branched Diffusion (DGBD), leverages depth maps and perspective information to generate images from alternative viewpoints while preserving shape and size fidelity. In the first DGBD branch, we fine-tune a pre-trained diffusion model on multi-view data, introducing a regularized batch-aware self-attention mechanism for multi-view consistency and generalization. Direct control over perspective is then achieved through cross-attention conditioned on camera position. Meanwhile, the second DGBD branch introduces non-perspective control using depth maps. Qualitative and quantitative experiments validate the effectiveness of our approach, surpassing or matching the performance of state-of-the-art novel view and multi-view synthesis methods.

关键词： Geometry computer vision Shape conferences Pipelines Text to image Cameras

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Fast Camera Image Denoising on Mobile GPUs with Deep Learning, Mobile AI 2021 Challenge: Report

Fast Camera Image Denoising on Mobile GPUs with Deep Learnin...

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

作者： Ignatov, Andrey Byeoung-Su, Kim Timofte, Radu Pouget, Angeline Song, Fenglong Li, Cheng Xiao, Shuai Fu, Zhongqian Maggioni, Matteo Huang, Yibin Cheng, Shen Lu, Xin Zhou, Yifeng Chen, Liangyu Liu, Donghao Zhang, Xiangyu Fan, Haoqiang Sun, Jian Liu, Shuaicheng Kwon, Minsu Lee, Myungje Yoo, Jaeyoon Kang, Changbeom Wang, Shinjo Huang, Bin Zhou, Tianbao Liu, Shuai Lei, Lei Feng, Chaoyu Huang, Liguang Lei, Zhikun Chen, Feifei Swiss Fed Inst Technol Comp Vis Lab Zurich Switzerland Samsung Elect Suwon South Korea AI Witchlabs Zollikerberg Switzerland Huawei Noahs Ark Lab Hong Kong Peoples R China Megvii Beijing Peoples R China ENERZAi Seoul South Korea Xiaomi AI Lab Beijing Peoples R China North China Univ Technol Beijing Peoples R China Xiaomi Inc Beijing Peoples R China Goodix Technol Algorithm Dept Shenzhen Peoples R China

ISBN: (纸本)9781665448994

Image denoising is one of the most critical problems in mobile photo processing. While many solutions have been proposed for this task, they are usually working with synthetic data and are too computationally expensive to run on mobile devices. To address this problem, we introduce the first Mobile AI challenge, where the target is to develop an end-to-end deep learning-based image denoising solution that can demonstrate high efficiency on smartphone GPUs. For this, the participants were provided with a novel large-scale dataset consisting of noisy-clean image pairs captured in the wild. The runtime of all models was evaluated on the Samsung Exynos 2100 chipset with a powerful Mali GPU capable of accelerating floating-point and quantized neural networks. The proposed solutions are fully compatible with any mobile GPU and are capable of processing 480p resolution images under 40-80 ms while achieving high fidelity results. A detailed description of all models developed in the challenge is provided in this paper.

关键词： Runtime Image resolution Neural networks Graphics processing units Mobile handsets pattern recognition Noise measurement

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Masked vision Transformers for Hyperspectral Image Classification

Masked Vision Transformers for Hyperspectral Image Classific...

引用

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

作者： Linus Scheibenreif Michael Mommert Damian Borth AIML Lab School of Computer Science University of St. Gallen

Transformer architectures have become state-of-the-art models in computer vision and natural language processing. To a significant degree, their success can be attributed to self-supervised pre-training on large scale unlabeled datasets. This work investigates the use of self-supervised masked image reconstruction to advance transformer models for hyperspectral remote sensing imagery. To facilitate self-supervised pre-training, we build a large dataset of unlabeled hyperspectral observations from the EnMAP satellite and systematically investigate modifications of the vision transformer architecture to optimally leverage the characteristics of hyperspectral data. We find significant improvements in accuracy on different land cover classification tasks over both standard vision and sequence transformers using (i) blockwise patch embeddings, (ii) spatialspectral self-attention, (iii) spectral positional embeddings and (iv) masked self-supervised pre-training 1 . The resulting model outperforms standard transformer architectures by +5% accuracy on a labeled subset of our EnMAP data and by +15% on Houston2018 hyperspectral dataset, making it competitive with a strong 3D convolutional neural network baseline. In an ablation study on label-efficiency based on the Houston2018 dataset, self-supervised pre-training significantly improves transformer accuracy when little labeled training data is available. The self-supervised model outperforms randomly initialized transformers and the 3D convolutional neural network by +7-8% when only 0.1-10% of the training labels are available.

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Demographic Bias Effects on Face Image Synthesis

Demographic Bias Effects on Face Image Synthesis

引用

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

作者： Roberto Leyva Victor Sanchez Gregory Epiphaniou Carsten Maple WMG University of Warwick UK Computer Science University of Warwick UK

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Face image synthesis has shown remarkable progress in recent years. However, the effect that the demographics of the data used to train synthesizers has on the generation of new face images remains an open question. This paper investigates the effects of the training set demographics in the face image synthesis task. To this end, we propose a strategy that allows synthesizing face images for specific groups of people with a high visual quality. The strategy uses an unsupervised learning approach to discover groups of people in the training set based on Bayesian inference via a probabilistic mixture model. If labels are available to define the groups, our strategy can also exploit such information in lieu of unsupervised learning. Once the groups are defined, our strategy trains a Generative Adversarial Network on each group to generate new face images with specific characteristics. Our results show remarkable performance in terms of image quality compared to several state-of-the-art baselines. More importantly, our strategy allows synthesizing face images with reduced demographic biases.

关键词： Training Image quality Visualization Image synthesis Face recognition Synthesizers Mixture models

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Isolated Sign Language recognition with Multi-Scale Spatial-Temporal Graph Convolutional Networks

Isolated Sign Language Recognition with Multi-Scale Spatial-...

引用

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

作者： Vazquez-Enriquez, Manuel Alba-Castro, Jose L. Docio-Fernandez, Laura Rodriguez-Banga, Eduardo Univ Vigo AtlanTTic Res Ctr Vigo 36310 Spain

ISBN: (纸本)9781665448994

Isolated Sign Language recognition (ISLR) fits nicely in the domain of problems that can be handled by graph-structured spatial-temporal algorithms. A recent multi-scale spatial-temporal graph convolution operator, MS-G3D, takes advantage of the semantic connectivity among non-neighbor nodes of the graph in a flexible temporal scale, which results in improved performance in classical Human Action recognition datasets. In this work, we present a solution for ISLR using a skeleton graph that includes body and finger joints and makes use of this specific property of MS-G3D, which seems crucial to capture the internal relationship among semantically connected distant nodes in sign language dynamics. To complete the analysis, we compare the results with a 3D-CNN architecture, S3D, already used for SLR, and fuse it with MS-G3D. The performance achieved on the AUTSL dataset shows that MS-G3D alone stands out as a viable technique for ISLR. In fact, the improvement after fusing with a 3D-CNN approach, at least on this medium-scale dataset, appears marginal. The transfer learning capability of the trained models is also explored using pre-training with the larger WLASL dataset and post-training with the smaller LSE_UVIGO dataset. The classification performance based on the MS-G3D model over AUTSL does not benefit from pre-training with WLASL, but the performance on the more similarly acquired LSE UVIGO dataset improves significantly from fine-tuning the MS-G3D AUTSL model.

关键词： computer vision Fuses Convolution Assistive technology Computational modeling conferences Transfer learning

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Beyond 3D Siamese Tracking: A Motion-Centric Paradigm for 3D Single Object Tracking in Point Clouds

Beyond 3D Siamese Tracking: A Motion-Centric Paradigm for 3D...

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

作者： Zheng, Chaoda Yan, Xu Zhang, Haiming Wang, Baoyuan Cheng, Shenghui Cui, Shuguang Li, Zhen Chinese Univ Hong Kong Shenzhen Shenzhen Peoples R China Future Network Intelligence Inst Shenzhen Peoples R China Shenzhen Res Inst Big Data Shenzhen Peoples R China Xiaobing AI Beijing Peoples R China Westlake Univ Hangzhou Peoples R China

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

3D single object tracking (3D SOT) in LiDAR point clouds plays a crucial role in autonomous driving. Current approaches all follow the Siamese paradigm based on appearance matching. However, LiDAR point clouds are usually textureless and incomplete, which hinders effective appearance matching. Besides, previous methods greatly overlook the critical motion clues among targets. In this work, beyond 3D Siamese tracking, we introduce a motion-centric paradigm to handle 3D SOT from a new perspective. Following this paradigm, we propose a matching free two-stage tracker M-2-Track. At the 1st-stage, M-2-Track localizes the target within successive frames via motion transformation. Then it refines the target box through motion-assisted shape completion at the 2nd-stage. Extensive experiments confirm that M-2-Track significantly outperforms previous state-of-the-arts on three large-scale datasets while running at 57FPS (similar to 8%, similar to 17% and similar to 22% precision gains on KITTI, NuScenes, and Waymo Open Dataset respectively). Further analysis verifies each component's effectiveness and shows the motioncentric paradigm's promising potential when combined with appearance matching.

关键词： Point cloud compression computer vision Three-dimensional displays Laser radar Target tracking Shape Image matching

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Is Our Continual Learner Reliable? Investigating Its Decision Attribution Stability through SHAP Value Consistency

Is Our Continual Learner Reliable? Investigating Its Decisio...

引用

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

作者： Yusong Cai Shimou Ling Liang Zhang Lili Pan Hongliang Li University of Electronic Science and Technology of China Chengdu China

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

In this work, we identify continual learning (CL) methods’ inherent differences in sequential decision attribution. In the sequential learning process, inconsistent decision attribution may undermine the interpretability of a continual learner. However, existing CL evaluation metrics, as well as current interpretability methods, cannot measure the decision attribution stability of a continual learner. To bridge the gap, we introduce Shapley value, a well-known decision attribution theory, and define SHAP value consistency (SHAPC) to measure the consistency of a continual learner’s decision attribution. Furthermore, we define the mean and the variance of SHAPC values, namely SHAPC-Mean and SHAPC-Var, to jointly evaluate the decision attribution stability of continual learners over sequential tasks. On Split CIFAR-10, Split CIFAR-100, and Split TinyImageNet, we compare the decision attribution stability of different CL methods using the proposed metrics, providing a new perspective for evaluating their reliability.

关键词： Measurement Continuing education computer vision Current measurement conferences Decision making Reliability theory

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BMAD: Benchmarks for Medical Anomaly Detection

BMAD: Benchmarks for Medical Anomaly Detection

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

作者： Jinan Bao Hanshi Sun Hanqiu Deng Yinsheng He Zhaoxiang Zhang Xingyu Li Electrical and Computer Engineering University of Alberta Electrical and Computer Engineering Carnegie Mellon University

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Anomaly detection (AD) is a fundamental research problem in machine learning and computer vision, with practical applications in industrial inspection, video surveillance, and medical diagnosis. In the field of medical imaging, AD plays a crucial role in identifying anomalies that may indicate rare diseases or conditions. However, despite its importance, there is currently a lack of a universal and fair benchmark for evaluating AD methods on medical images, which hinders the development of more generalized and robust AD methods in this specific domain. To address this gap, we present a comprehensive evaluation benchmark for assessing AD methods on medical images. This benchmark consists of six reorganized datasets from five medical domains (i.e. brain MRI, liver CT, retinal OCT, chest X-ray, and digital histopathology) and three key evaluation metrics, and includes a total of fifteen state-of-the-art AD algorithms. This standardized and well-curated medical benchmark with the well-structured codebase enables researchers to easily compare and evaluate different AD methods, and ultimately leads to the development of more effective and robust AD algorithms for medical imaging. More information on BMAD is available in our GitHub repository: https://***/DorisBao/BMAD 1

关键词： computer vision Benchmark testing Video surveillance Retina pattern recognition Medical diagnosis Medical diagnostic imaging

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