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OMG: Observe Multiple Granularities for Natural Language-Based Vehicle Retrieval

OMG: Observe Multiple Granularities for Natural Language-Bas...

引用

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

作者： Du, Yunhao Zhang, Binyu Ruan, Xiangning Su, Fei Zhao, Zhicheng Chen, Hong Beijing Univ Posts & Telecommun Beijing Peoples R China Beijing Key Lab Network Syst & Network Culture Beijing Peoples R China China Mobile Res Inst Beijing Peoples R China

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Retrieving tracked-vehicles by natural language descriptions plays a critical role in smart city construction. It aims to find the best match for the given texts from a set of tracked vehicles in surveillance videos. Existing works generally solve it by a dual-stream framework, which consists of a text encoder, a visual encoder and a cross-modal loss function. Although some progress has been made, they failed to fully exploit the information at various levels of granularity. To tackle this issue, we propose a novel framework for the natural language-based vehicle retrieval task, OMG, which Observes Multiple Granularities with respect to visual representation, textual representation and objective functions. For the visual representation, target features, context features and motion features are encoded separately. For the textual representation, one global embedding, three local embeddings and a color-type prompt embedding are extracted to represent various granularities of semantic features. Finally, the overall framework is optimized by a cross-modal multi-granularity contrastive loss function. Experiments demonstrate the effectiveness of our method. Our OMG significantly outperforms all previous methods and ranks the 9th on the 6th AI City Challenge Track2.

关键词： Representation learning Visualization Smart cities Surveillance Semantics Feature extraction pattern recognition

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Semi-Supervised Hyperspectral Object Detection Challenge Results - PBVS 2022

Semi-Supervised Hyperspectral Object Detection Challenge Res...

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

作者： Rangnekar, Aneesh Mulhollan, Zachary Vodacek, Anthony Hoffman, Matthew Sappa, Angel Blasch, Erik Yu, Jun Zhang, Liwen Du, Shenshen Chang, Hao Lu, Keda Zhang, Zhong Gao, Fang Yu, Ye Shuang, Feng Wang, Lei Ling, Qiang Shyam, Pranjay Yoon, Kuk-Jin Kim, Kyung-Soo Rochester Inst Technol Rochester NY 14623 USA ESPOL Polytech Univ Guayaquil Ecuador Comp Vision Ctr Campus UAB Barcelona Spain US Air Force Res Lab Rome NY USA

ISBN: (纸本)9781665487399

This paper summarizes the top contributions to the first semi-supervised hyperspectral object detection (SSHOD) challenge, which was organized as a part of the Perception Beyond the Visible Spectrum (PBVS) 2022 workshop at the computer vision and pattern recognition (CVPR) conference. The SSHODC challenge is a first-of-its-kind hyperspectral dataset with temporally contiguous frames collected from a university rooftop observing a 4-way vehicle intersection over a period of three days. The dataset contains a total of 2890 frames, captured at an average resolution of 1600 x 192 pixels, with 51 hyperspectral bands from 400nm to 900nm. SSHOD challenge uses 989 images as the training set, 605 images as validation set and 1296 images as the evaluation (test) set. Each set was acquired on a different day to maximize the variance in weather conditions. Labels are provided for 10% of the annotated data, hence formulating a semi-supervised learning task for the participants which is evaluated in terms of average precision over the entire set of classes, as well as individual moving object classes: namely vehicle, bus and bike. The challenge received participation registration from 38 individuals, with 8 participating in the validation phase and 3 participating in the test phase. This paper describes the dataset acquisition, with challenge formulation, proposed methods and qualitative and quantitative results. [GRAPHICS] .

关键词： Training computer vision conferences Training data Object detection Semisupervised learning Transformers

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CIPPSRNet: A Camera Internal Parameters Perception Network Based Contrastive Learning for Thermal Image Super-Resolution

CIPPSRNet: A Camera Internal Parameters Perception Network B...

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

作者： Wang, Kai Sun, Qigong Wang, Yicheng Wei, Huiyuan Lv, Chonghua Tian, Xiaolin Liu, Xu SenseTime Res Hong Kong Peoples R China Xidian Univ Xian Peoples R China

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Thermal Image Super-Resolution (TISR) is a technique for converting Low-Resolution (LR) thermal images to High-Resolution (HR) thermal images. This technique has recently become a research hotspot due to its ability to reduce sensor costs and improve visual perception. However, current research does not provide an effective solution for multi-sensor data training, possibly driven by pixel mismatch and simple degradation setting issues. In this paper, we proposed a Camera Internal Parameters Perception Network (CIPPSRNet) for LR thermal image enhancement. The camera internal parameters (CIP) were explicitly modeled as a feature representation, the LR features were transformed into the intermediate domain containing the internal parameters information by perceiving CIP representation. The mapping between the intermediate domain and the spatial domain of the HR features was learned by CIPPSRNet. In addition, we introduced contrastive learning to optimize the pretrained Camera Internal Parameters Representation Network and the feature encoders. Our proposed network is capable of achieving a more efficient transformation from the LR to the HR domains. Additionally, the use of contrastive learning can improve the network's adaptability to misalignment data with insufficient pixel matching and its robustness. Experiments on PBVS2022 TISR Dataset show that our network has achieved state-of-the-art performance for the Thermal SR task.

关键词： Training Degradation Visualization Superresolution Cameras Robustness pattern recognition

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Learning Co-segmentation by Segment Swapping for Retrieval and Discovery

Learning Co-segmentation by Segment Swapping for Retrieval a...

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

作者： Shen, Xi Efros, Alexei A. Joulin, Armand Aubry, Mathieu Ecole Ponts ParisTech LIGM UMR 8049 Champs Sur Marne France Univ Calif Berkeley Berkeley CA 94720 USA Facebook AI Res Menlo Pk CA USA

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

The goal of this work is to efficiently identify visually similar patterns in images, e.g. identifying an artwork detail copied between an engraving and an oil painting, or recognizing parts a night-time photograph visible in its daytime counterpart. Lack of training data is a key challenge for this co-segmentation task. We present a simple yet surprisingly effective approach to overcome this difficulty: we generate synthetic training pairs by selecting segments in an image and copy-pasting them into another image. We then learn to predict the repeated region masks. We find that it is crucial to predict the correspondences as an auxiliary task and to use Poisson blending and style transfer on the training pairs to generalize on real data. We analyse results with two deep architectures relevant to our joint image analysis task: a transformer-based architecture and Sparse Nc-Net, a recent network designed to predict coarse correspondences using 4D convolutions. We show our approach provides clear improvements for artwork details retrieval on the Brueghel dataset and achieves competitive performance on two place recognition benchmarks, Tokyo247 and Pitts30K. We also demonstrate the potential of our approach for unsupervised image collection analysis by introducing a spectral graph clustering approach to object discovery and demonstrating it on the object discovery dataset of [49] and the Brueghel dataset. Our code and data are available at http://***/similar to shenx/SegSwap/.

关键词： Training Image segmentation Oils Training data computer architecture Oil insulation Transformers

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An Investigation into the Impact of AI-Powered Image Enhancement on Forensic Facial recognition

An Investigation into the Impact of AI-Powered Image Enhance...

引用

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

作者： Justin Norman Hany Farid University of California Berkeley Berkeley CA USA

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Advances in machine learning and computer vision have led to significant improvements in automated facial recognition. Many real-world forensic settings, however, are confronted with challenging low-quality and low-resolution images that often confound even state-of-the art facial recognition. We investigate if and when advances in neural-based image enhancement and restoration can be used to restore degraded images while preserving facial identity for use in forensic facial recognition.

关键词： computer vision Art Face recognition Forensics conferences Machine learning Image restoration

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Denoising diffusion models for out-of-distribution detection

Denoising diffusion models for out-of-distribution detection

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2023 ieee/cvf conference on computer vision and pattern recognition workshops, cvprw 2023

作者： Graham, Mark S. Pinaya, Walter H. L. Tudosiu, Petru-Daniel Nachev, Parashkev Ourselin, Sebastien Cardoso, M. Jorge King's College London United Kingdom University College London United Kingdom

ISBN: (纸本)9798350302493

Out-of-distribution detection is crucial to the safe deployment of machine learning systems. Currently, unsupervised out-of-distribution detection is dominated by generative-based approaches that make use of estimates of the likelihood or other measurements from a generative model. Reconstruction-based methods offer an alternative approach, in which a measure of reconstruction error is used to determine if a sample is out-of-distribution. However, reconstruction-based approaches are less favoured, as they require careful tuning of the model's information bottleneck-such as the size of the latent dimension - to produce good results. In this work, we exploit the view of denoising diffusion probabilistic models (DDPM) as denoising autoencoders where the bottleneck is controlled externally, by means of the amount of noise applied. We propose to use DDPMs to reconstruct an input that has been noised to a range of noise levels, and use the resulting multi-dimensional reconstruction error to classify out-of-distribution inputs. We validate our approach both on standard computer-vision datasets and on higher dimension medical datasets. Our approach outperforms not only reconstruction-based methods, but also state-of-the-art generative-based approaches. Code is available at https://***/marksgraham/ddpm-ood. © 2023 ieee.

关键词： computer vision

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TikTok for good: Creating a diverse emotion expression database

TikTok for good: Creating a diverse emotion expression datab...

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

作者： Surabhi, Saimourya Shah, Bhavik Washington, Peter Mutlu, Onur Cezmi Leblanc, Emilie Mohite, Prathamesh Husic, Arman Kline, Aaron Dunlap, Kaitlyn McNealis, Maya Liu, Bennett Deveaux, Nick Sleiman, Essam Wall, Dennis P. Stanford Univ Dept Biomed Data Sci Dept Pediat Syst Med Stanford CA 94305 USA Stanford Univ Dept Psychiat & Behav Sci Stanford CA 94305 USA

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Facial expression recognition (FER) is a critical computer vision task for a variety of applications. Despite the widespread use of FER, there is a dearth of racially diverse facial emotion datasets which are enriched for children, teens, and adults. To bridge this gap, we have built a diverse expression recognition database using publicly available videos from TikTok, a video-focused social networking service. We describe the construction of the TikTok Facial expression recognition (FER) database. The dataset is extracted from 6428 videos scraped from TikTok. The videos consist of 9392 distinct individuals and labels for 15 emotion-related prompts. We were able to achieve a F1 score 0.78 for Ekman emotions on expression classification using transfer learning. We hope that the scale and diversity of the TikTokFER dataset will be of use to affective computing practitioners.

关键词： Bridges computer vision Emotion recognition Databases Social networking (online) Face recognition conferences

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Nighttime Image Dehazing Based on Variational Decomposition Model

Nighttime Image Dehazing Based on Variational Decomposition ...

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

作者： Liu, Yun Yan, Zhongsheng Wu, Aimin Ye, Tian Li, Yuche Southwest Univ Coll Artificial Intelligence Chongqing 400715 Peoples R China Chongqing Coll Int Business & Econ Coll Big Data & Intelligent Engn Chongqing 401520 Peoples R China Jimei Univ Sch Ocean Informat Engn Xiamen 361021 Peoples R China China Univ Petr Coll Geosci Beijing 102249 Peoples R China

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Most of existing dehazing algorithms are unable to deal with nighttime hazy scenarios well due to complex degraded factors such as non-uniform illumination, low light and glows. To obtain high-quality image under nighttime haze imaging conditions, we present an effective single nighttime image dehazing framework based on a variational decomposition model to simultaneously address these undesirable issues. First, a variational decomposition model consisting of three regularization terms is proposed to simultaneously decompose a nighttime hazy image into a structure layer, a detail layer and a noise layer. Concretely, we employ l(1) norm to constrain the structure component, adopt l(0) sparsity term to enforce the piece-wise continuous of the detail layer, and use l(2) norm to separate the noise layer. Next, the structure layer is recovered by means of inversing the physical model and the detail layers are revealed in a multi-scale gradient enhancement manner. Finally, the dehazed structure layer and the enhanced detail layers are integrated into a haze-free image. Experimental results show that the proposed framework achieves superior performance on nighttime haze removal and noise suppression compared with several state-of-the-art dehazing techniques.

关键词： Degradation computer vision conferences Computational modeling Noise reduction Lighting Focusing

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EVREAL: Towards a Comprehensive Benchmark and Analysis Suite for Event-based Video Reconstruction

EVREAL: Towards a Comprehensive Benchmark and Analysis Suite...

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2023 ieee/cvf conference on computer vision and pattern recognition workshops, cvprw 2023

作者： Ercan, Burak Eker, Onur Erdem, Aykut Erdem, Erkut Hacettepe University Computer Engineering Department Turkey Havelsan Inc Turkey Koç University Computer Engineering Department Turkey Koç University Kuis Ai Center Turkey

ISBN: (纸本)9798350302493

Event cameras are a new type of vision sensor that incorporates asynchronous and independent pixels, offering advantages over traditional frame-based cameras such as high dynamic range and minimal motion blur. However, their output is not easily understandable by humans, making the reconstruction of intensity images from event streams a fundamental task in event-based vision. While recent deep learning-based methods have shown promise in video reconstruction from events, this problem is not completely solved yet. To facilitate comparison between different approaches, standardized evaluation protocols and diverse test datasets are essential. This paper proposes a unified evaluation methodology and introduces an open-source framework called EVREAL to comprehensively benchmark and analyze various event-based video reconstruction methods from the literature. Using EVREAL, we give a detailed analysis of the state-of-the-art methods for event-based video reconstruction, and provide valuable insights into the performance of these methods under varying settings, challenging scenarios, and downstream tasks. © 2023 ieee.

关键词： Cameras

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Deep Dehazing Powered by Image Processing Network

Deep Dehazing Powered by Image Processing Network

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2023 ieee/cvf conference on computer vision and pattern recognition workshops, cvprw 2023

作者： Kim, Guisik Park, Jinhee Kwon, Junseok Chung-Ang University Korea Electronics Technology Institute Seoul Korea Republic of Chung-Ang University School of Computer Science and Engineering Seoul Korea Republic of

ISBN: (纸本)9798350302493

Image processing is a very fundamental technique in the field of low-level vision. However, with the development of deep learning over the past five years, most low-level vision methods tend to ignore this technique. Recent dehazing methods also refrain from using conventional image processing techniques, whereas only focusing on the development of new deep neural network (DNN) architectures. Unlike this recent trend, we show that image processing techniques are still competitive, if they are incorporated into DNNs. In this paper, we utilize conventional image processing techniques (i.e. curve adjustment, retinex decomposition, and multiple image fusion) for accurate dehazing. Moreover, we employ direct learning for stable dehazing performance. The proposed method can perform with low computational cost and easy to learn. The experimental results demonstrate that the proposed method produces accurate dehazing results compared to recent algorithms. © 2023 ieee.

关键词： Image fusion

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