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检索条件"任意字段=2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2024"

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Improving the Efficiency-Accuracy Trade-off of DETR-Style Models in Practice

Improving the Efficiency-Accuracy Trade-off of DETR-Style Mo...

引用

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

作者： Yumin Suh Dongwan Kim Abhishek Aich Samuel Schulter Jong-Chyi-Su Bohyung Han Manmohan Chandraker NEC Laboratories America Seoul National University

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

We aim to provide a comprehensive view of the inference efficiency of DETR-style detection models. We explore the effect of basic efficiency techniques and identify the factors that are easy to implement, yet effectively improve the efficiency-accuracy trade-off. Specifically, we investigate the effect of input resolution, multi-scale feature enhancement, and backbone pre-training. Our experiments support that 1) adjusting the input resolution is a simple yet effective way to achieve a better efficiency-accuracy trade-off. 2) Multi-scale feature enhancement can be lightened with a marginal decrease in accuracy, and 3) improved backbone pre-training can further improve the trade-off.

关键词： computer vision Accuracy conferences pattern recognition

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Multimodal Understanding of Memes with Fair Explanations

Multimodal Understanding of Memes with Fair Explanations

引用

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

作者： Yang Zhong Bhiman Kumar Baghel Department of Computer Science University of Pittsburgh PA USA

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Digital Memes have been widely utilized in people’s daily lives over social media platforms. Composed of images and descriptive texts, memes are often distributed with the flair of sarcasm or humor, yet can also spread harmful content or biases from social and cultural factors. Aside from mainstream tasks such as meme generation and classification, generating explanations for memes has become more vital and poses challenges in avoiding propagating already embedded biases. Our work studied whether recent advanced vision Language Models (VL models) can fairly explain meme contents from different domains/topics, contributing to a unified benchmark for meme explanation. With the dataset, we semi-automatically and manually evaluate the quality of VL model-generated explanations, identifying the major categories of biases in meme explanations.

关键词： computer vision Social networking (online) conferences Computational modeling Benchmark testing pattern recognition Cultural differences

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A Data-Centric Solution to NonHomogeneous Dehazing via vision Transformer

A Data-Centric Solution to NonHomogeneous Dehazing via Visio...

引用

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

作者： Y., Liu H., Liu L., Li Z., Wu J., Chen McMaster University Hamilton Canada China Telecom Research Institute Shanghai China

ISBN: (纸本)9798350302493

Recent years have witnessed an increased interest in image dehazing. Many deep learning methods have been proposed to tackle this challenge, and have made significant accomplishments dealing with homogeneous haze. However, these solutions cannot maintain comparable performance when they are applied to images with non-homogeneous haze, e.g., NH-HAZE23 dataset introduced by NTIRE challenge. One of the reasons for such failures is that non-homogeneous haze does not obey one of the assumptions that is required for modeling homogeneous haze. In addition, a large number of pairs of non-homogeneous hazy image and the clean counterpart is required using traditional end-to-end training approaches, while NH-HAZE23 dataset is of limited quantities. Although it is possible to augment the NH-HAZE23 dataset by leveraging other non-homogeneous dehazing datasets, we observe that it is necessary to design a proper data-preprocessing technique that reduces the distribution gaps between the target dataset and the augmented one. This finding indeed aligns with the essence of data-centric AI. With a novel network architecture and a principled data-preprocessing approach that systematically enhances data quality, we present an innovative dehazing method. Specifically, we apply RGB-channel-wise transformations on the augmented datasets, and incorporate the state-of-the-art transformers as the backbone in the two-branch framework. We conduct extensive experiments and ablation studies to demonstrate the effectiveness of our proposed method. The source code is available at https://***/yangyiliu21/ntire2023-ITBdehaze. © 2023 ieee.

关键词： Network architecture

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CAT-NeRF: Constancy-Aware Tx2Former for Dynamic Body Modeling

CAT-NeRF: Constancy-Aware Tx2Former for Dynamic Body Modelin...

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

作者： Zhu, Haidong Zheng, Zhaoheng Zheng, Wanrong Nevatia, Ram University of Southern California United States

ISBN: (纸本)9798350302493

This paper addresses the problem of human rendering in the video with temporal appearance constancy. Reconstructing dynamic body shapes with volumetric neural rendering methods, such as NeRF, requires finding the correspondence of the points in the canonical and observation space, which demands understanding human body shape and motion. Some methods use rigid transformation, such as SE(3), which cannot precisely model each frame's unique motion and muscle movements. Others generate the transformation for each frame with a trainable network, such as neural blend weight field or translation vector field, which does not consider the appearance constancy of general body shape. In this paper, we propose CAT-NeRF for self-awareness of appearance constancy with Tx2Former, a novel way to combine two Transformer layers, to separate appearance constancy and uniqueness. Appearance constancy models the general shape across the video, and uniqueness models the unique patterns for each frame. We further introduce a novel Covariance Loss to limit the correlation between each pair of appearance uniquenesses to ensure the frame-unique pattern is maximally captured in appearance uniqueness. We assess our method on H36M and ZJU-MoCap and show state-of-the-art performance. © 2023 ieee.

关键词： computer vision

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NTIRE 2022 Image Inpainting Challenge: Report

NTIRE 2022 Image Inpainting Challenge: Report

引用

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

作者： Romero, Andres Castillo, Angela Abril-Nova, Jose Timofte, Radu Das, Ritwik Hira, Sanchit Pan, Zhihong Zhang, Min Li, Baopu He, Dongliang Lin, Tianwei Li, Fu Wu, Chengyue Liu, Xianming Wang, Xinying Yu, Yi Yang, Jie Li, Rengang Zhao, Yaqian Guo, Zhenhua Fan, Baoyu Li, Xiaochuan Zhang, Runze Lu, Zeyu Huang, Junqin Wu, Gang Jiang, Junjun Cai, Jiayin Li, Changlin Tao, Xin Tai, Yu-Wing Zhou, Xiaoqiang Huang, Huaibo Swiss Fed Inst Technol Comp Vis Lab Zurich Switzerland Univ Los Andes Ctr Res & Format Artificial Intelligence Bogota Colombia Univ Wurzburg Wurzburg Germany Baidu Res Sunnyvale CA USA Univ Southern Calif Los Angeles CA 90089 USA Baidu Inc Dept Comp Vis Technol VIS Sunnyvale CA USA Harbin Inst Technol Harbin Peoples R China Beihang Univ Beijing Peoples R China MGTV Changchun Peoples R China Inspur Elect Informat Ind Co Ltd Jinan Peoples R China State Key Lab High End Server & Storage Technol Jinan Peoples R China Univ Sci & Technol China Hefei Peoples R China

ISBN: (纸本)9781665487399

Image Inpainting has recently become an important research problem due to the rise of generative image synthesis models. While many solutions have been proposed for this problem, it is challenging to establish a testbed due to the different possible types of inpainting masks e.g., completion mask, expand mask, thick brushes mask, etc. Most inpainting solutions shine on object removal or texture synthesis, while semantic generation is still difficult to achieve. To address these issues, we introduce the first general Image Inpainting Challenge. The target is to develop solutions that can achieve a robust performance across different and challenging masks while generating compelling semantic images. The proposed challenge consists of two tracks: unsupervised image inpainting and semantically-guided image inpainting. For Track 1, the participants were provided with four datasets: FFHQ, Places, ImageNet, and WikiArt, and trained their models to perform a mask-agnostic image inpainting solution. For Track 2, FFHQ and Places only. This report gathers the description and discussion of all solutions that participated in the final stage of the challenge.

关键词： computer vision Target tracking Brushes Image synthesis conferences Computational modeling Semantics

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TransER: Hybrid Model and Ensemble-based Sequential Learning for Non-homogenous Dehazing

TransER: Hybrid Model and Ensemble-based Sequential Learning...

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

作者： Hoang, Trung Zhang, Haichuan Yazdani, Amirsaeed Monga, Vishal Pennsylvania State University School of Electrical Engineering and Computer Science University Park United States

ISBN: (纸本)9798350302493

Image dehazing is one of the most challenging imaging inverse problems that estimates the haze-free images from hazy ones. While recent transformer/convolutional neural network-based methods have shown excellent performance in handling both homogeneous and non-homogeneous dehazing problems, these networks are often trained end-to-end to estimate the haze-free image directly and require a large number of parameters. In this work, we propose a novel, lightweight two-stage deep network for non-homogeneous dehazing. In particular, our proposed method, denoted as TransER, consists of two separate deep neural networks which are TransConv Fusion Dehaze (TFD) model in Stage I and Lightweight Ensemble Reconstruction (LER) network in Stage II. The first model (TFD) using transformer-based encoder and decoders generates two estimates of the haze-free image: a parameter-based dehazed output based on the physical modeling of the problem and a pseudo haze-free output generated directly by the model in an end-to-end fashion. LER in stage II reconstructs the final dehazed output fusing the two estimates from stage I. We incorporate knowledge distillation to develop a teacher network with the same architecture as LER, allowing it to supervise the intermediate features. Extensive experiments performed on challenging real and synthetic scene image datasets (NTIRE 2019-2023, and RESIDE-indoor) demonstrate that TransER can outperform many state-of-the-art competing methods while using a significantly lower number of parameters. The source code is available at https://***/trungpsu1210/TransER. © 2023 ieee.

关键词： Distillation

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Region-based Appearance and Flow Characteristics for Anomaly Detection in Infrared Surveillance Imagery

Region-based Appearance and Flow Characteristics for Anomaly...

引用

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

作者： Gaus, Yona Falinie A. Bhowmik, Neelanjan Isaac-Medina, Brian K. S. Shum, Hubert P. H. Atapour-Abarghouei, Amir Breckon, Toby P. Durham University Department of Computer Science Durham United Kingdom Durham University Department of Engineering Durham United Kingdom

ISBN: (纸本)9798350302493

Anomaly detection is a classical problem within automated visual surveillance, namely the determination of the normal from the abnormal when operational data availability is highly biased towards one class (normal) due to both insufficient sample size, and inadequate distribution coverage for the other class (abnormal). In this work, we propose the dual use of both visual appearance and localized motion characteristics, derived from optic flow, applied on a per-region basis to facilitate object-wise anomaly detection within this context. Leveraging established object localization techniques from a region proposal network, optic flow is extracted from each object region and combined with appearance in the far infrared (thermal) band to give a 3-channel spatiotemporal tensor representation for each object (1 × thermal - spatial appearance;2 × optic flow magnitude as x and y components - temporal motion). This formulation is used as the basis for training contemporary semi-supervised anomaly detection approaches in a region-based manner such that anomalous objects can be detected as a combination of appearance and/or motion within the scene. Evaluation is performed using the Long-Term infrared (thermal) Imaging (LTD) benchmark dataset against which successful detection of both anomalous object appearance and motion characteristics are demonstrated using a range of semi-supervised anomaly detection approaches. © 2023 ieee.

关键词： Anomaly detection

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BeCAPTCHA-Type: Biometric Keystroke Data Generation for Improved Bot Detection

BeCAPTCHA-Type: Biometric Keystroke Data Generation for Impr...

引用

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

作者： Dealcala, Daniel Morales, Aythami Tolosana, Ruben Acien, Alejandro Fierrez, Julian Hernandez, Santiago Ferrer, Miguel A. Diaz, Moises Universidad Autonoma de Madrid Biometrics and Data Pattern Analytics Lab Spain University Las Palmas Gran Canaria Spain

ISBN: (纸本)9798350302493

This work proposes a data driven learning model for the synthesis of keystroke biometric data. The proposed method is compared with two statistical approaches based on Universal and User-dependent models. These approaches are validated on a bot detection task, using the keystroke synthetic data to improve the training process of keystroke-based bot detection systems. Our experimental framework considers a dataset with 136 million keystroke events from 168 thousand subjects. We have analyzed the performance of the three synthesis approaches through qualitative and quantitative experiments. Different bot detectors are considered based on several supervised classifiers (Support Vector Machine, Random Forest, Gaussian Naive Bayes and a Long Short-Term Memory network) and a learning framework including human and synthetic samples. The experiments demonstrate the realism of the synthetic samples. The classification results suggest that in scenarios with large labeled data, these synthetic samples can be detected with high accuracy. However, if the proposed synthetic data is nor properly modelled using massive data by bot detectors, then that data will be very difficult to detect even for the most sophisticate bot detectors. Furthermore, these results show the great potential of the presented models for improving the training of bot detection technology. © 2023 ieee.

关键词： Biometrics

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Generalized Foggy-Scene Semantic Segmentation by Frequency Decoupling

Generalized Foggy-Scene Semantic Segmentation by Frequency D...

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

作者： Qi Bi Shaodi You Theo Gevers Computer Vision Research Group University of Amsterdam Amsterdam The Netherlands

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Foggy-scene semantic segmentation (FSSS) is highly challenging due to the diverse effects of fog on scene properties and the limited training data. Existing research has mainly focused on domain adaptation for FSSS, which has practical limitations when dealing with new scenes. In our paper, we introduce domain-generalized FSSS, which can work effectively on unknown distributions without extensive training. To address domain gaps, we propose a frequency decoupling (FreD) approach that separates fog-related effects (amplitude) from scene semantics (phase) in feature representations. Our method is compatible with both CNN and vision Transformer backbones and outperforms existing approaches in various scenarios.

关键词： Training computer vision Frequency-domain analysis Semantic segmentation conferences Semantics Training data

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ReMOVE: A Reference-free Metric for Object Erasure

ReMOVE: A Reference-free Metric for Object Erasure

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

作者： Aditya Chandrasekar Goirik Chakrabarty Jai Bardhan Ramya Hebbalaguppe Prathosh AP TCS Research IISc Bangalore IIT Delhi

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

We introduce ReMOVE, a novel reference-free metric for assessing object erasure efficacy in diffusion-based image editing models post-generation. Unlike existing measures such as LPIPS and CLIPScore, ReMOVE addresses the challenge of evaluating inpainting without a reference image, common in practical scenarios. It effectively distinguishes between object removal and replacement. This is a key issue in diffusion models due to stochastic nature of image generation. Traditional metrics fail to align with the intuitive definition of inpainting, which aims for (1) seamless object removal within masked regions (2) while preserving the background continuity. ReMOVE not only correlates with state-of-the-art metrics and aligns with human perception but also captures the nuanced aspects of the inpainting process, providing a finer-grained evaluation of the generated outputs.

关键词： Measurement computer vision Image synthesis conferences Stochastic processes Diffusion models pattern recognition

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