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检索条件"任意字段=Proceedings - IEEE Computer Society Conference on Pattern Recognition and Image Processing."

共 8406 条记录，以下是521-530 订阅

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Learning Robust Convolutional Neural Networks with Relevant Feature Focusing Via Explanations

Learning Robust Convolutional Neural Networks with Relevant ...

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2022 ieee International conference on Multimedia and Expo, ICME 2022

作者： Adachi, Kazuki Yamaguchi, Shin'ya Ntt Corporation Computer and Data Science Laboratories Japan

ISBN: (数字)9781665485630

ISBN: (纸本)9781665485630

Existing image recognition techniques based on convolutional neural networks (CNNs) basically assume that the training and test datasets are sampled from i.i.d distributions. However, this assumption is easily broken in the real world because of the distribution shift that occurs when the co-occurrence relations between objects and backgrounds in input images change. Under this type of distribution shift, CNNs learn to focus on features that are not task-relevant, such as backgrounds from the training data, and degrade their accuracy on the test data. To tackle this problem, we propose relevant feature focusing (ReFF). ReFF detects task-relevant features and regularizes CNNs via explanation outputs (e.g., Grad-CAM). Since ReFF is composed of post-hoc explanation modules, it can be easily applied to off-the-shelf CNNs. Furthermore, ReFF requires no additional inference cost at test time because it is only used for regularization while training. We demonstrate that CNNs trained with ReFF focus on features relevant to the target task and that ReFF improves the test-time accuracy. © 2022 ieee.

关键词： Convolutional neural networks

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Large Kernel Frequency-enhanced Network for Efficient Single image Super-Resolution

Large Kernel Frequency-enhanced Network for Efficient Single...

引用

ieee computer society conference on computer Vision and pattern recognition Workshops (CVPRW)

作者： Jiadi Chen Chunjiang Duanmu Huanhuan Long Zhejiang Normal University Jinhua China

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

In recent years, there has been significant progress in efficient and lightweight image super-resolution, due in part to the design of several powerful and lightweight attention mechanisms that enhance model representation ability. However, the attention maps of most methods are obtained directly from the spatial domain, limiting their upper bound due to the locality of spatial convolutions and limited receptive fields. In this paper, we shift focus to the frequency domain, since the natural global properties of the frequency domain can address this issue. To explore attention maps from the frequency domain perspective, we investigate and correct some misconceptions in existing frequency domain feature processing.methods and propose a new frequency domain attention mechanism called frequency-enhanced pixel attention (FPA). Additionally, we use large kernel convolutions and partial convolutions to improve the ability to extract deep features while maintaining a lightweight design. On the basis of these improvements, we propose a large kernel frequency-enhanced network (LKFN) with smaller model size and higher computational efficiency. It can effectively capture long-range dependencies between pixels in a whole image and achieve state-of-the-art performance in existing efficient super-resolution methods.

关键词： Attention mechanisms Upper bound Limiting image color analysis Frequency-domain analysis Superresolution Feature extraction

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An Efficient Approach for Anomaly Detection in Traffic Videos

An Efficient Approach for Anomaly Detection in Traffic Video...

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ieee/CVF conference on computer Vision and pattern recognition (CVPR)

作者： Doshi, Keval Yilmaz, Yasin Univ S Florida 4202 E FowlerAve Tampa FL 33620 USA

ISBN: (纸本)9781665448994

Due to its relevance in intelligent transportation systems, anomaly detection in traffic videos has recently received much interest. It remains a difficult problem due to a variety of factors influencing the video quality of a real-time traffic feed, such as temperature, perspective, lighting conditions, and so on. Even though state-of-the-art methods perform well on the available benchmark datasets, they need a large amount of external training data as well as substantial computational resources. In this paper, we propose an efficient approach for a video anomaly detection system which is capable of running at the edge devices, e.g., on a roadside camera. The proposed approach comprises a pre-processing.module that detects changes in the scene and removes the corrupted frames, a two-stage background modelling module and a two-stage object detector. Finally, a backtracking anomaly detection algorithm computes a similarity statistic and decides on the onset time of the anomaly. We also propose a sequential change detection algorithm that can quickly adapt to a new scene and detect changes in the similarity statistic. Experimental results on the Track 4 test set of the 2021 AI City Challenge show the efficacy of the proposed framework as we achieve an F1-score of 0.9157 along with 8.4027 root mean square error (RMSE) and are ranked fourth in the competition.

关键词： Performance evaluation Temperature Computational modeling image edge detection Urban areas Training data Artificial intelligence

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Supervised Machine Learning: Discriminative Learning Methods for pattern Classification using HELM 5

Supervised Machine Learning: Discriminative Learning Methods...

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5th International conference on Inventive Research in Computing Applications, ICIRCA 2023

作者： Shirisha, N. Kiran, Ajmeera Prashanthi, A. Reddy, MrKamjula Lakshmi Kanth Mlr Institute of Technology Hyderabad India Mlr Institute of Technology Department of Computer Science and Engineering Hyderabad India Nalla Narasimha Reddy Education Society's Group of Institutions Department of Computer Science and Engineering India Anurag University Department of Artificial Intelligence Hyderabad India

ISBN: (纸本)9798350321425

The ability to forecast the data is significantly helped by machine learning's involvement. At the present time, data analysis is playing a very significant part in both the Information Technology (IT) industry and academic institutions. Numerous real-time applications, such as translation in real time, facial recognition, musical compositions, and so on, have been developed through the various initiatives that have been carried out up to this point. According to the standards that must be met by every data analyst, data must first be preprocessed before any predictions can be made on that data. Predictions must also be based exclusively on the preprocessed data. This work demonstrates the data pre-processing.along with the prediction of a churn model. The model contains a set of records of various customers, including information regarding whether or not the client possesses credit cards and whether or not the consumer is an active member. This research works on the construction of a hybrid ensemble learning model (abbreviated HELM) to collect heterogeneous weak learners in order to anticipate such analyses. In order to work on the Churn Model classification, a variety of machine learning algorithms are integrated with one another. The findings revealed the efficiency of each machine learning algorithm that was used, in addition to HELM's operational effectiveness. © 2023 ieee.

关键词： Forecasting

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GPT as Psychologist? Preliminary Evaluations for GPT-4V on Visual Affective Computing

GPT as Psychologist? Preliminary Evaluations for GPT-4V on V...

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ieee computer society conference on computer Vision and pattern recognition Workshops (CVPRW)

作者： Hao Lu Xuesong Niu Jiyao Wang Yin Wang Qingyong Hu Jiaqi Tang Yuting Zhang Kaishen Yuan Bin Huang Zitong Yu Dengbo He Shuiguang Deng Hao Chen Yingcong Chen Shiguang Shan The Hong Kong University of Science & Technology (Guangzhou) The Hong Kong University of Science & Technology Beijing Institute for General Artificial Intelligence Zhejiang University Great Bay University Hangzhou Research Institute Beihang University Chinese Academy of Sciences Institute of Computing Technology

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Multimodal large language models (MLLMs) are designed to process and integrate information from multiple sources, such as text, speech, images, and videos. Despite its success in language understanding, it is critical to evaluate the performance of downstream tasks for better human-centric applications. This paper assesses the application of MLLMs with 5 crucial abilities for affective computing, spanning from visual affective tasks and reasoning tasks. The results show that GPT-4V has high accuracy in facial action unit recognition and micro-expression detection while its general facial expression recognition performance is not accurate. We also highlight the challenges of achieving fine-grained micro-expression recognition and the potential for further study and demonstrate the versatility and potential of GPT-4V for handling advanced tasks in emotion recognition and related fields by integrating with task-related agents for more complex tasks, such as heart rate estimation through signal processing. In conclusion, this paper provides valuable insights into the potential applications and challenges of MLLMs in human-centric computing. Our interesting examples are at https://***/EnVision-Research/GPT4Affectivity.

关键词： Heart rate Emotion recognition Visualization computer vision Affective computing Accuracy Face recognition

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Mitigating Challenges of the Space Environment for Onboard Artificial Intelligence: Design Overview of the Imaging Payload on SpIRIT

Mitigating Challenges of the Space Environment for Onboard A...

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ieee computer society conference on computer Vision and pattern recognition Workshops (CVPRW)

作者： Miguel Ortiz Del Castillo Jonathan Morgan Jack McRobbie Clint Therakam Zaher Joukhadar Robert Mearns Simon Barraclough Richard Sinnott Andrew Woods Chris Bayliss Kris Ehinger Ben Rubinstein James Bailey Airlie Chapman Michele Trenti The University of Melbourne

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Artificial intelligence (AI) and autonomous edge computing in space are emerging areas of interest to augment capabilities of nanosatellites, where modern sensors generate orders of magnitude more data than can typically be transmitted to mission control. Here, we present the hardware and software design of an onboard AI subsystem hosted on SpIRIT. The system is optimised for on-board computer vision experiments based on visible light and long wave infrared cameras. This paper highlights the key design choices made to maximise the robustness of the system in harsh space conditions, and their motivation relative to key mission requirements, such as limited compute resources, resilience to cosmic radiation, extreme temperature variations, distribution shifts, and very low transmission bandwidths. The payload, called Loris, consists of six visible light cameras, three infrared cameras, a camera control board and a Graphics processing.Unit (GPU) system-on-module. Loris enables the execution of AI models with on-orbit fine-tuning as well as a next-generation image compression algorithm, including progressive coding. This innovative approach not only enhances the data processing.capabilities of nanosatellites but also lays the groundwork for broader applications to remote sensing from space.

关键词： computer vision Temperature distribution image coding Space missions Small satellites Graphics processing.units Aerospace electronics

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Learning Epipolar-Spatial Relationship for Light Field image Super-Resolution

Learning Epipolar-Spatial Relationship for Light Field Image...

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ieee computer society conference on computer Vision and pattern recognition Workshops (CVPRW)

作者： Ahmed Salem Hatem Ibrahem Hyun-Soo Kang School of Information and Communication Engineering Chungbuk National University Cheongju Korea

Light field (LF) imaging has become increasingly popular in recent years for capturing and processing.visual information. A significant challenge in LF processing.is super-resolution (SR), which aims to enhance the resolution of low-resolution LF images. This article proposes a new LF image super-resolution (LFSR) approach that leverages the epipolar-spatial relationship within the LF. To train a deep neural network for LFSR, the proposed method involves extracting three types of information from the LF: spatial, horizontal epipolar, and vertical epipolar. Experimental results demonstrate the effectiveness of the proposed approach compared with state-of-the-art (SOTA) performance, as evidenced by quantitative metrics and visual quality. In addition, we conducted ablation studies to assess the effectiveness of each type of information and gain insights into the underlying mechanisms of the proposed method. Our approach achieved competitive results on the NTIRE 2023 Light Field image Super-Resolution Challenge: our proposed model was ranked 10th on the test set and 6th on the validation set among 148 participants. Paper’s code is available at: https://***/ahmeddiefy/EpiS_LFSR.

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Sensor Equivariance: A Framework for Semantic Segmentation with Diverse Camera Models

Sensor Equivariance: A Framework for Semantic Segmentation w...

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ieee computer society conference on computer Vision and pattern recognition Workshops (CVPRW)

作者： Hannes Reichert Manuel Hetzel Andreas Hubert Konrad Doll Bernhard Sick University of Applied Sciences Aschaffenburg Germany University of Kassel Germany

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Objects are represented differently in projection-based sensors such as cameras depending on sensor resolution, field of view, and distortion, leading to distorted physical and geometric properties. As a result, sensor data processing.depend on these properties. With the large variations of sensors on the market, an equivariant representation and suitable processing.are necessary to become independent of the sensor used. In this work, we propose an extension of conventional image data by an additional channel in which the associated projection properties are encoded. Furthermore, we introduce a SensorConv layer as an extension to the conventional convolution layer. SensorConv enable using projection properties in convolutional neural networks. To that end, we propose an architecture for using the SensorConv layer in the Detectron2 [21] framework. We collected a dataset of equirectangular images for our experiments with the CARLA [3] simulator. To analyze multiple sensor models (i.e., sensor intrinsic), we created an augmentation method to emulate a high variability of sensors from the collected equirectangular panoramas. In our experiment, we show that our method can generalize better across different camera sensors.

关键词： computer vision Convolution Semantic segmentation conferences Cameras Distortion Data processing

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Building Vision-Language Models on Solid Foundations with Masked Distillation

Building Vision-Language Models on Solid Foundations with Ma...

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conference on computer Vision and pattern recognition (CVPR)

作者： Sepehr Sameni Kushal Kafle Hao Tan Simon Jenni University of Bern Adobe Research

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

Recent advancements in Vision-Language Models (VLMs) have marked a significant leap in bridging the gap between computer vision and natural language processing. However, traditional VLMs, trained through contrastive learning on limited and noisy image-text pairs, often lack the spatial and linguistic understanding to generalize well to dense vision tasks or less common languages. Our approach, Solid Foun-dation CLIP (SF-CLIP), circumvents this issue by implicitly building on the solid visual and language understanding of foundational models trained on vast amounts of unimodal data. SF-CLIP integrates contrastive image-text pretraining with a masked knowledge distillation from large foundational text and vision models. This methodology guides our VLM in developing robust text and image representations. As a result, SF-CLIP shows exceptional zero-shot classification accuracy and enhanced image and text retrieval capabilities, setting a new state of the art for ViT-B/16 trained on YFCC15M and ***, the dense per-patch supervision enhances our zero-shot and linear probe performance in semantic segmentation tasks. A remarkable aspect of our model is its multilingual proficiency, evidenced by strong retrieval results in multiple languages despite being trained predominantly on English data. We achieve all of these improvements without sacrificing the training efficiency through our selective application of masked distillation and the inheritance of teacher word embeddings.

关键词： Training Solid modeling computer vision Visualization Computational modeling Semantic segmentation Buildings

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MobileDeRainGAN: An Efficient Semi-Supervised Approach to Single image Rain Removal for Task-Driven Applications

MobileDeRainGAN: An Efficient Semi-Supervised Approach to Si...

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ieee computer society conference on computer Vision and pattern recognition Workshops (CVPRW)

作者： Ruphan Swaminathan Pradyot Korupolu Columbia University Ottonomy Inc

Rain removal is an essential task in computer vision, particularly for applications such as autonomous navigation to function seamlessly during rain. However, most existing single-image deraining algorithms are limited by their inability to generalize on diverse real-world rainy images, the need for real-time processing. and the lack of task-driven metric enhancement. This paper proposes MobileDeRainGAN, an efficient semi-supervised algorithm that addresses these challenges. The proposed approach includes a novel latent bridge network and multi-scale discriminator that effectively removes rain-related artifacts at different scales. Our cross-domain experiments on Rain1400 and RainCityscapes datasets demonstrate substantial improvements over state-of-the-art methods in terms of generalization and object detection scores in a semi-supervised setting. Furthermore, our approach is significantly faster and can run in real-time even on edge devices. Overall, our proposed MobileDeRainGAN algorithm offers a significant improvement in rain removal performance on real-world images while being efficient, scalable, and suitable for real-world applications.

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