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GM-DETR: Generalized Muiltispectral DEtection TRansformer with Efficient Fusion Encoder for Visible-Infrared Detection

GM-DETR: Generalized Muiltispectral DEtection TRansformer wi...

引用

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

作者： Yiming Xiao Fanman Meng Qingbo Wu Linfeng Xu Mingzhou He Hongliang Li University of Electronic Science and Technology of China China

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Multispectral object detection based on RGB and IR achieves improved accurate and robust performance by integrating complementary information from different modalities. However, existing methods predominantly focus on effectively fusing information from both modalities to enhance detection performance, and rarely study the diversified utilization of RGB and IR data and explore the adaptability of the model to practical application scenarios. We first analyze the utilization of datasets for multispectral object detection, and compare their testing performance. To better leverage datasets and address more generalized model application scenarios, we propose a Generalized Multispectral DEtection TRansformer (GM-DETR) with a two-stage training strategy. Specifically, we design the Modality-Specific Feature Interaction (MSFI) module to extract the high-level information from RGB and IR, and propose the Cross-Modality-Scale feature Fusion (CMSF) module for fusing RGB and IR modalities, which performs multi-scale cross-modalities fusion. Our GM-DETR achieves state-of-the-art performance on FLIR and LLVIP benchmark datasets.

关键词： Training Adaptation models computer vision conferences Object detection Benchmark testing Feature extraction

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Multimodal Attack Detection for Action recognition Models

Multimodal Attack Detection for Action Recognition Models

引用

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

作者： Furkan Mumcu Yasin Yilmaz University of South Florida Tampa FL

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Adversarial machine learning attacks on video action recognition models is a growing research area and many effective attacks were introduced in recent years. These attacks show that action recognition models can be breached in many ways. Hence using these models in practice raises significant security concerns. However, there are very few works which focus on defending against or detecting attacks. In this work, we propose a novel universal detection method which is compatible with any action recognition model. In our extensive experiments, we show that our method consistently detects various attacks against different target models with high true positive rates while satisfying very low false positive rates. Tested against four state-of-the-art attacks targeting four action recognition models, the proposed detector achieves an average AUC of 0.911 over 16 test cases while the best performance achieved by the existing detectors is 0.645 average AUC. This 41.2% improvement is enabled by the robustness of the proposed detector to varying attack methods and target models. The lowest AUC achieved by our detector across the 16 test cases is 0.837 while the competing detector’s performance drops as low as 0.211. We also show that the proposed detector is robust to varying attack strengths. In addition, we analyze our method’s real-time performance with different hardware setups to demonstrate its potential as a practical defense mechanism.

关键词： computer vision Target recognition conferences Graphics processing units Detectors Real-time systems Robustness

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Reactive Model Correction: Mitigating Harm to Task-Relevant Features via Conditional Bias Suppression

Reactive Model Correction: Mitigating Harm to Task-Relevant ...

引用

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

作者： Dilyara Bareeva Maximilian Dreyer Frederik Pahde Wojciech Samek Sebastian Lapuschkin Fraunhofer Heinrich Hertz Institute Technical University of Berlin BIFOLD – Berlin Institute for the Foundations of Learning and Data

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Deep Neural Networks are prone to learning and relying on spurious correlations in the training data, which, for high-risk applications, can have fatal consequences. Various approaches to suppress model reliance on harmful features have been proposed that can be applied post-hoc without additional training. Whereas those methods can be applied with efficiency, they also tend to harm model performance by globally shifting the distribution of latent features. To mitigate unintended overcorrection of model behavior, we propose a reactive approach conditioned on model-derived knowledge and eXplainable Artificial Intelligence (XAI) insights. While the reactive approach can be applied to many post-hoc methods, we demonstrate the incorporation of reactivity in particular for P-ClArC (Projective Class Artifact Compensation), introducing a new method called R-ClArC (Reactive Class Artifact Compensation). Through rigorous experiments in controlled settings (FunnyBirds) and with a real-world dataset (ISIC2019), we show that introducing reactivity can minimize the detrimental effect of the applied correction while simultaneously ensuring low reliance on spurious features.

关键词： Training computer vision Correlation Explainable AI conferences Training data Artificial neural networks

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Neural Fields for Co-Reconstructing 3D Objects from Incidental 2D Data

Neural Fields for Co-Reconstructing 3D Objects from Incident...

引用

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

作者： Dylan Campbell Eldar Insafutdinov João F. Henriques Andrea Vedaldi Australian National University University of Oxford

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

We ask whether 3D objects can be reconstructed from real world data collected for some other purpose, such as autonomous driving or augmented reality, thus inferring objects only incidentally. 3D reconstruction from incidental data is a major challenge because, in addition to significant noise, only a few views of each object are observed, which are insufficient for reconstruction. We approach this problem as a co-reconstruction task, where multiple objects are reconstructed together, learning shape and appearance priors for regularization. In order to do so, we introduce a neural radiance field that is conditioned via an attention mechanism on the identity of the individual objects. We further disentangle shape from appearance and diffuse color from specular color via an asymmetric two-stream network, which factors shared information from instance-specific details. We demonstrate the ability of this method to reconstruct full 3D objects from partial, incidental observations in autonomous driving and other datasets.

关键词： computer vision Three-dimensional displays Shape Image color analysis conferences Noise Neural radiance field

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High Quality Reference Feature for Two Stage Bracketing Image Restoration and Enhancement

High Quality Reference Feature for Two Stage Bracketing Imag...

引用

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

作者： Xiaoxia Xing HyunHee Park Fan Wang Ying Zhang Sejun Song Changho Kim Xiangyu Kong Samsung R&D Institute China-Beijing Department of Camera Innovation Group Samsung Electronics

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

In a low-light environment, it is difficult to obtain high-quality or high-resolution images with sharp details and high dynamic range (HDR) without noise or blur. To solve this problem, the Bracketing Image Restoration and Enhancement integrates Dnoise, Deblur, HDR Reconstruction, and Super Resolution techniques into a unified framework. However, we find that most methods select the image that aligns with GT as the reference image. Since the details of the reference image are not good enough, seriously affects the feature fusion, which finally leads to details being blurred. To generate a high dynamic range and a high-quality image, we propose a two-stage Bracketing method named RT-IRE. In the first stage, we generate the high-quality reference feature to guide feature fusion, remove the degradation, and reconstruct HDR to get coarse results. The second stage learns the residuals between the coarse result and the GT, which further enhances and generates details. Extensive experiments show the effectiveness of the proposed module. In particular, RT-IRE won two champions in the NTIRE 2024 Bracketing Image Restoration and Enhancement Challenge.

关键词： Degradation computer vision Accuracy conferences Noise Superresolution Image restoration

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ChatVTG: Video Temporal Grounding via Chat with Video Dialogue Large Language Models

ChatVTG: Video Temporal Grounding via Chat with Video Dialog...

引用

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

作者： Mengxue Qu Xiaodong Chen Wu Liu Alicia Li Yao Zhao Institute of Information Science Beijing Jiaotong University Beijing Key Laboratory of Advanced Information Science and Network Technology University of Science and Technology of China Horace Mann School

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Video Temporal Grounding (VTG) aims to ground specific segments within an untrimmed video corresponding to the given natural language query. Existing VTG methods largely depend on supervised learning and extensive annotated data, which is labor-intensive and prone to human biases. To address these challenges, we present ChatVTG, a novel approach that utilizes Video Dialogue Large Language Models (LLMs) for zero-shot video temporal grounding. Our ChatVTG leverages Video Dialogue LLMs to generate multi-granularity segment captions and matches these captions with the given query for coarse temporal grounding, circumventing the need for paired annotation data. Furthermore, to obtain more precise temporal grounding results, we employ moment refinement for fine-grained caption proposals. Extensive experiments on three mainstream VTG datasets, including Charades-STA, ActivityNet-Captions, and TACoS, demonstrate the effectiveness of ChatVTG. Our ChatVTG surpasses the performance of current zero-shot methods.

关键词： computer vision Grounding Annotations Large language models conferences Supervised learning Natural languages

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VisTA-SR: Improving the Accuracy and Resolution of Low-Cost Thermal Imaging Cameras for Agriculture

VisTA-SR: Improving the Accuracy and Resolution of Low-Cost ...

引用

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

作者： Heesup Yun Sassoum Lo Christine H. Diepenbrock Brian N. Bailey J. Mason Earles University of California Davis CA

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Thermal cameras are an important tool for agricultural research because they allow for non-invasive measurement of plant temperature, which relates to important photochemical, hydraulic, and agronomic traits. Utilizing low-cost thermal cameras can lower the barrier to introducing thermal imaging in agricultural research and production. This paper presents an approach to improve the temperature accuracy and image quality of low-cost thermal imaging cameras for agricultural applications. Leveraging advancements in computer vision techniques, particularly deep learning networks, we propose a method, called VisTA-SR (Visual & Thermal Alignment and Super-Resolution Enhancement) that combines RGB and thermal images to enhance the capabilities of low-resolution thermal cameras. The research includes calibration and validation of temperature measurements, acquisition of paired image datasets, and the development of a deep learning network tailored for agricultural thermal imaging. Our study addresses the challenges of image enhancement in the agricultural domain and explores the potential of low-cost thermal cameras to replace high-resolution industrial cameras. Experimental results demonstrate the effectiveness of our approach in enhancing temperature accuracy and image sharpness, paving the way for more accessible and efficient thermal imaging solutions in agriculture.

关键词： Temperature measurement Deep learning computer vision Visualization Accuracy Plants (biology) Superresolution

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Unsupervised Domain Adaptation for Weed Segmentation Using Greedy Pseudo-labelling

Unsupervised Domain Adaptation for Weed Segmentation Using G...

引用

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

作者： Yingchao Huang Abdul Bais University of Regina Regina Canada

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Automatic weed identification based on RGB images with convolutional neural networks (CNN) is a new frontier of precision agriculture. However, the CNN models expect a large volume of labelled data. Their performance deteriorates across different fields due to varied agricultural contexts. To address this, we propose an unsupervised domain adaptation (DA) framework leveraging pseudo-labelling. Our method involves co-training labelled source data with pseudo-labelled target data. We introduce a novel greedy pseudo-labelling strategy to optimize pseudo-label selection, maximizing gains while minimizing overfitting risks. Monitoring overfitting with covariance helps detect fluctuations in class pixel counts during co-training, enhancing target performance. The proposed framework has demonstrated superior performance by evaluation against literature approaches, including the input-level DA methods with Fourier Transform, feature-level with CycleGAN methods and AdaptSegNet, and output-level with self-training. It is tested with the ROSE challenge dataset from different cameras and years with diverse plant stages. Particularly in challenging conditions for plants across different years with varied plant stages, the proposed method outperforms existing literature that struggles to surpass the baseline.

关键词： Precision agriculture Solid modeling computer vision Fourier transforms Fluctuations conferences Data models

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ViTA: An Efficient Video-to-Text Algorithm using VLM for RAG-based Video Analysis System

ViTA: An Efficient Video-to-Text Algorithm using VLM for RAG...

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

作者： Md Adnan Arefeen Biplob Debnath Md Yusuf Sarwar Uddin Srimat Chakradhar NEC Laboratories America University of Missouri-Kansas City

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Retrieval-augmented generation (RAG) is used in natural language processing (NLP) to provide query-relevant information in enterprise documents to large language models (LLMs). Such enterprise context enables the LLMs to generate more informed and accurate responses. When enterprise data is primarily videos, AI models like vision language models (VLMs) are necessary to convert information in videos into text. While essential, this conversion is a bottleneck, especially for large corpus of videos. It delays the timely use of enterprise videos to generate useful *** propose ViTA, a novel method that leverages two unique characteristics of VLMs to expedite the conversion process. As VLMs output more text tokens, they incur higher latency. In addition, large (heavyweight) VLMs can extract intricate details from images and videos, but they incur much higher latency per output token when compared to smaller (lightweight) VLMs that may miss details. To expedite conversion, ViTA first employs a lightweight VLM to quickly understand the gist or overview of an image or a video clip, and directs a heavyweight VLM (through prompt engineering) to extract additional details by using only a few (preset number of) output tokens. Our experimental results show that ViTA expedites the conversion time by as much as 43%, without compromising the accuracy of responses when compared to a baseline system that only uses a heavyweight VLM.

关键词： computer vision Accuracy Large language models conferences Natural language processing Data models pattern recognition

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Dynamic Addition of Noise in a Diffusion Model for Anomaly Detection

Dynamic Addition of Noise in a Diffusion Model for Anomaly D...

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

作者： Justin Tebbe Jawad Tayyub Otto von Guericke University Magdeburg Magdeburg Germany Endress + Hauser Maulburg Germany

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Diffusion models have found valuable applications in anomaly detection by capturing the nominal data distribution and identifying anomalies via reconstruction. Despite their merits, they struggle to localize anomalies of varying scales, especially larger anomalies such as entire missing components. Addressing this, we present a novel framework that enhances the capability of diffusion models, by extending the previous introduced implicit conditioning approach [24] in three significant ways. First, we incorporate a dynamic step size computation that allows for variable noising steps in the forward process guided by an initial anomaly prediction. Second, we demonstrate that denoising an only scaled input, without any added noise, outperforms conventional denoising process. Third, we project images in a latent space to abstract away from fine details that interfere with reconstruction of large missing components. Additionally, we propose a fine-tuning mechanism that facilitates the model to effectively grasp the nuances of the target domain. Our method undergoes rigorous evaluation on prominent anomaly detection datasets VisA, BTAD and MVTec yielding strong performance. Importantly, our framework effectively localizes anomalies regardless of their scale, marking a pivotal advancement in diffusion-based anomaly detection.

关键词： Location awareness Technological innovation computer vision conferences Noise reduction Noise Diffusion models

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