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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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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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Capturing Cellular Topology in Multi-Gigapixel Pathology Images

Capturing Cellular Topology in Multi-Gigapixel Pathology Ima...

引用

IEEE computer Society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Wenqi Lu Simon Graham Mohsin Bilal Nasir Rajpoot Fayyaz Minhas Department of Computer Science University of Warwick UK

ISBN: (数字)9781728193601

ISBN: (纸本)9781728193618

In computational pathology, multi-gigapixel whole slide images (WSIs) are typically divided into small patches because of their extremely large size and memory requirements. However, following this strategy, one risks losing visual context which is very important in the development of machine learning models aimed at diagnostic and prognostic assessment of WSIs. In this paper, we propose a novel graph convolutional neural network based model (called Slide Graph) which overcomes these limitations by building a graph representation of the cellular architecture in an entire WSI in a bottom-up manner. We evaluate Slide Graph for prediction of the status of human epidermal growth factor receptor 2 (HER2) and progesterone receptor (PR) expression from WSIs of H&E stained tissue slides of breast cancer. We demonstrate that the proposed model outperforms previous state-of-the-art methods and is more computationally efficient. The proposed paradigm of WSI-level graphs can potentially be applied to other problems in computational pathology as well.

关键词： Pathology Machine learning Visualization Computational modeling Convolutional neural networks Principal component analysis Breast cancer

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Constraint-Aware Importance Estimation for Global Filter Pruning under Multiple Resource Constraints

Constraint-Aware Importance Estimation for Global Filter Pru...

引用

IEEE computer Society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Yu-Cheng Wu Chih-Ting Liu Bo-Ying Chen Shao-Yi Chien NTU IoX Center National Taiwan University

ISBN: (数字)9781728193601

ISBN: (纸本)9781728193618

Filter pruning is an efficient way to structurally remove the redundant parameters in convolutional neural network, where at the same time reduces the computation, memory storage and transfer cost. Recent state-of-the-art methods globally estimate the importance of each filter based on its impact to the loss and iteratively remove those with smaller values until the pruned network meets some resource constraints, such as the commonly used number (or ratio) of filter left. However, when there is a more practical constraint like the total number of FLOPs, they ignore its relation to the estimation of filter importance. We propose a novel method called Constraint-Aware Importance Estimation (CAIE) that integrates information of the impact on the given resource into the original importance estimation only based on loss when pruning each filter. Moreover, our CAIE can be generalized to the pruning problem under multiple resource constraints simultaneously. Extensive experiments show that under the same multiple resource constraints, the model pruned with our CAIE method can not only accurately meet the constraints but also achieve the optimal performance results when comparing to existing state-of-the-art methods.

关键词： Estimation Computational modeling Training Optimization Performance evaluation Taylor series computer vision

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Discriminant Distribution-Agnostic Loss for Facial Expression recognition in the Wild

Discriminant Distribution-Agnostic Loss for Facial Expressio...

引用

IEEE computer Society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Amir Hossein Farzaneh Xiaojun Qi Department of Computer Science Utah State University Logan UT USA

ISBN: (数字)9781728193601

ISBN: (纸本)9781728193618

Facial Expression recognition (FER) has demonstrated remarkable progress due to the advancement of deep Convolutional Neural Networks (CNNs). FER's goal as a visual recognition problem is to learn a mapping from the facial embedding space to a set of fixed expression categories using a supervised learning algorithm. Softmax loss as the de facto standard in practice fails to learn discriminative features for efficient learning. Center loss and its variants as promising solutions increase deep feature discriminability in the embedding space and enable efficient learning. They fundamentally aim to maximize intra-class similarity and inter-class separation in the embedding space. However, center loss and its variants ignore the underlying extreme class imbalance in challenging wild FER datasets. As a result, they lead to a separation bias toward majority classes and leave minority classes overlapped in the embedding space. In this paper, we propose a novel Discriminant Distribution-Agnostic loss (DDA loss) to optimize the embedding space for extreme class imbalance scenarios. Specifically, DDA loss enforces inter-class separation of deep features for both majority and minority classes. Any CNN model can be trained with the DDA loss to yield well separated deep feature clusters in the embedding space. We conduct experiments on two popular large-scale wild FER datasets (RAF-DB and AffectNet) to show the discriminative power of the proposed loss function.

关键词： Face recognition Feature extraction Visualization Emotion recognition Clustering algorithms Linear programming

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Supervised Learning of Edges and Object Boundaries

Supervised Learning of Edges and Object Boundaries

引用

conference on computer vision and pattern recognition (CVPR)

作者： P. Dollar Zhuowen Tu S. Belongie Computer Science & Engineering University of California San Diego USA Lab of Neuro Imaging University of California Los Angeles USA

Edge detection is one of the most studied problems in computer vision, yet it remains a very challenging task. It is difficult since often the decision for an edge cannot be made purely based on low level cues such as gradient, instead we need to engage all levels of information, low, middle, and high, in order to decide where to put edges. In this paper we propose a novel supervised learning algorithm for edge and object boundary detection which we refer to as Boosted Edge Learning or BEL for short. A decision of an edge point is made independently at each location in the image; a very large aperture is used providing significant context for each decision. In the learning stage, the algorithm selects and combines a large number of features across different scales in order to learn a discriminative model using an extended version of the Probabilistic Boosting Tree classification algorithm. The learning based framework is highly adaptive and there are no parameters to tune. We show applications for edge detection in a number of specific image domains as well as on natural images. We test on various datasets including the Berkeley dataset and the results obtained are very good.

关键词： Supervised learning Image edge detection Object detection computer science computer vision Apertures Boosting Application software Detectors Neuroimaging

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PEA: Improving the Performance of ReLU Networks for Free by Using Progressive Ensemble Activations

PEA: Improving the Performance of ReLU Networks for Free by ...

引用

IEEE computer Society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Á kos Utasi Continental AI Development Center Budapest

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487405

In recent years novel activation functions have been proposed to improve the performance of neural networks, and they show superior performance compared to the ReLU counterpart. However, there are environments, where the availability of complex activations is limited, and usually only the ReLU is supported. In this paper we propose methods that can be used to improve the performance of ReLU networks by using these efficient novel activations during model training. More specifically, we propose ensemble activations that are composed of the ReLU and one of these novel activations. Furthermore, the coefficients of the ensemble are neither fixed nor learned, but are progressively updated during the training process in a way that by the end of the training only the ReLU activations remain active in the network and the other activations can be removed. This means that in inference time the network contains ReLU activations only. We perform extensive evaluations on the ImageNet classification task using various compact network architectures and various novel activation functions. Results show 0.2–0.8% top-1 accuracy gain, which confirms the applicability of the proposed methods. Furthermore, we demonstrate the proposed methods on semantic segmentation and we boost the performance of a compact segmentation network by 0.34% mIOU on the Cityscapes dataset.

关键词： Training Image segmentation computer vision conferences Semantics Neural networks Network architecture

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Classifier Guided Cluster Density Reduction for Dataset Selection

Classifier Guided Cluster Density Reduction for Dataset Sele...

引用

IEEE computer Society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Cheng Chang Keyu Long Zijian Li Himanshu Rai Layer 6 AI

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

In this paper, we address the challenge of selecting an optimal dataset from a source pool with annotations to enhance performance on a target dataset derived from a different source. This is important in scenarios where it is hard to afford on-the-fly dataset annotation and is also the theme of the second Visual Data Understanding (VDU) Challenge. Our solution, the Classifier Guided Cluster Density Reduction (CCDR) framework, operates in two stages. Initially, we employ a filtering technique to identify images that align with the target dataset’s distribution. Subsequently, we implement a graph-based cluster density reduction method, steered by a classifier that approximates the distance between the target distribution and source distribution. This classifier is trained to distinguish between images that resemble the target dataset and those that do not, facilitating the pruning process shown in Figure 1. Our approach maintains a balance between selecting pertinent images that match the target distribution and eliminating redundant ones that do not contribute to the enhancement of the detection model. We demonstrate the superiority of our method over various baselines in object detection tasks, particularly in optimizing the training set distribution on the region100 dataset. We have released our code here: https://***/himsR/DataCVChallenge-2024/tree/main

关键词： Training Visualization computer vision Codes Annotations Filtering conferences

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Adapting the Segment Anything Model During Usage in Novel Situations

Adapting the Segment Anything Model During Usage in Novel Si...

引用

IEEE computer Society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Robin Schön Julian Lorenz Katja Ludwig Rainer Lienhart Universität Augsburg Augsburg

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

The interactive segmentation task consists in the creation of object segmentation masks based on user interactions. The most common way to guide a model towards producing a correct segmentation consists in clicks on the object and background. The recently published Segment Anything Model (SAM) supports a generalized version of the interactive segmentation problem and has been trained on an object segmentation dataset which contains 1.1B masks. Though being trained extensively and with the explicit purpose of serving as a foundation model, we show significant limitations of SAM when being applied for interactive segmentation on novel domains or object types. On the used datasets, SAM displays a failure rate FR 30 @90 of up to 72.6%. Since we still want such foundation models to be immediately applicable, we present a framework that can adapt SAM during immediate usage. For this we will leverage the user interactions and masks, which are constructed during the interactive segmentation process. We use this information to generate pseudo-labels, which we use to compute a loss function and optimize a part of the SAM model. The presented method causes a relative reduction of up to 48.1% in the FR 20 @85 and 46.6% in the FR 30 @90 metrics.

关键词： Measurement Adaptation models Image segmentation computer vision Computational modeling conferences Object segmentation

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Activity Analysis in Microtubule Videos by Mixture of Hidden Markov Models

Activity Analysis in Microtubule Videos by Mixture of Hidden...

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conference on computer vision and pattern recognition (CVPR)

作者： A. Altinok M. El-Saban A.J. Peck L. Wilson S.C. Feinstein B.S. Manjunath K. Rose Computer Science University of California Santa Barbara CA USA Electrical and Computer Engineering University of California Santa Barbara CA USA Molecular Cellular and Developmental Biology University of California Santa Barbara CA USA

We present an automated method for the tracking and dynamics modeling of microtubules -a major component of the cytoskeleton- which provides researchers with a previously unattainable level of data analysis and quantification capabilities. The proposed method improves upon the manual tracking and analysis techniques by i) increasing accuracy and quantified sample size in data collection, ii) eliminating user bias and standardizing analysis, iii) making available new features that are impractical to capture manually, iv) enabling statistical extraction of dynamics patterns from cellular processes, and v) greatly reducing required time for entire studies. An automated procedure is proposed to track each resolvable microtubule, whose aggregate activity is then modeled by mixtures of Hidden Markov Models to uncover dynamics patterns of underlying cellular and experimental conditions. Our results support manually established findings on an actual microtubule dataset and illustrate how automated analysis of spatial and temporal patterns offers previously unattainable insights to cellular processes.

关键词： Videos Hidden Markov models pattern analysis Cells (biology) Biological system modeling computer vision Biology computing Data analysis Data mining Application software

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