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Deepfake Catcher: Can a Simple Fusion be Effective and Outperform Complex DNNs?

Deepfake Catcher: Can a Simple Fusion be Effective and Outpe...

引用

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

作者： Akshay Agarwal Nalini Ratha IISER Bhopal India University at Buffalo USA

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Despite having completely different configurations, deep learning architectures learn a specific set of features that are common across architectures. For example, the initial few layers learn the low-level edge features from the images. Based on this fact, in this research, we have showcased the potential of deep neural network fusion for simple and effective deepfake detection. The advantage of building an architecture in such a manner is to build a low-power-consuming and accurate defense that can be deployed on mobile devices. To utilize the pre-trained knowledge and obtain downstream task-specific knowledge, we have identified a breakpoint in different networks and divided the obtained knowledge of a network into fixed and adaptive information. We have kept the fixed knowledge intact while modifying the adaptive knowledge along with entirely new knowledge for the deepfake detection task. In the end, the decision of multiple deep architectures trained based on their breakpoint are combined for improved performance. Extensive comparisons performed with existing state-of-the-art architectures demonstrate the effectiveness of the proposed deepfake detection algorithm. The proposed algorithm not only surpasses the existing state-of-the-art (SOTA) algorithms but also needs low computational power. We have further challenged the proposed algorithm by evaluating it by collecting real-world deepfake images.

关键词： Knowledge engineering Image quality Deepfakes Social networking (online) Architecture Deep architecture computer architecture

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Histopathological Image Classification with Cell Morphology Aware Deep Neural Networks

Histopathological Image Classification with Cell Morphology ...

引用

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

作者： Andrey Ignatov Josephine Yates Valentina Boeva Computer Vision Lab ETH Zurich Computational Cancer Genomics Group

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Histopathological images are widely used for the analysis of diseased (tumor) tissues and patient treatment selection. While the majority of microscopy image processing was previously done manually by pathologists, recent advances in computer vision allow for accurate recognition of lesion regions with deep learning-based solutions. Such models, however, usually require extensive annotated datasets for training, which is often not the case in the considered task, where the number of available patient data samples is very limited. To deal with this problem, we propose a novel DeepCMorph model pre-trained to learn cell morphology and identify a large number of different cancer types. The model consists of two modules: the first one performs cell nuclei segmentation and annotates each cell type, and is trained on a combination of 8 publicly available datasets to ensure its high generalizability and robustness. The second module combines the obtained segmentation map with the original microscopy image and is trained for the downstream task. We pre-trained this module on the Pan-Cancer TCGA dataset consisting of over 270K tissue patches extracted from 8736 diagnostic slides from 7175 patients. The proposed solution achieved a new state-of-the-art performance on the dataset under consideration, detecting 32 cancer types with over 82% accuracy and outperforming all previously proposed solutions by more than 4%. We demonstrate that the resulting pre-trained model can be easily fine-tuned on smaller microscopy datasets, yielding superior results compared to the current top solutions and models initialized with ImageNet weights. The codes and pre-trained models presented in this paper are available at: https://***/aiff22/DeepCMorph

关键词： Image segmentation computer vision Analytical models Accuracy Microscopy Microprocessors Computational modeling

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HyperLeaf2024 – A Hyperspectral Imaging Dataset for Classification and Regression of Wheat Leaves

HyperLeaf2024 – A Hyperspectral Imaging Dataset for Classif...

引用

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

作者： William Michael Laprade Pawel Pieta Svetlana Kutuzova Jesper Cairo Westergaard Mads Nielsen Svend Christensen Anders Bjorholm Dahl Department of Applied Mathematics and Computer Science Technical University of Denmark Department of Computer Science University of Copenhagen Department of Plant and Environmental Sciences University of Copenhagen

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Hyperspectral imaging is a widely used method in remote sensing, particularly for use in airborne and satellite-based land surveillance. Its versatility is, however, much larger and has also seen usage in everything ranging from food processing and surveillance to astronomy and waste sorting. It is also gaining inroads with agricultural research. With most available datasets focusing on per-pixel classification, there is, however, a potential for hyperspectral whole-image analysis, but there is a severe lack of datasets for whole-image analysis. To help fill this gap and facilitate methodological development in whole-image hyperspectral image analysis, we introduce the Hy-perLeaf2024 dataset. The dataset consists of 2410 hyper-spectral images of wheat leaves, along with associated classification and regression targets at both the leaf level and the plot level. In addition to the dataset, we also provide experiments showing the importance of pretraining and highlighting the future research direction in whole-image hyper-spectral image analysis.

关键词： Adaptation models Image analysis Image coding Surveillance conferences Focusing Distance measurement

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DSTCFuse: A Method based on Dual-cycled Cross-awareness of Structure Tensor for Semantic Segmentation via Infrared and Visible Image Fusion

DSTCFuse: A Method based on Dual-cycled Cross-awareness of S...

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

作者： Xuan Li Rongfu Chen Jie Wang Lei Ma Li Cheng Haiwen Yuan School of Electrical and Information Engineering Wuhan Institute of Technology Hubei Key Laboratory of Optical Information and Pattern Recognition

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Multi-modality information fusion can compensate deficiencies of single modality and provide rich scene information for 2D semantic segmentation. However, the inconsistency in the feature space between different modalities may lead to poor presentation of objects and that would affect subsequent segmented effectiveness. The idea of modal transition can reduce the modal differences and avoid biased processing during the fusion process, but it is hard to perfectly retain the contents of the source images. To address these challenges, a fusion method based on dual-cycled cross-awareness of structure tensor is proposed. Firstly, we propose a dual-cycle modality transition network based on cross-awareness consistency to learn the differences in feature space from different modalities. Secondly, a set of globally structure-tensor preserving modules are designed to enhance the capabilities of the network in capturing complementary features and perceiving global modal consistency. Under the joint constraint of globally structure-tensor awareness loss and cross-awareness loss, our network achieves a robust mapping of feature space from visible to pseudo-infrared images without relying on Ground-Truth. Finally, the pseudo-infrared images that inherit the superior qualities of two modalities are fused with the original infrared images directly, which effectively reduces the complexity of fusion. Extensive comparative experiments show that our method outperforms state-of-the-art methods in qualitative and quantitative evaluation.

关键词： Degradation computer vision Tensors Semantic segmentation conferences Interference pattern recognition

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Language-guided Multi-modal Emotional Mimicry Intensity Estimation

Language-guided Multi-modal Emotional Mimicry Intensity Esti...

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

作者： Feng Qiu Wei Zhang Chen Liu Lincheng Li Heming Du Tianchen Guo Xin Yu Netease Fuxi AI Lab The University of Queensland

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Emotional Mimicry Intensity (EMI) estimation aims to identify the intensity of mimicry exhibited by individuals in response to observed emotions. The challenge in EMI estimation lies in discerning nuanced facial expression cues on mimicry behaviors based on the seed video and the text instructions. In this paper, we propose a multi-modal EMI estimation framework by leveraging visual, auditory, and textual modalities to capture a comprehensive emotional profile. We first extract representations for each modality separately and then fuse the modality-specific representations via a Temporal Segment Network, optimizing for temporal coherence and emotional context. Furthermore, we find that participants demonstrate notable proficiency in mimicking text instructions, yet exhibit less effectiveness in replicating facial expressions and vocal tones. In light of this, we design a contrastive learning mechanism to refine the extracted feature based on textual guidance. By doing so, features derived from similar text instructions are closely aligned, enhancing the estimation of emotional mimicry intensity by leveraging the dominant textual modality. Experiments conducted on the Hume-Vidmimic2 dataset illustrate the effectiveness of our framework in EMI estimation. Our framework is recognized as the leading solution in the Emotional Mimicry Intensity (EMI) Estimation Challenge at the 6th Workshop and Competition on Affective Behavior Analysis in-the-wild (ABAW). More information for the Competition can be found in: 6th ABAW.

关键词： Visualization Emotion recognition computer vision Fuses conferences Electromagnetic interference Estimation

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Pairwise Teacher-Student Network for Semi-Supervised Hashing

Pairwise Teacher-Student Network for Semi-Supervised Hashing

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

作者： Shifeng Zhang Jianmin Li Bo Zhang Department of Computer Science and Technology Institute for Artificial Intelligence State Key Lab of Intelligent Technology and Systems Beijing National Research Center for Information Science and Technology Tsinghua University Beijing China

ISBN: (数字)9781728125060

ISBN: (纸本)9781728125077

Hashing method maps similar high-dimensional data to binary hashcodes with smaller hamming distance, and it has received broad attention due to its low storage cost and fast retrieval speed. Pairwise similarity is easily obtained and widely used for retrieval, and most supervised hashing algorithms are carefully designed for the pairwise supervisions. As labeling all data pairs is difficult, semi-supervised hashing is proposed which aims at learning efficient codes with limited labeled pairs and abundant unlabeled ones. Existing methods build graphs to capture the structure of dataset, but they are not working well for complex data as the graph is built based on the data representations and determining the representations of complex data is difficult. In this paper, we propose a novel teacher-student semi-supervised hashing framework in which the student is trained with the pairwise information produced by the teacher network. The network follows the smoothness assumption, which achieves consistent distances for similar data pairs so that the retrieval results are similar for neighborhood queries. Experiments on large-scale datasets show that the proposed method reaches impressive gain over the supervised baselines and is superior to state-of-the-art semi-supervised hashing methods.

关键词： Training Hamming distance Semantics Data models Semisupervised learning Perturbation methods Labeling

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Focusing on What Matters: Fine-grained Medical Activity recognition for Trauma Resuscitation via Actor Tracking

Focusing on What Matters: Fine-grained Medical Activity Reco...

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

作者： Wenjin Zhang Keyi Li Sen Yang Sifan Yuan Ivan Marsic Genevieve J. Sippel Mary S. Kim Randall S. Burd Rutgers University Waymo Children’s National Hospital

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Trauma is a leading cause of mortality worldwide, with about 20% of these deaths being preventable. Most of these preventable deaths result from errors during the initial resuscitation of injured patients. Decision support has been evaluated as an approach to support teams during this phase to reduce errors. Existing systems require manual data entry and monitoring, which makes tasks challenging to accomplish in a time-critical setting. This paper identified the specific challenges of achieving effective decision support in trauma resuscitation based on computer vision techniques, including complex backgrounds, crowded scenes, fine-grained activities, and a scarcity of labeled data. To address the first three challenges, the proposed system involved an actor tracker that identifies individuals, allowing the system to focus on actor-specific features. Video Masked Autoencoder (Video-MAE) was used to overcome the issue of insufficient labeled data. This approach enables self-supervised learning using unlabeled video content, improving feature representation for medical activities. For more reliable performance, an ensemble fusion method was introduced. This technique combines predictions from consecutive video clips and different actors. Our method outperformed existing approaches in identifying fine-grained activities, providing a solution for activity recognition in trauma resuscitation and similar complex domains.

关键词： computer vision conferences Focusing Self-supervised learning Manuals Activity recognition Time factors

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AMC-Loss: Angular Margin Contrastive Loss for Improved Explainability in Image Classification

AMC-Loss: Angular Margin Contrastive Loss for Improved Expla...

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

作者： Hongjun Choi Anirudh Som Pavan Turaga Geometric Media Lab School of Arts Media and Engineering Arizona State University

ISBN: (数字)9781728193601

ISBN: (纸本)9781728193618

Deep-learning architectures for classification problems involve the cross-entropy loss sometimes assisted with auxiliary loss functions like center loss, contrastive loss and triplet loss. These auxiliary loss functions facilitate better discrimination between the different classes of interest. However, recent studies hint at the fact that these loss functions do not take into account the intrinsic angular distribution exhibited by the low-level and high-level feature representations. This results in less compactness between samples from the same class and unclear boundary separations between data clusters of different classes. In this paper, we address this issue by proposing the use of geometric constraints, rooted in Riemannian geometry. Specifically, we propose Angular Margin Contrastive Loss (AMC-Loss), a new loss function to be used along with the traditional cross-entropy loss. The AMC-Loss employs the discriminative angular distance metric that is equivalent to geodesic distance on a hypersphere manifold such that it can serve a clear geometric interpretation. We demonstrate the effectiveness of AMC-Loss by providing quantitative and qualitative results. We find that although the proposed geometrically constrained loss-function improves quantitative results modestly, it has a qualitatively surprisingly beneficial effect on increasing the interpretability of deep-net decisions as seen by the visual explanations generated by techniques such as the Grad-CAM. Our code is available at https://***/hchoi71/AMC-Loss.

关键词： Training Task analysis Feature extraction Euclidean distance Airplanes computer vision Media

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Deep-Learning-Based Aerial Image Classification for Emergency Response Applications Using Unmanned Aerial Vehicles

Deep-Learning-Based Aerial Image Classification for Emergenc...

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

作者： Christos Kyrkou Theocharis Theocharides KIOS Research and Innovation Center of Excellence University of Cyprus Nicosia Cyprus Department of Electrical and Computer Engineering KIOS Research and Innovation Center of Excellence University of Cyprus Nicosia Cyprus

ISBN: (数字)9781728125060

ISBN: (纸本)9781728125077

Unmanned Aerial Vehicles (UAVs), equipped with camera sensors can facilitate enhanced situational awareness for many emergency response and disaster management applications since they are capable of operating in remote and difficult to access areas. In addition, by utilizing an embedded platform and deep learning UAVs can autonomously monitor a disaster stricken area, analyze the image in real-time and alert in the presence of various calamities such as collapsed buildings, flood, or fire in order to faster mitigate their effects on the environment and on human population. To this end, this paper focuses on the automated aerial scene classification of disaster events from on-board a UAV. Specifically, a dedicated Aerial Image Database for Emergency Response (AIDER) applications is introduced and a comparative analysis of existing approaches is performed. Through this analysis a lightweight convolutional neural network (CNN) architecture is developed, capable of running efficiently on an embedded platform achieving ~3x higher performance compared to existing models with minimal memory requirements with less than 2% accuracy drop compared to the state-of-the-art. These preliminary results provide a solid basis for further experimentation towards real-time aerial image classification for emergency response applications using UAVs.

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Simple In-place Data Augmentation for Surveillance Object Detection

Simple In-place Data Augmentation for Surveillance Object De...

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

作者： Munkh-Erdene Otgonbold Ganzorig Batnasan Munkhjargal Gochoo Department of Computer Science and Software Engineering United Arab Emirates University UAE Department of Electronics Mongolian University of Science and Technology Mongolia Emirates Center for Mobility Research United Arab Emirates University UAE

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Motivated by the need to improve model performance in traffic monitoring tasks with limited labeled samples, we propose a straightforward augmentation technique tailored for object detection datasets, specifically designed for stationary camera-based applications. Our approach focuses on placing objects in the same positions as the originals to ensure its effectiveness. By applying in-place augmentation on objects from the same camera input image, we address the challenge of overlapping with original and previously selected objects. Through extensive testing on two traffic monitoring datasets, we illustrate the efficacy of our augmentation strategy in improving model performance, particularly in scenarios with limited labeled samples and imbalanced class distributions. Notably, our method achieves comparable performance to models trained on the entire dataset while utilizing only 8.5 percent of the original data. Moreover, we report significant improvements, with mAP@.5 increasing from 0.4798 to 0.5025, and the mAP@.5:.95 rising from 0.29 to 0.3138 on the FishEye8K dataset. These results highlight the potential of our augmentation approach in enhancing object detection models for traffic monitoring applications.

关键词： Image segmentation Surveillance Scalability conferences Object detection Data augmentation Cameras

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