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Scattering Prompt Tuning: A Fine-tuned Foundation Model for SAR Object recognition

Scattering Prompt Tuning: A Fine-tuned Foundation Model for ...

引用

ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Weilong Guo Shengyang Liv Jian Yang Key Laboratory of Space Utilization Chinese Academy of Sciences Technology and the Engineering Center for Space Utilization Chinese Academy of Sciences University of Chinese Academy of Sciences

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Synthetic Aperture Radar (SAR) serves as a vital tool in various earth observation applications, providing robust imaging under challenging weather conditions. While the fine-tuned foundation models excel in many downstream tasks, they struggle with SAR object recognition because of SAR's unique imaging and scattering characteristics. In this study, we propose a novel approach named Scattering Prompt Tuning (SPT) based vision foundation model. It uses SAR image scattering information as a prompt and integrates learnable parameters into the pre-trained model's input space to help learn SAR's unique information. We also employ a lightweight Residual AdapterMLP for fine-tuning, design a Sequential Feature Aggregation (SFA) to selectively fuse features from different transformer blocks effectively, and develop a Dynamic Distributional Contrast loss (DCLoss) to maintain the proper distance between different objects in feature space. Additionally, a four-stage training strategy, incorporating semi-supervised learning, is deployed to enhance SAR object recognition performance further Our approach reaches a Top-1 accuracy of 37.9% and an AUROC of 0.83 on the final dataset, winning the first place in the SAR Classification track of PBVS 2024 Multi-modal Aerial View Object Classification Challenge, which is better than the latest advanced fine-tuned foundation models.

关键词： Training Scattering Imaging Semisupervised learning Radar imaging Radar polarimetry pattern recognition

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Situation Monitor: Diversity-Driven Zero-Shot Out-of-Distribution Detection using Budding Ensemble Architecture for Object Detection

Situation Monitor: Diversity-Driven Zero-Shot Out-of-Distrib...

引用

ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Syed Sha Qutub Michael Paulitsch Kay-Ulrich Scholl Neslihan Kose Cihangir Korbinian Hagn Fabian Oboril Gereon Hinz Alois Knoll Intel Labs Munich Germany Technical University of Munich Munich Germany

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

We introduce Situation Monitor, a novel zero-shot Out-of-Distribution (OOD) detection approach for transformer-based object detection models to enhance reliability in safety-critical machine learning applications such as autonomous driving. The Situation Monitor utilizes the Diversity-based Budding Ensemble Architecture (DBEA) and increases the OOD performance by integrating a diversity loss into the training process on top of the budding ensemble architecture, detecting Far-OOD samples and minimizing false positives on Near-OOD samples. Moreover, utilizing the resulting DBEA increases the model’s OOD performance and improves the calibration of confidence scores, particularly concerning the intersection over union of the detected objects. The DBEA model achieves these advancements with a 14% reduction in trainable parameters compared to the vanilla model. This signifies a substantial improvement in efficiency without compromising the model’s ability to detect OOD instances and calibrate the confidence scores accurately.

关键词： Training Adaptation models Zero-shot learning Computational modeling Scalability computer architecture Object detection

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VolRAFT: Volumetric Optical Flow Network for Digital Volume Correlation of Synchrotron Radiation-based Micro-CT Images of Bone-Implant Interfaces

VolRAFT: Volumetric Optical Flow Network for Digital Volume ...

引用

ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Tak Ming Wong Julian Moosmann Berit Zeller-Plumhoff Institute of Metallic Biomaterials Helmholtz-Zentrum Hereon Germany Institute of Materials Physics Helmholtz-Zentrum Hereon Germany

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

In materials science research, digital volume correlation (DVC) analysis is commonly used to track deformations and strains to elucidate morphology-function relationships. Optical flow-based DVC is particularly popular because of its robustness to estimate the correlation as a dense deformation vector. Recently, computer vision researchers showed that network-based optical flow approaches can outperform classical iterative optical flow approaches. In this paper, we propose a supervised machine learning approach for digital volume correlation, VolRAFT, that estimates the 3D displacement vector between the reference volume and the deformed volume. The proposed approach extends the state-of-the-art network-based optical flow method, RAFT, from 2D images to 3D volumes such that it predicts the volumetric displacement vector from the input volume pairs. Experiments show that the proposed network performs well in estimating different displacement fields when compared to cutting-edge iterative DVC methods for bone-implant materials based on high resolution synchrotron-radiation micro-computed tomography imaging data.

关键词： Training computer vision Materials science and technology Correlation Three-dimensional displays Deformation Tomography

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Conv-Adapter: Exploring Parameter Efficient Transfer Learning for ConvNets

Conv-Adapter: Exploring Parameter Efficient Transfer Learnin...

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ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Hao Chen Ran Tao Han Zhang Yidong Wang Xiang Li Wei Ye Jindong Wang Guosheng Hu Marios Savvides Carneige Mellon University Peking University Microsoft Research Asia Oosto

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

While parameter efficient tuning (PET) methods have shown great potential with transformer architecture on Natural Language Processing (NLP) tasks, their effectiveness with large-scale ConvNets is still under-studied on computer vision (CV) tasks. This paper proposes Conv-Adapter, a PET module designed for ConvNets. Conv-Adapter is light-weight, domain-transferable, and architecture-agnostic with generalized performance on different tasks. When transferring on downstream tasks, Conv- Adapter learns tasks-specific feature modulation to the intermediate representations of backbones while keeping the pre-trained parameters frozen. By introducing only a tiny amount of learnable parameters, e.g., only 3.5% full finetuning parameters of ResNet50. It can also be applied for transformer-based backbones. Conv-Adapter outperforms previous PET baseline methods and achieves comparable or surpasses the performance of full fine-tuning on 23 classification tasks of various domains. It also presents superior performance on the few-shot classification with an average margin of 3.39%. Beyond classification, Conv-Adapter can generalize to detection and segmentation tasks with more than 50% reduction of parameters but comparable performance to the traditional full fine-tuning 1

关键词： computer vision conferences Transfer learning Modulation Transformers Natural language processing Robustness

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Investigating Calibration and Corruption Robustness of Post-hoc Pruned Perception CNNs: An Image Classification Benchmark Study

Investigating Calibration and Corruption Robustness of Post-...

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ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Pallavi Mitra Gesina Schwalbe Nadja Klein Continental AG Germany University of Lübeck Germany TU Dortmund Germany

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Convolutional Neural Networks (CNNs) have achieved state-of-the-art performance in many computer vision tasks. However, high computational and storage demands hinder their deployment into resource-constrained environments, such as embedded devices. Model pruning helps to meet these restrictions by reducing the model size, while maintaining superior performance. Meanwhile, safety-critical applications pose more than just resource and performance constraints. In particular, predictions must not be overly confident, i.e., provide properly calibrated uncertainty estimations (proper uncertainty calibration), and CNNs must be robust against corruptions like naturally occurring input perturbations (natural corruption robustness). This work investigates the important trade-off between uncertainty calibration, natural corruption robustness, and performance for current state-of-research post-hoc CNN pruning techniques in the context of image classification tasks. Our study reveals that post-hoc pruning substantially improves the model’s uncertainty calibration, performance, and natural corruption robustness, sparking hope for safe and robust embedded CNNs. Furthermore, uncertainty calibration and natural corruption robustness are not mutually exclusive targets under pruning, as evidenced by the improved safety aspects obtained by post-hoc unstructured pruning with increasing compression.

关键词： Performance evaluation computer vision Uncertainty Image coding Computational modeling Robustness Calibration

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Low Latency Point Cloud Rendering with Learned Splatting

Low Latency Point Cloud Rendering with Learned Splatting

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ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Yueyu Hu Ran Gong Qi Sun Yao Wang Tandon School of Engineering New York University Tsinghua University

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Point cloud is a critical 3D representation with many emerging applications. Because of the point sparsity and irregularity, high-quality rendering of point clouds is challenging and often requires complex computations to recover the continuous surface representation. On the other hand, to avoid visual discomfort, the motion-to-photon latency has to be very short, under 10 ms. Existing rendering solutions lack in either quality or speed. To tackle these challenges, we present a framework that unlocks interactive, free-viewing and high-fidelity point cloud rendering. We train a generic neural network to estimate 3D elliptical Gaussians from arbitrary point clouds and use differentiable surface splatting to render smooth texture and surface normal for arbitrary views. Our approach does not require per-scene optimization, and enable real-time rendering of dynamic point cloud. Experimental results demonstrate the proposed solution enjoys superior visual quality and speed, as well as generalizability to different scene content and robustness to compression artifacts.

关键词： Point cloud compression Visualization Three-dimensional displays Visual communication Neural networks Rendering (computer graphics) Robustness

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Multi-modal Arousal and Valence Estimation under Noisy Conditions

Multi-modal Arousal and Valence Estimation under Noisy Condi...

引用

ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Denis Dresvyanskiy Maxim Markitantov Jiawei Yu Heysem Kaya Alexey Karpov Ulm University Germany ITMO University Russia St. Petersburg Federal Research Center of the Russian Academy of Sciences St. Petersburg Russia Department of Information and Computing Sciences Utrecht University The Netherlands

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Automatic emotion recognition has gained significant attention over the past two decades due to the central role that emotions play in human communication. While multi-modal systems demonstrate high performances on laboratory-controlled data, their validity on non-lab-controlled, namely ‘in-the-wild’ data, remains a challenge. This work investigates audio-visual deep learning approaches for emotion recognition in-the-wild, with a particular focus on the effectiveness of architectures based on fine-tuned Convolutional Neural Networks (CNN) and Public Dimensional Emotion Model (PDEM) for video and audio modality, respectively. We explore and compare various temporal modeling techniques (e.g., transformer architectures) and fusion strategies by leveraging the embeddings from developed multi-stage trained modality-specific Deep Neural Networks (DNN). The results are reported on the Af-fWild2 dataset following the Affective Behavior Analysis in-the-Wild 2024 (ABAW’24) challenge protocol. Our investigation highlights the complexities of robust multi-modal emotion recognition in an unconstrained environment, providing insights into the usage of various deep learning architectures for tackling this challenging task.

关键词： Deep learning Emotion recognition Protocols conferences Estimation computer architecture Transformers

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HSiPu² - A New Human Physical Fitness Action Dataset for recognition and 3D Reconstruction Evaluation

HSiPu<SUP>2</SUP> - A New Human Physical Fitness Action Data...

引用

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

作者： Zhang, Chuanlei Liu, Lixin Yao, Minda Chen, Wei Chen, Dufeng Wu, Yuliang Tianjin Univ Sci & Technol Tianjin Peoples R China China Univ Min & Technol Beijing Beijing Peoples R China China Univ Min & Technol Xuzhou Jiangsu Peoples R China Beijing Geotech & Invest Engn Inst Beijing Peoples R China Sichuan Staff Univ Sci & Technol Chengdu Peoples R China

ISBN: (纸本)9781665448994

In this paper a human physical fitness action feature dataset named HSiPu2 is introduced, which contains 8,044 action data sequences and 80,440 images. The dataset is built for three physical fitness actions, which are situp, push-up and pull-up, and each action data has two categories corresponding to standard and non-standard actions. Two cameras work as sensors to capture features from different views. HSiPu2 data set facilitates the evaluation of the performance of machine learning algorithms used in recognition and evaluation problems related to human behaviours recognition. The dataset is freely and publicly available online. A new recognition method based on deep learning techniques, including the two-branch multi-stage convolutional neural networks (CNNs) and long short-term memory (LSTM) networks with attention, are employed to learn the long-term dependencies from videos for human physical fitness action recognition on the HSiPu2. For comparison purpose, traditional machine learning models, including Decision Tree, Random Forest, SVM, Bagging, GBDT, AdaBoost, XGBoost and Voting, are utilized for the action recolonization. Furhtermore, HSiPu2 also can serve as a dataset to evaluating a human action 3D model.

关键词： Support vector machines Solid modeling Three-dimensional displays Sensor phenomena and characterization pattern recognition Convolutional neural networks Proposals

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Domain Targeted Synthetic Plant Style Transfer using Stable Diffusion, LoRA and ControlNet

Domain Targeted Synthetic Plant Style Transfer using Stable ...

引用

ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Zane K. J. Hartley Rob J. Lind Michael P. Pound Andrew P. French University of Nottingham Syngenta Jealott’s Hill International Research Centre

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Synthetic images can help alleviate much of the cost in the creation of training data for plant phenotyping-focused AI development. Synthetic-to-real style transfer is of particular interest to users of artificial data because of the domain shift problem created by training neural networks on images generated in a digital environment. In this paper we present a pipeline for synthetic plant creation and image-to-image style transfer, with a particular interest in synthetic to real domain adaptation targeting specific real datasets. Utilizing new advances in generative AI, we employ a combination of Stable diffusion, Low Ranked Adapters (LoRA) and ControlNets to produce an advanced system of style transfer. We focus our work on the core task of leaf instance segmentation, exploring both synthetic to real style transfer as well as inter-species style transfer and find that our pipeline makes numerous improvements over CycleGAN for style transfer, and the images we produce are comparable to real images when used as training data.

关键词： Training Instance segmentation computer vision Costs Generative AI conferences Pipelines

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vision Paper: Proof-Carrying Code Completions 39

Vision Paper: Proof-Carrying Code Completions

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39th ACM/ieee International conference on Automated Software Engineering workshops, ASEW 2024

作者： Kamran, Parnian Devanbu, Premkumar Stanford, Caleb University of California Davis United States

ISBN: (纸本)9798400712494

Code completions produced by today's large language models (LLMs) offer no formal guarantees. We propose proof-carrying code completions (PC3). In this paradigm, a high-resourced entity (the LLM provided by the server) must provide a code completion together with a proof of a chosen safety property which can be independently checked by a low-resourced entity (the user). In order to provide safety proofs without requiring the user to write specifications in formal logic, we statically generate preconditions for all dangerous function calls (i.e., functions that may violate the safety property) which must be proved by the *** demonstrate the main ideas, we provide a prototype implementation in the program verification language Dafny, and a case study focusing on file system vulnerabilities. Unlike Python code generated by GPT-4, Dafny code generated by PC3 provably avoids a common weakness related to path traversal (CWE-35), using a single generation attempt (k = 1) and a modest number of tokens (3, 350). Our tool is available as an open source repository at https://***/DavisPL/PCCC. © 2024 ACM.

关键词： Formal verification

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