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Generalized Few-Shot Meets Remote Sensing: Discovering Novel Classes in Land Cover Mapping via Hybrid Semantic Segmentation Framework

Generalized Few-Shot Meets Remote Sensing: Discovering Novel...

引用

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

作者： Zhuohong Li Fangxiao Lu Jiaqi Zou Lei Hu Hongyan Zhang Wuhan University China University of Geosciences

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Land-cover mapping is one of the vital applications in Earth observation, aiming at classifying each pixel’s land-cover type of remote-sensing images. As natural and human activities change the landscape, the land-cover map needs to be rapidly updated. However, discovering newly appeared land-cover types in existing classification systems is still a non-trivial task hindered by various scales of complex land objects and insufficient labeled data over a wide-span geographic area. In this paper, we propose a generalized few-shot segmentation-based framework, named SegLand, to update novel classes in high-resolution land-cover mapping. Specifically, the proposed framework is designed in three parts: (a) Data pre-processing: the base training set and the few-shot support sets of novel classes are analyzed and augmented; (b) Hybrid segmentation structure: Multiple base learners and a modified Projection onto Orthogonal Prototypes (POP) network are combined to enhance the base-class recognition and to dig novel classes from insufficient labels data; (c) Ultimate fusion: the semantic segmentation results of the base learners and POP network are reasonably fused. The proposed framework has won first place in the leaderboard of the OpenEarthMap Land Cover Mapping Few-Shot Challenge. Experiments demonstrate the superiority of the framework for automatically updating novel land-cover classes with limited labeled data.

关键词： Earth Training computer vision Semantic segmentation conferences Land surface Prototypes

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A System for Dense Monocular Mapping with a Fisheye Camera

A System for Dense Monocular Mapping with a Fisheye Camera

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

作者： Louis Gallagher Ganesh Sistu Jonathan Horgan John B. McDonald Department of Computer Science Maynooth University Co Kildare Ireland Valeo Vision Systems Tuam Ireland

We introduce a novel dense mapping system that uses a single monocular fisheye camera as the sole input sensor and incrementally builds a dense surfel representations of the scene’s 3D geometry. We extend an existing hybrid sparse-dense monocular SLAM system, reformulating the mapping pipeline in terms of the Kannala-Brandt fisheye camera model. Each frame is processed in its original undistorted fisheye form, with no attempt to remove distortion. To estimate depth, we introduce a new version of the PackNet depth estimation neural network adapted for fisheye inputs. We reformulate PackNet’s multi-view stereo self-supervised loss in terms of the Kannala-Brandt fisheye camera model. To encourage the network to learn metric depth during training, the pose network is weakly supervised with the camera’s ground-truth inter-frame velocity. To improve overall performance, we additionally provide sparse depth supervision from dataset LiDAR and SICK laser scanners. We demonstrate our system’s performance on the realworld KITTI-360 benchmark dataset. Our experimental results show that our system is capable of accurate, metric camera tracking and dense surface reconstruction within local windows. Our system operates within real-time processing rates and in challenging conditions. We direct the reader to the following video where the system can be seen in operation: https://***/Y-9q_wfqocs.

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A Multimodal Approach Integrating Convolutional and Recurrent Neural Networks for Alzheimer’s Disease Temporal Progression Prediction

A Multimodal Approach Integrating Convolutional and Recurren...

引用

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

作者： Durga Supriya HL Swetha Mary Thomas Sowmya Kamath S National Institute of Technology Karnataka India Mangalore Karnataka India Dept. of Information Technology Healthcare Analytics and Language Engineering (HALE) Lab National Institute of Technology Karnataka Mangaluru India

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Alzheimer’s Disease (AD) poses a substantial healthcare challenge marked by cognitive decline and a lack of definitive treatments. As the global population ages, the prevalence of AD escalates, underscoring the need for more advanced diagnostic techniques. Current single-modality methods have limitations, emphasizing the critical need for early detection and precise diagnosis to facilitate timely interventions and the development of effective therapies. We propose a novel multimodal medical diagnostic framework for AD employing a hybrid deep learning model. This framework integrates a 3D Convolutional Neural Network (CNN) to extract detailed intra-slice features from MRI volumes and a Long Short-Term Memory (LSTM) network to capture intricate inter-sequence patterns indicative of AD progression. By leveraging longitudinal MRI data alongside spatial, temporal, biomarkers, and cognitive scores, our framework significantly enhances diagnostic accuracy, particularly in the early stages of the disease. We incorporate Grad-CAM to enhance the interpretability of MRI-based diagnoses by highlighting relevant brain regions. This multimodal approach achieves a promising accuracy of 92.65%, outperforming state-of-the-art works by a margin of 6%.

关键词： Accuracy Three-dimensional displays Recurrent neural networks Magnetic resonance imaging Feature extraction Spatial databases pattern recognition

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OGRMPI: An Efficient Multiview Integrated Multiplane Image based on Occlusion Guided Residuals

OGRMPI: An Efficient Multiview Integrated Multiplane Image b...

引用

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

作者： Dae Yeol Lee Guan-Ming Su Peng Yin Dolby Laboratories Sunnyvale CA USA

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Multiplane Image (MPI) is a volumetric scene representation method that uses multiple layers of texture (RGB) and alpha (A) planes. It offers high deployability due to its compatibility with standard codecs and low rendering complexity. While existing MPI methods have shown promising results, they are constrained by either a limited range of pose span or necessitates transmitting multiple MPIs. In this paper, we present a novel framework to generate an efficient single MPI that integrates the information from multiple views. The key idea is to utilize the surface opacity estimates (A) to locate and retrieve occluded RGB pixels from other camera views that share matching depth, which we call Occlusion Guided Residuals (OGR). Additionally, we introduce an inter-layer texture filler, which is a learned RGB texture on intermediate depth between MPI layers to deal with scenes with continuous depth with a limited number of MPI layers. We composite MPI using the aforementioned RGB textures and refine alpha layers through training with multiview rendering supervision. Thus, through iterations of training, we jointly optimize scene opacity (A) and textures (RGB) leading to an accurate MPI representation. Experiments on various multiview image and video datasets demonstrate that the proposed method achieves state-of-the-art performances with data efficiency. Notably, with just 16 layers, the proposed method attains performance on par with other methods that use twice the layer number or fuse multiple MPIs.

关键词： Training Interpolation computer vision Accuracy Three-dimensional displays Fuses conferences

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3D Clothed Human Reconstruction from Sparse Multi-View Images

3D Clothed Human Reconstruction from Sparse Multi-View Image...

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

作者： Jin Gyu Hong Seung Young Noh Hee Kyung Lee Won Sik Cheong Ju Yong Chang Dept of ECE Kwangwoon University Seoul South Korea Electronics and Telecommunications Research Institute Daejeon South Korea

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Clothed human reconstruction based on implicit functions has recently received considerable attention. In this study, we explore the most effective 2D feature fusion method from multi-view inputs experimentally and propose a method utilizing the 3D coarse volume predicted by the network to provide a better 3D prior. We fuse 2D features using an attention-based method to obtain detailed geometric predictions. In addition, we propose depth and color projection networks that predict the coarse depth volume and the coarse color volume from the input RGB images and depth maps, respectively. Coarse depth volume and coarse color volume are used as 3D priors to predict occupancy and texture, respectively. Further, we combine the fused 2D features and 3D features extracted from our 3D prior to predict occupancy and propose a technique to adjust the influence of 2D and 3D features using learnable weights. The effectiveness of our method is demonstrated through qualitative and quantitative comparisons with recent multi-view clothed human reconstruction models.

关键词： Solid modeling computer vision Three-dimensional displays Image color analysis Fuses conferences Feature extraction

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Task Navigator: Decomposing Complex Tasks for Multimodal Large Language Models

Task Navigator: Decomposing Complex Tasks for Multimodal Lar...

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

作者： Feipeng Ma Yizhou Zhou Yueyi Zhang Siying Wu Zheyu Zhang Zilong He Fengyun Rao Xiaoyan Sun University of Science and Technology of China WeChat Tencent Inc. Institute of Artificial Intelligence Hefei Comprehensive National Science Center

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Inspired by the remarkable progress achieved by recent Large Language Models (LLMs), Multimodal Large Language Models (MLLMs) take LLMs as their brains, and have achieved surprising results in many downstream tasks by training on a large amount of task-specific data. However, when faced with complex tasks that require the collaboration of multiple capabilities, existing MLLMs recollect training data and retrain the model, ignoring the systematic utilization of LLMs and their possessed capabilities learned in downstream tasks. Inspired by the way humans tackle complex questions, in this paper, we propose a novel framework called Task Navigator. In our framework, LLMs act as navigators to chart a viable path for solving complex tasks and guide MLLMs through the process step by step. Specifically, LLMs iteratively break down sub-problems and refine them to be more reasonable and answerable, which are subsequently resolved by MLLMs to obtain relevant subanswers, until the LLMs have collected enough information to answer the initial question. Task Navigator provides an effective way to extend MLLMs to tackle complex tasks, thus broadening MLLMs’ applicability. To evaluate the performance of the proposed framework, we have curated a carefully designed benchmark called VersaChallenge. Experiments on VersaChallenge demonstrate the effectiveness of our proposed method.

关键词： Training computer vision Systematics Navigation Large language models conferences Training data

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Continual Diffusion with STAMINA: STack-And-Mask INcremental Adapters

Continual Diffusion with STAMINA: STack-And-Mask INcremental...

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

作者： James Seale Smith Yen-Chang Hsu Zsolt Kira Yilin Shen Hongxia Jin Samsung Research America Georgia Institute of Technology

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Recent work has demonstrated a remarkable ability to customize text-to-image diffusion models to multiple, fine-grained concepts in a sequential (i.e., continual) manner while only providing a few example images for each concept. This setting is known as continual diffusion. Here, we ask the question: Can we scale these methods to longer concept sequences without forgetting? Although prior work mitigates the forgetting of previously learned concepts, we show that its capacity to learn new tasks reaches saturation over longer sequences. We address this challenge by introducing a novel method, STack-And-Mask INcremental Adapters (STAMINA), which is composed of low-ranked attention-masked adapters and customized MLP tokens. STAMINA is designed to enhance the robust fine-tuning properties of LoRA for sequential concept learning via learnable hard-attention masks parameterized with low rank MLPs, enabling precise, scalable learning via sparse adaptation. Notably, all introduced trainable parameters can be folded back into the model after training, inducing no additional inference parameter costs. We show that STAMINA outperforms the prior SOTA for the setting of text-to-image continual customization on a 50-concept benchmark composed of landmarks and human faces, with no stored replay data. Additionally, we extended our method to the setting of continual learning for image classification, demonstrating that our gains also translate to state-of-the-art performance in this standard benchmark.

关键词： Training Continuing education Costs Text to image Benchmark testing Diffusion models pattern recognition

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PP-SAM: Perturbed Prompts for Robust Adaption of Segment Anything Model for Polyp Segmentation

PP-SAM: Perturbed Prompts for Robust Adaption of Segment Any...

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

作者： Md Mostafijur Rahman Mustafa Munir Debesh Jha Ulas Bagci Radu Marculescu The University of Texas at Austin Northwestern University

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

The Segment Anything Model (SAM), originally designed for general-purpose segmentation tasks, has been used recently for polyp segmentation. Nonetheless, fine-tuning SAM with data from new imaging centers or clinics poses significant challenges. This is because this necessitates the creation of an expensive and time-intensive annotated dataset, along with the potential for variability in user prompts during inference. To address these issues, we propose a robust fine-tuning technique, PP-SAM, that allows SAM to adapt to the polyp segmentation task with limited images. To this end, we utilize variable perturbed bounding box prompts (BBP) to enrich the learning context and enhance the model’s robustness to BBP perturbations during inference. Rigorous experiments on polyp segmentation benchmarks reveal that our variable BBP perturbation significantly improves model resilience. Notably, on Kvasir, 1-shot fine-tuning boosts the DICE score by 20% and 37% with 50 and 100-pixel BBP perturbations during inference, respectively. Moreover, our experiments show that 1-shot, 5-shot, and 10-shot PP-SAM with 50-pixel perturbations during inference outperform a recent state-of-the-art (SOTA) polyp segmentation method by 26%, 7%, and 5% DICE scores, respectively. Our results motivate the broader applicability of our PP-SAM for other medical imaging tasks with limited samples. Our implementation is available at https://***/SLDGroup/PP-SAM.

关键词： Training Image segmentation Adaptation models Perturbation methods conferences Robustness pattern recognition

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Key Patches Are All You Need: A Multiple Instance Learning Framework For Robust Medical Diagnosis

Key Patches Are All You Need: A Multiple Instance Learning F...

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

作者： D. J. Araújo M. R. Verdelho A. Bissoto J. C. Nascimento C. Santiago C. Barata LARSyS Instituto Superior Técnico Institute for Systems and Robotics Portugal Institute of Computing Recod.ai Lab University of Campinas Brazil Lisbon ELLIS Unit

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Deep learning models have revolutionized the field of medical image analysis, due to their outstanding performances. However, they are sensitive to spurious correlations, often taking advantage of dataset bias to improve results for in-domain data, but jeopardizing their generalization capabilities. In this paper, we propose to limit the amount of information these models use to reach the final classification, by using a multiple instance learning (MIL) framework. MIL forces the model to use only a (small) subset of patches in the image, identifying discriminative regions. This mimics the clinical procedures, where medical decisions are based on localized findings. We evaluate our framework on two medical applications: skin cancer diagnosis using dermoscopy and breast cancer diagnosis using mammography. Our results show that using only a subset of the patches does not compromise diagnostic performance for in-domain data, compared to the baseline approaches. However, our approach is more robust to shifts in patient demographics, while also providing more detailed explanations about which regions contributed to the decision. Code is available at: https://***/diogojpa99/Medical-Multiple-Instance-Learning.

关键词： Image analysis Pipelines MIMICs Focusing Breast cancer pattern recognition Mirrors

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The New Agronomists: Language Models are Experts in Crop Management

The New Agronomists: Language Models are Experts in Crop Man...

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

作者： Jing Wu Zhixin Lai Suiyao Chen Ran Tao Pan Zhao Naira Hovakimyan University of Illinois at Urbana-Champaign Cornell University University of South Florida University of Alabama

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Crop management plays a crucial role in determining crop yield, economic profitability, and environmental sustainability. Despite the availability of management guidelines, optimizing these practices remains a complex and multifaceted challenge. In response, previous studies have explored using reinforcement learning with crop simulators, typically employing simple neural-network-based reinforcement learning (RL) agents. Building on this foundation, this paper introduces a more advanced intelligent crop management system. This system uniquely combines RL, a language model (LM), and crop simulations facilitated by the Decision Support System for Agrotechnology Transfer (DSSAT). We utilize deep RL, specifically a deep Q-network, to train management policies that process numerous state variables from the simulator as observations. A novel aspect of our approach is the conversion of these state variables into more informative language, facilitating the language model’s capacity to understand states and explore optimal management practices. The empirical results reveal that the LM exhibits superior learning capabilities. Through simulation experiments with maize crops in Florida (US) and Zaragoza (Spain), the LM not only achieves state-of-the-art performance under various evaluation metrics but also demonstrates a remarkable improvement of over 49% in economic profit, coupled with reduced environmental impact when compared to baseline methods. Our code is available at https://***/jingwu6/LM_AG.

关键词： Decision support systems Profitability Noise Green products Crops Deep reinforcement learning pattern recognition

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