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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...

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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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Learning Video Representations of Human Motion from Synthetic Data

Learning Video Representations of Human Motion from Syntheti...

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2022 ieee/cvf conference on computer vision and pattern recognition, CVPR 2022

作者： Guo, Xi Wu, Wei Wang, Dongliang Su, Jing Su, Haisheng Gan, Weihao Huang, Jian Yang, Qin Beihang University China SenseTime Research

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

In this paper, we take an early step towards video representation learning of human actions with the help of large-scale synthetic videos, particularly for human motion representation enhancement. Specifically, we first introduce an automatic action-related video synthesis pipeline based on a photorealistic video game. A large-scale human action dataset named GATA (GTA Animation Transformed Actions) is then built by the proposed pipeline, which includes 8.1 million action clips spanning over 28K action classes. Based on the presented dataset, we design a contrastive learning framework for human motion representation learning, which shows significant performance improvements on several typical video datasets for action recognition, e.g., Charades, HAA 500 and NTU-RGB. Besides, we further explore a domain adaptation method based on cross-domain positive pairs mining to alleviate the domain gap between synthetic and realistic data. Extensive properties analyses of learned representation are conducted to demonstrate the effectiveness of the proposed dataset for enhancing human motion representation learning. © 2022 ieee.

关键词： Representation learning computer vision Pipelines Games Animation Data mining

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COOD: Combined out-of-distribution detection using multiple measures for anomaly & novel class detection in large-scale hierarchical classification

COOD: Combined out-of-distribution detection using multiple ...

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

作者： Laurens E. Hogeweg Rajesh Gangireddy Django Brunink Vincent J. Kalkman Ludo Cornelissen Jacob W. Kamminga Intel Naturalis Biodiversity Center University of Twente

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

High-performing out-of-distribution (OOD) detection, both anomaly and novel class, is an important prerequisite for the practical use of classification models. In this paper we focus on the species recognition task in images, concerned with large databases, a large number of fine-grained hierarchical classes, severe class imbalance, and varying image quality. We propose a framework for combining individual OOD measures into one combined OOD (COOD) measure using a supervised model. The individual measures are several existing state-of-the-art measures and several novel OOD measures developed with novel class detection and hierarchical class structure in mind. COOD was extensively evaluated on three large-scale (500k+ images) biodiversity datasets in the context of anomaly and novel class detection. We show that COOD outperforms individual, including state-of-the-art, OOD measures by a large margin in terms of TPR@1%FPR in the majority of experiments, e.g., improving detecting ImageNet images (OOD) from 54.3% to 83.3% for the iNaturalist 2018 dataset. SHAP (feature contribution) analysis shows that different individual OOD measures are essential for various tasks, indicating that multiple OOD measures and combinations are needed to generalize. Additionally, we show that explicitly considering ID images that are incorrectly classified for the original (species) recognition task is important for constructing high-performing OOD detection methods and for practical applicability. The framework can easily be extended or adapted to other tasks and media modalities.

关键词： Image quality computer vision Image recognition Databases conferences Biological system modeling Media

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PV-Cap: 3D Dynamic Scene Understanding Through Open Physics-based Vocabulary

PV-Cap: 3D Dynamic Scene Understanding Through Open Physics-...

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

作者： Hidetomo Sakaino Thao Nguyen Phuong Vinh Nguyen Duy Data Solution Dept. AI-Image Group (FCJ.ABC) FPT Consulting Japan FPT Software Department of Computer Science and Communications Engineering Waseda University

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Recently, large vision Language (VL) models, i.e., CLIP, have demonstrated impressive capabilities in training solely on internet-scale image-language pairs. Moreover, almost all VL models have tackled indoor objects under controlled illumination and camera views. However, outdoor 3D environments are time-varying uncontrolled scenes under natural phenomena. Therefore, captions from such unseen scenes and objects are hard to obtain in a state-of-the-art (SOTA) one-shot algorithm, resulting in insufficient captions. This paper proposes PV-Cap (Physics-based Vocabulary for Caption) for enhancing 3D scene understanding through enriched captions. Since many tasks in understanding 3D dynamic scenes are hard to deal with, PVCap aims to disentangle such complexities through multiple grouped Deep Learning and vision Language models step-wisely. Proposed i-VQA (iterative VQA) and 3D-CPP (3D Contrastive Physical-Scale Pretraining) extended from SOTA 2D-CLIP also contribute to generating physical and 3D-based captions. Using many images with 3D dynamic events, i.e., road scenes with traffic flow and accidents, experiments have demonstrated the usability and effectiveness of proposed PV-Cap over SOTA models in terms of segmentation and captions.

关键词： Deep learning Training Solid modeling Vocabulary Three-dimensional displays Roads Cameras

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End-to-End Neural Network Compression via l1/l2 Regularized Latency Surrogates

End-to-End Neural Network Compression via l1/l2 Regularized ...

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

作者： Anshul Nasery Hardik Shah Arun Sai Suggala Prateek Jain University of Washington ETH Zurich Google Research India

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Neural network (NN) compression via techniques such as pruning, quantization requires setting compression hyperparameters (e.g., number of channels to be pruned, bitwidths for quantization) for each layer either manually or via neural architecture search (NAS) which can be computationally expensive. We address this problem by providing an end-to-end technique that optimizes for model’s Floating Point Operations (FLOPs) via a novel $\frac{{{\ell _1}}}{{{\ell _2}}}$ latency surrogate. Our algorithm is versatile and can be used with many popular compression methods including pruning, low-rank factorization, and quantization, and can optimize for on-device latency. Crucially, it is fast and runs in almost the same amount of time as a single model training run; which is a significant training speed-up over standard NAS methods. For BERT compression on GLUE fine-tuning tasks, we achieve 50% reduction in FLOPs with only 1% drop in performance. For compressing MobileNetV3 on ImageNet-1K, we achieve 15% reduction in FLOPs without drop in accuracy, while still requiring 3× less training compute than SOTA NAS techniques. Finally, for transfer learning on smaller datasets, our technique identifies 1.2× - 1.4× cheaper architectures than standard MobileNetV3, EfficientNet suite of architectures at almost the same training cost and accuracy.

关键词： Training Quantization (signal) Image coding Accuracy Costs Transfer learning computer architecture

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NTIRE 2024 Challenge on Blind Enhancement of Compressed Image: Methods and Results

NTIRE 2024 Challenge on Blind Enhancement of Compressed Imag...

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

作者： Ren Yang Radu Timofte Bingchen Li Xin Li Mengxi Guo Shijie Zhao Li Zhang Zhibo Chen Dongyang Zhang Yash Arora Aditya Arora Yuanbin Chen Hui Tang Tao Wang Longxuan Zhao Bin Chen Tong Tong Qiao Mo Jingwei Bao Jinhua Hao Yukang Ding Hantang Li Ming Sun Chao Zhou Shuyuan Zhu Zhi Jin Wei Wang Dandan Zhan Jiawei Wu Jiahao Wu Luwei Tu Hongyu An Xinfeng Zhang Woon-Ha Yeo Wang-Taek Oh Young-Il Kim Han-Cheol Ryu Long Sun Mingjun Zhen Jinshan Pan Jiangxin Dong Jinhui Tang Yapeng Du Ao Li Ziyang He Lei Luo Ce Zhu Xin Yao Sunder Ali Khowaja IK Hyun Lee Jaeho Lee Seongwan Kim Sharif S M A Nodirkhuja Khujaev Roman Tsoy

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

This paper reviews the Challenge on Blind Enhancement of Compressed Image at NTIRE 2024, which aims at enhancing the quality of JPEG images which are compressed with unknown quality factor. The challenge requires that the total size of codes and pre-trained model(s) cannot exceed 300 MB, since we encourage solutions for blind enhancement with generalized models, instead of separately training several models for each quality factor. In this report, we summarize the detailed settings of the challenge, the final results, and the solutions proposed by the participants. The challenge has 129 registered participants and received 13 valid submissions. Several teams (including all TOP 3 teams) have publicly released the codes (see Sec. 4). They gauge the state-of-the-art of blind quality enhancement of compressed image.

关键词： Q-factor Training computer vision Image coding Codes Reviews conferences

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Gaussian Splatting Decoder for 3D-aware Generative Adversarial Networks

Gaussian Splatting Decoder for 3D-aware Generative Adversari...

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

作者： Florian Barthel Arian Beckmann Wieland Morgenstern Anna Hilsmann Peter Eisert Fraunhofer Heinrich Hertz Institute HHI Humboldt University of Berlin

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

NeRF-based 3D-aware Generative Adversarial Networks (GANs) like EG3D or GIRAFFE have shown very high rendering quality under large representational variety. However, rendering with Neural Radiance Fields poses challenges for 3D applications: First, the significant computational demands of NeRF rendering preclude its use on low-power devices, such as mobiles and VR/AR headsets. Second, implicit representations based on neural networks are difficult to incorporate into explicit 3D scenes, such as VR environments or video games. 3D Gaussian Splatting (3DGS) overcomes these limitations by providing an explicit 3D representation that can be rendered efficiently at high frame rates. In this work, we present a novel approach that combines the high rendering quality of NeRF-based 3D-aware GANs with the flexibility and computational advantages of 3DGS. By training a decoder that maps implicit NeRF representations to explicit 3D Gaussian Splatting attributes, we can integrate the representational diversity and quality of 3D GANs into the ecosystem of 3D Gaussian Splatting for the first time. Additionally, our approach allows for a high resolution GAN inversion and real-time GAN editing with 3D Gaussian Splatting *** page: ***/gaussian_decoder

关键词： Training Visualization Video games Three-dimensional displays Image resolution Generative adversarial networks Rendering (computer graphics)

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Repeat and Concatenate: 2D to 3D Image Translation with 3D to 3D Generative Modeling

Repeat and Concatenate: 2D to 3D Image Translation with 3D t...

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

作者： Abril Corona-Figueroa Hubert P. H. Shum Chris G. Willcocks Department of Computer Science Durham University Durham UK

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

This paper investigates a 2D to 3D image translation method with a straightforward technique, enabling correlated 2D X-ray to 3D CT-like reconstruction. We observe that existing approaches, which integrate information across multiple 2D views in the latent space, lose valuable signal information during latent encoding. Instead, we simply repeat and concatenate the 2D views into higher-channel 3D volumes and approach the 3D reconstruction challenge as a straightforward 3D to 3D generative modeling problem, sidestepping several complex modeling issues. This method enables the reconstructed 3D volume to retain valuable information from the 2D inputs, which are passed between channel states in a Swin UNETR backbone. Our approach applies neural optimal transport, which is fast and stable to train, effectively integrating signal information across multiple views without the requirement for precise alignment; it produces non-collapsed reconstructions that are highly faithful to the 2D views, even after limited training. We demonstrate correlated results, both qualitatively and quantitatively, having trained our model on a single dataset and evaluated its generalization ability across six datasets, including out-of-distribution samples.

关键词： Training Solid modeling Three-dimensional displays Uncertainty Encoding pattern recognition Iterative methods

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Test-Time Adaptation with SaLIP: A Cascade of SAM and CLIP for Zero-shot Medical Image Segmentation

Test-Time Adaptation with SaLIP: A Cascade of SAM and CLIP f...

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

作者： Sidra Aleem Fangyijie Wang Mayug Maniparambil Eric Arazo Julia Dietlmeier Kathleen Curran Noel E. O’Connor Suzanne Little ML-Labs Dublin City University ML-Labs University College Dublin Centre for Applied AI (CeADAR) University College Dublin Ireland Insight SFI Centre for Data Analytics Dublin City University

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

The Segment Anything Model (SAM) and CLIP are remarkable vision foundation models (VFMs). SAM, a prompt-driven segmentation model, excels in segmentation tasks across diverse domains, while CLIP is renowned for its zero-shot recognition capabilities. However, their unified potential has not yet been explored in medical image segmentation. To adapt SAM, to medical imaging, existing methods primarily rely on tuning strategies that require extensive data or prior prompts tailored to the specific task, making it particularly challenging when only a limited number of data samples are available. This work presents an in-depth exploration of integrating SAM and CLIP into a unified framework for medical image segmentation. Specifically, we propose a simple unified framework, SaLIP, for organ segmentation. Initially, SAM is used for part-based segmentation within the image, followed by CLIP to retrieve the mask corresponding to the region of interest (ROI) from the pool of SAM’s generated masks. Finally, SAM is prompted by the retrieved ROI to segment a specific organ. Thus, SaLIP is training/fine-tuning free and does not rely on domain expertise or labeled data for prompt engineering. Our method shows substantial enhancements in zero-shot segmentation, showcasing notable improvements in DICE scores across diverse segmentation tasks like brain (63.46%), lung (50.11%), and fetal head (30.82%), when compared to un-prompted SAM. Code and text prompts are available at SaLIP.

关键词： Image segmentation Adaptation models Filtering Inference mechanisms Lung Robustness pattern recognition

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Learning to Classify New Foods Incrementally Via Compressed Exemplars

Learning to Classify New Foods Incrementally Via Compressed ...

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

作者： Justin Yang Zhihao Duan Jiangpeng He Fengqing Zhu Elmore School of Electrical and Computer Engineering Purdue University West Lafayette Indiana USA

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Food image classification systems play a crucial role in health monitoring and diet tracking through image-based dietary assessment techniques. However, existing food recognition systems rely on static datasets characterized by a pre-defined fixed number of food classes. This contrasts drastically with the reality of food consumption, which features constantly changing data. Therefore, food image classification systems should adapt to and manage data that continuously evolves. This is where continual learning plays an important role. A challenge in continual learning is catastrophic forgetting, where ML models tend to discard old knowledge upon learning new information. While memory-replay algorithms have shown promise in mitigating this problem by storing old data as exemplars, they are hampered by the limited capacity of memory buffers, leading to an imbalance between new and previously learned data. To address this, our work explores the use of neural image compression to extend buffer size and enhance data diversity. We introduced the concept of continuously learning a neural compression model to adaptively improve the quality of compressed data and optimize the bitrates per pixel (bpp) to store more exemplars. Our extensive experiments, including evaluations on food-specific datasets including Food-101 and VFN-74, as well as the general dataset ImageNet-100, demonstrate improvements in classification accuracy. This progress is pivotal in advancing more realistic food recognition systems that are capable of adapting to continually evolving data. Moreover, the principles and methodologies we’ve developed hold promise for broader applications, extending their benefits to other domains of continual machine learning systems.

关键词： Continuing education Adaptation models computer vision Image coding conferences Machine learning Data models

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