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Refining Biologically Inconsistent Segmentation Masks with Masked Autoencoders

Refining Biologically Inconsistent Segmentation Masks with M...

引用

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

作者： Alexander Sauer Yuan Tian Joerg Bewersdorf Jens Rittscher Department of Engineering Science University of Oxford Department of Cell Biology Yale School of Medicine

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Microscopy images often feature regions of low signal-to-noise ratio (SNR) which leads to a considerable amount of ambiguity in the correct corresponding segmentation. This ambiguity can introduce inconsistencies in the segmentation mask which violate known biological constraints. In this work, we present a methodology which identifies areas of low SNR and refines the segmentation masks such that they are consistent with biological structures. Low SNR regions with uncertain segmentation are detected using model ensembling and selectively restored by a masked autoencoder (MAE) which leverages information about well-imaged surrounding areas. The prior knowledge of biologically consistent segmentation masks is directly learned from the *** validate our approach in the context of analysing intracellular structures, specifically by refining segmentation masks of mitochondria in expansion microscopy images with a global staining.

关键词： Image segmentation computer vision Mitochondria Microscopy conferences Biological system modeling Refining

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ILCOC: An Incremental Learning Framework based on Contrastive One-class Classifiers

ILCOC: An Incremental Learning Framework based on Contrastiv...

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

作者： Sun, Wenju Zhang, Jing Wang, Danyu Geng, Yangli-Ao Li, Qingyong Beijing Jiaotong Univ Beijing Key Lab Traff Data Anal & Min Beijing 100044 Peoples R China

ISBN: (纸本)9781665448994

In the class incremental learning, the number of classes to be handled dynamically raises with the number of considered tasks. The main challenge of this learning schema is catastrophic forgetting, that is the performance degradation on old tasks after learning new tasks. Existing incremental learning algorithms generally choose to train a multi-class classifier (e.g. softmax classifier), which learns a decision boundary to divide the feature space into several parts. Therefore, when new data arrive, the learned boundary will be updated and thus may cause forgetting. Compared with multi-class classifiers, a one-class classifier focuses on characterizing the distribution of a single class. As a result, the decision boundary learned for each category is tighter and does not change during learning new tasks. Inspired by this characteristic of one-class classifier, we propose a novel Incremental Learning framework based on Contrastive One-class Classifiers (ILCOC) to avoid catastrophic forgetting. Specifically, we train a specific one-class classifier for each category and parallelly use them to achieve incremental multi-class recognition. Besides, we design a scale-boundary loss, a classifier-contrastive loss and a negative-suppression loss to strengthen the comparability of classifiers outputs and the discrimination ability of each one-class classifier. We evaluate ILCOC on MNIST, CIFAR-10 and Tiny-ImageNet datasets, and the experimental results show that ILCOC achieves state-of-the-art performance.(1)

关键词： Degradation computer vision Heuristic algorithms conferences Computational modeling pattern recognition Classification algorithms

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computer vision-based Assistance System for the Visually Impaired Using Mobile Edge Artificial Intelligence

Computer Vision-based Assistance System for the Visually Imp...

引用

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

作者： Mahendran, Jagadish K. Barry, Daniel T. Nivedha, Anita K. Bhandarkar, Suchendra M. Kutir Technol Corp Vancouver BC Canada Denbar Robot Boston MA USA Mol Forecaster Inc Montreal PQ Canada Univ Georgia Dept Comp Sci Athens GA 30602 USA

ISBN: (纸本)9781665448994

Despite significant recent developments, visual assistance systems are still severely constrained by sensor capabilities, form factor, battery power consumption, computational resources and the use of traditional computer vision algorithms. Current visual assistance systems cannot adequately perform complex computer vision tasks that entail deep learning. We present the design and implementation of a novel visual assistance system that employs deep learning and point cloud processing to perform advanced perception tasks on a cost-effective, low-power mobile computing platform. The proposed system design circumvents the need for expensive, power-intensive Graphical Processing Unit (GPU)-based hardware required by most deep learning algorithms for real-time inference by employing instead edge Artificial Intelligence (AI) accelerators such as the Neural Compute Stick-2 (NCS2), model optimization techniques such as OpenVINO, and TensorFlow Lite, and smart depth sensors such as OpenCV AI Kit-Depth (OAK-D). Critical system design challenges such as training data collection, real-time capability, computational efficiency, power consumption, portability and reliability are addressed. The proposed system includes more advanced functionality than existing systems such as assessment of traffic conditions and detection and localization of hanging obstacles, crosswalks, moving obstacles and sudden elevation changes. The proposed system design incorporates an AI-based voice interface that allows for user-friendly interaction and control and is shown to realize a simple, cost-effective, power-efficient, portable and unobtrusive visual assistance device.

关键词： Edge Artificial Intelligence (AI) Mobile Computing Visual Assistance Device Deep Learning

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LTNet: Light Transfer Network for Depth Guided Image Relighting

LTNet: Light Transfer Network for Depth Guided Image Relight...

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

作者： Zhu, Yu Ding, Bosong Li, Chenghua Qian, Wanli Li, Fangya Yao, Yiheng Gang, Ruipeng Zhang, Chunjie Cheng, Jian Chinese Acad Sci CASIA Inst Automat Beijing 100190 Peoples R China NRTA Acad Broadcasting Sci Beijing 100866 Peoples R China Anhui Univ Sch Comp Sci & Technol Hefei 230601 Peoples R China BJTU Beijing Key Lab Adv Informat Sci & Network Techno Beijing Peoples R China CASIA Nanjing Artificial Intelligence Chip Res Nanjing 211100 Peoples R China Georgia Inst Technol Atlanta GA 30318 USA State Key Lab Media Convergence & Commun Beijing 100024 Peoples R China

ISBN: (纸本)9781665448994

Relighting is an interesting yet challenging low-level vision problem, which aims to re-render the scene with new light sources. In this paper, we introduce LTNet, a novel framework for image relighting. Unlike previous methods, we propose to solve this challenging problem by decoupling the enhancement process. Specifically, we propose to train a network that focuses on learning light variations. Our key insight is that light variations are the critical information to be learned because the scene stays unchanged during the light transfer process. To this end, we employ a global residual connection and corresponding residual loss for capturing light variations. Experimental results show that the proposed method achieves better visual quality on the VIDIT dataset in the NTIRE2021 relighting challenge.

关键词： Measurement Visualization computer vision conferences Focusing pattern recognition Task analysis

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Interpolation-Based Event Visual Data Filtering Algorithms

Interpolation-Based Event Visual Data Filtering Algorithms

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

作者： Marcin Kowalczyk Tomasz Kryjak Embedded Vision Systems Group AGH University of Krakow

The field of neuromorphic vision is developing rapidly, and event cameras are finding their way into more and more applications. However, the data stream from these sensors is characterised by significant noise. In this paper, we propose a method for event data that is capable of removing approximately 99% of noise while preserving the majority of the valid signal. We have proposed four algorithms based on the matrix of infinite impulse response (IIR) filters method. We compared them on several event datasets that were further modified by adding artificially generated noise and noise recorded with dynamic vision sensor. The proposed methods use about 30KB of memory for a sensor with a resolution of 1280 × 720 and is therefore well suited for implementation in embedded devices.

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LayoutFormer++: Conditional Graphic Layout Generation via Constraint Serialization and Decoding Space Restriction

LayoutFormer++: Conditional Graphic Layout Generation via Co...

引用

2023 ieee/CVF conference on computer vision and pattern recognition, CVPR 2023

作者： Jiang, Zhaoyun Guo, Jiaqi Sun, Shizhao Deng, Huayu Wu, Zhongkai Mijovic, Vuksan Yang, Zijiang James Lou, Jian-Guang Zhang, Dongmei Xi'an Jiaotong University China Microsoft Research Asia China Shanghai Jiaotong University China Beihang University China Microsoft

ISBN: (纸本)9798350301298

Conditional graphic layout generation, which generates realistic layouts according to user constraints, is a challenging task that has not been well-studied yet. First, there is limited discussion about how to handle diverse user constraints flexibly and uniformly. Second, to make the layouts conform to user constraints, existing work often sacrifices generation quality significantly. In this work, we propose LayoutFormer++ to tackle the above problems. First, to flexibly handle diverse constraints, we propose a constraint serialization scheme, which represents different user constraints as sequences of tokens with a predefined format. Then, we formulate conditional layout generation as a sequence-to-sequence transformation, and leverage encoder-decoder framework with Transformer as the basic architecture. Furthermore, to make the layout better meet user requirements without harming quality, we propose a decoding space restriction strategy. Specifically, we prune the predicted distribution by ignoring the options that definitely violate user constraints and likely result in low-quality layouts, and make the model samples from the restricted distribution. Experiments demonstrate that LayoutFormer++ outperforms existing approaches on all the tasks in terms of both better generation quality and less constraint violation. © 2023 ieee.

关键词： vision applications and systems

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Efficient Online Multi-Camera Tracking with Memory-Efficient Accumulated Appearance Features and Trajectory Validation

Efficient Online Multi-Camera Tracking with Memory-Efficient...

引用

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

作者： Lap Quoc Tran Huan Duc Vi Asilla

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Multi-camera tracking (MCT) plays a crucial role in various computer vision applications. However, accurate tracking of individuals across multiple cameras faces challenges, particularly with identity switches. In this paper, we present an efficient online MCT system that tackles these challenges through online processing. Our system leverages memory-efficient accumulated appearance features to provide stable representations of individuals across cameras and time. By incorporating trajectory validation using hierarchical agglomerative clustering (HAC) in overlapping regions, ID transfers are identified and rectified. Evaluation on the 2024 AI City Challenge Track 1 dataset [39] demonstrates the competitive performance of our system, achieving accurate tracking in both overlapping and non-overlapping camera networks. With a 40.3% HOTA score [29], our system ranked 9th in the challenge. The integration of trajectory validation enhances performance by 8% over the baseline, and the accumulated appearance features further contribute to a 17% improvement.

关键词： computer vision Accuracy Urban areas Cameras Real-time systems Trajectory Spatiotemporal phenomena

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Raising the Bar of AI-generated Image Detection with CLIP

Raising the Bar of AI-generated Image Detection with CLIP

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

作者： Davide Cozzolino Giovanni Poggi Riccardo Corvi Matthias Nießner Luisa Verdoliva University Federico II of Naples Technical University of Munich

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

The aim of this work is to explore the potential of pre-trained vision-language models (VLMs) for universal detection of AI-generated images. We develop a lightweight detection strategy based on CLIP features and study its performance in a wide variety of challenging scenarios. We find that, contrary to previous beliefs, it is neither necessary nor convenient to use a large domain-specific dataset for training. On the contrary, by using only a handful of example images from a single generative model, a CLIP-based detector exhibits surprising generalization ability and high robustness across different architectures, including recent commercial tools such as Dalle-3, Midjourney v5, and Firefly. We match the state-of-the-art (SoTA) on in-distribution data and significantly improve upon it in terms of generalization to out-of-distribution data (+6% AUC) and robustness to impaired/laundered data (+13%). Our project is available at https://***/ClipBased-SyntheticImageDetection/

关键词： Training computer vision conferences Detectors computer architecture Feature extraction Robustness

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Strategies to Leverage Foundational Model Knowledge in Object Affordance Grounding

Strategies to Leverage Foundational Model Knowledge in Objec...

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

作者： Arushi Rai Kyle Buettner Adriana Kovashka Department of Computer Science University of Pittsburgh PA USA Intelligent Systems Program University of Pittsburgh PA USA

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

An important task for intelligent systems is affordance grounding, where the goal is to locate regions on an object where an action can be performed. Past weakly supervised approaches learn from human-object interaction (HOI) by transferring grounding knowledge from exocentric to ego-centric views of an object. The use of HOI priors is inherently noisy and thus provides a limited source of supervision. To address this challenge, we identify that recent foundational models (i.e. VLMs and LLMs) can serve as auxiliary sources of knowledge for frameworks due to their vast world knowledge. In this work, we propose strategies to extract and leverage foundational model knowledge related to attributes and object parts to enhance an HOI-based affordance grounding framework. In particular, we propose to combine HOI and foundational model priors through (1) a spatial consistency loss and (2) heatmap aggregation. Our strategies result in mKLD and mNSS improvements, and insights suggest future directions for improving affordance grounding capabilities.

关键词： Heating systems computer vision Grounding Affordances conferences Pipelines pattern recognition

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NICO++: Towards Better Benchmarking for Domain Generalization

NICO++: Towards Better Benchmarking for Domain Generalizatio...

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2023 ieee/CVF conference on computer vision and pattern recognition, CVPR 2023

作者： Zhang, Xingxuan He, Yue Xu, Renzhe Yu, Han Shen, Zheyan Cui, Peng Tsinghua University Department of Computer Science China

Despite the remarkable performance that modern deep neural networks have achieved on independent and identically distributed (I.I.D.) data, they can crash under distribution shifts. Most current evaluation methods for do-main generalization (DG) adopt the leave-one-out strategy as a compromise on the limited number of domains. We propose a large-scale benchmark with extensive labeled domains named NICO++ along with more rational evaluation methods for comprehensively evaluating DC algorithms. To evaluate DG datasets, we propose two metrics to quantify covariate shift and concept shift, respectively. Two novel generalization bounds from the perspective of data construction are proposed to prove that limited concept shift and significant covariate shift favor the evaluation capability for generalization. Through extensive experiments, NlCO++ shows its superior evaluation capability compared with current DG datasets and its contribution in alleviating unfairness caused by the leak of oracle knowledge in model selection. The data and code for the benchmark based on NICO++ are available at https://***/xxgege/NICO-plus. © 2023 ieee.

关键词： Deep neural networks

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