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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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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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Multi-Objective Hardware Aware Neural Architecture Search using Hardware Cost Diversity

Multi-Objective Hardware Aware Neural Architecture Search us...

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

作者： Nilotpal Sinha Peyman Rostami Abd El Rahman Shabayek Anis Kacem Djamila Aouada SnT University of Luxembourg

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Hardware-aware Neural Architecture Search approaches (HW-NAS) automate the design of deep learning architectures, tailored specifically to a given target hardware platform. Yet, these techniques demand substantial computational resources, primarily due to the expensive process of assessing the performance of identified architectures. To alleviate this problem, a recent direction in the literature has employed representation similarity metric for efficiently evaluating architecture performance. Nonetheless, since it is inherently a single objective method, it requires multiple runs to identify the optimal architecture set satisfying the diverse hardware cost constraints, thereby increasing the search cost. Furthermore, simply converting the single objective into a multi-objective approach results in an under-explored architectural search space. In this study, we propose a Multi-Objective method to address the HW-NAS problem, called MO-HDNAS, to identify the trade-off set of architectures in a single run with low computational cost. This is achieved by optimizing three objectives: maximizing the representation similarity metric, minimizing hardware cost, and maximizing the hardware cost diversity. The third objective, i.e. hardware cost diversity, is used to facilitate a better exploration of the architecture search space. Experimental results demonstrate the effectiveness of our proposed method in efficiently addressing the HW-NAS problem across six edge devices for the image classification task.

关键词： Performance evaluation Costs Image edge detection computer architecture Search problems Hardware Computational efficiency

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2T-UNET: A Two-Tower UNet with Depth Clues for Robust Stereo Depth Estimation

2T-UNET: A Two-Tower UNet with Depth Clues for Robust Stereo...

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

作者： Mansi Sharma Rohit Choudhary Rithvik Anil Thapar Institute of Engineering & Technology Patiala Punjab India Indian Institute of Technology Madras India

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Stereo correspondence matching is an essential part of the multi-step stereo depth estimation process. This paper revisits the depth estimation problem, avoiding the explicit stereo-matching step using a simple two-tower convolutional neural network. The proposed algorithm is entitled as 2T-UNet. The idea behind 2T-UNet is to replace cost volume construction with twin convolution towers. These towers have an allowance for different weights between them. Additionally, the input for twin encoders in 2T-UNet are different compared to the existing stereo methods. Generally, a stereo network takes a right and left image pair as input to determine the scene geometry. However, in the 2T-UNet model, the right stereo image is taken as one input and the left stereo image along with its monocular depth clue information, is taken as the other input. Depth clues provide complementary suggestions that help enhance the quality of predicted scene geometry. The 2T-UNet surpasses state-of-the-art monocular and stereo depth estimation methods on the challenging Scene flow dataset, both quantitatively and qualitatively. The architecture performs incredibly well on complex natural scenes, highlighting its usefulness for various real-time applications. Pretrained weights and code will be made readily available.

关键词： Geometry Solid modeling Costs Three-dimensional displays Poles and towers Estimation computer architecture

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Unsupervised Multi-Person 3D Human Pose Estimation From 2D Poses Alone

Unsupervised Multi-Person 3D Human Pose Estimation From 2D P...

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

作者： Peter Hardy Hansung Kim School of Electronics and Computer Science University of Southampton UK

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Current unsupervised 2D-3D human pose estimation (HPE) methods do not work in multi-person scenarios due to perspective ambiguity in monocular images. Therefore, we present one of the first studies investigating the feasibility of unsupervised multi-person 2D-3D HPE from just 2D poses alone, focusing on reconstructing human interactions. To address the issue of perspective ambiguity, we expand upon prior work by predicting the cameras’ elevation angle relative to the subjects’ pelvis. This allows us to rotate the predicted poses to be level with the ground plane, while obtaining an estimate for the vertical offset in 3D between individuals. Our method involves independently lifting each subject’s 2D pose to 3D, before combining them in a shared 3D coordinate system. The poses are then rotated and offset by the predicted elevation angle before being scaled. This by itself enables us to retrieve an accurate 3D reconstruction of their poses. We present our results on the CHI3D dataset, introducing its use for unsupervised 2D-3D pose estimation with three new quantitative metrics, and establishing a benchmark for future research.

关键词： Measurement Three-dimensional displays Accuracy Pose estimation Focusing Benchmark testing Real-time systems

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Calibrating Higher-Order Statistics for Few-Shot Class-Incremental Learning with Pre-trained vision Transformers

Calibrating Higher-Order Statistics for Few-Shot Class-Incre...

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

作者： Dipam Goswami Bartłomiej Twardowski Joost Van de Weijer Department of Computer Science Universitat Autònoma de Barcelona Computer Vision Center Barcelona IDEAS-NCBR

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Few-shot class-incremental learning (FSCIL) aims to adapt the model to new classes from very few data (5 samples) without forgetting the previously learned classes. Recent works in many-shot CIL (MSCIL) (using all available training data) exploited pre-trained models to reduce forgetting and achieve better plasticity. In a similar fashion, we use ViT models pre-trained on large-scale datasets for few-shot settings, which face the critical issue of low plasticity. FSCIL methods start with a many-shot first task to learn a very good feature extractor and then move to the few-shot setting from the second task onwards. While the focus of most recent studies is on how to learn the many-shot first task so that the model generalizes to all future few-shot tasks, we explore in this work how to better model the few-shot data using pre-trained models, irrespective of how the first task is trained. Inspired by recent works in MSCIL, we explore how using higher-order feature statistics can influence the classification of few-shot classes. We identify the main challenge of obtaining a good covariance matrix from few-shot data and propose to calibrate the covariance matrix for new classes based on semantic similarity to the many-shot base classes. Using the calibrated feature statistics in combination with existing methods significantly improves few-shot continual classification on several FSCIL benchmarks. Code is available at https://***/dipamgoswami/FSCIL-Calibration.

关键词： Adaptation models computer vision Training data Benchmark testing Feature extraction Transformers Data models

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CDAD-Net: Bridging Domain Gaps in Generalized Category Discovery

CDAD-Net: Bridging Domain Gaps in Generalized Category Disco...

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

作者： Sai Bhargav Rongali Sarthak Mehrotra Ankit Jha Mohamad Hassan N C Shirsha Bose Tanisha Gupta Mainak Singha Biplab Banerjee Indian Institute of Technology Bombay India INRIA Grenoble France Technical University of Munich Germany

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

In Generalized Category Discovery (GCD), we cluster unlabeled samples of known and novel classes, leveraging a training dataset of known classes. A salient challenge arises due to domain shifts between these datasets. To address this, we present a novel setting: Across Domain Generalized Category Discovery (AD-GCD) and bring forth CDAD-Net (Class Discoverer Across Domains) as a remedy. CDAD-Net is architected to synchronize potential known class samples across both the labeled (source) and unlabeled (target) datasets, while emphasizing the distinct categorization of the target data. To facilitate this, we propose an entropy-driven adversarial learning strategy that accounts for the distance distributions of target samples relative to source-domain class prototypes. Parallelly, the discriminative nature of the shared space is upheld through a fusion of three metric learning objectives. In the source domain, our focus is on refining the proximity between samples and their affiliated class prototypes, while in the target domain, we integrate a neighborhood-centric contrastive learning mechanism, enriched with an adept neighbors-mining approach. To further accentuate the nuanced feature interrelation among semantically aligned images, we champion the concept of conditional image inpainting, underscoring the premise that semantically analogous images prove more efficacious to the task than their disjointed counterparts. Experimentally, CDAD-Net eclipses existing literature with a performance increment of 8-15% on three AD-GCD benchmarks we present.

关键词： Training Measurement computer vision conferences Refining Prototypes Contrastive learning

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Tri-VAE: Triplet Variational Autoencoder for Unsupervised Anomaly Detection in Brain Tumor MRI

Tri-VAE: Triplet Variational Autoencoder for Unsupervised An...

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

作者： Hansen Wijanarko Evelyne Calista Li-Fen Chen Yong-Sheng Chen National Yang Ming Chiao Tung University Taiwan

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

The intricate manifestations of pathological brain lesions in imaging data pose challenges for supervised detection methods due to the scarcity of annotated samples. To overcome this difficulty, our focus shifts to unsupervised anomaly detection. In this work, we exclusively train the proposed model using healthy data to identify unseen anomalies during testing. This study entails investigating the triplet-based variational autoencoder to simultaneously learn the distribution of healthy brain data and denoising capabilities. Importantly, we rectify a misconception inherent in prior projection-based approaches which relies on the presumption that healthy regions within images would persist unaltered in the reconstructed output. This inadvertently implied a substantial likeness in latent space representations between lesion and lesion-free images. However, this assumption might not hold true, particularly due to the potential significant impact of lesion area intensities on the projection process notably for autoencoders with single information bottleneck. To overcome this limitation, we disentangled metric learning from latent sampling. This approach ensures that both lesion and lesion-free input images are projected into the same distribution, specifically the lesion-free projection. Moreover, we introduce a semantic-guided gated cross skip module to enhance spatial detail retrieval while suppressing anomalies, leveraging robust healthy brain representation semantics exist in the deeper levels of the decoder. We also discovered that incorporating structure similarity index measure as an extra training objective bolsters the capability of anomaly detection for the proposed model.

关键词： Training Measurement Pathology Semantics Noise reduction Brain modeling pattern recognition

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IrrNet: Advancing Irrigation Mapping with Incremental Patch Size Training on Remote Sensing Imagery

IrrNet: Advancing Irrigation Mapping with Incremental Patch ...

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

作者： Oishee Bintey Hoque Samarth Swarup Abhijin Adiga Sayjro Kossi Nouwakpo Madhav Marathe Department of Computer Science University of Virginia Charlottesville VA USA Biocomplexity Institute University of Virginia Charlottesville VA USA US Department of Agriculture Agricultural Research Service Kimberly ID USA

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Irrigation mapping plays a crucial role in effective water management, essential for preserving both water quality and quantity, and is key to mitigating the global issue of water scarcity. The complexity of agricultural fields, adorned with diverse irrigation practices, especially when multiple systems coexist in close quarters, poses a unique challenge. This complexity is further compounded by the nature of Landsat’s remote sensing data, where each pixel is rich with densely packed information, complicating the task of accurate irrigation mapping. In this study, we introduce an innovative approach that employs a progressive training method, which strategically increases patch sizes throughout the training process, utilizing datasets from Landsat 5 and 7, labeled with the WRLU dataset for precise labeling. This initial focus allows the model to capture detailed features, progressively shifting to broader, more general features as the patch size enlarges. Remarkably, our method enhances the performance of existing state-of-the-art models by approximately 20%. Furthermore, our analysis delves into the significance of incorporating various spectral bands into the model, assessing their impact on performance. The findings reveal that additional bands are instrumental in enabling the model to discern finer details more effectively. This work sets a new standard for leveraging remote sensing imagery in irrigation mapping.

关键词： Training Irrigation Analytical models Accuracy Water quality Robustness Complexity theory

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HMANet: Hybrid Multi-Axis Aggregation Network for Image Super-Resolution

HMANet: Hybrid Multi-Axis Aggregation Network for Image Supe...

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

作者： Shu-Chuan Chu Zhi-Chao Dou Jeng-Shyang Pan Shaowei Weng Junbao Li College of Computer Science and Engineering Shandong University of Science and Technology School of Artificial Intelligence Nanjing University of Information Science and Technology School of Information Engineering Guangdong University of Technology School of Electronic and Information Engineering Harbin Institute of Technology

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Transformer-based methods have demonstrated excellent performance on super-resolution visual tasks, surpassing conventional convolutional neural networks. However, existing work typically restricts self-attention computation to non-overlapping windows to save computational costs. This means that Transformer-based networks can only use input information from a limited spatial range. Therefore, a novel Hybrid Multi-Axis Aggregation network (HMA) is proposed in this paper to exploit feature potential information better. HMA is constructed by stacking Residual Hybrid Transformer Blocks(RHTB) and Grid Attention Blocks(GAB). On the one side, RHTB combines channel attention and self-attention to enhance nonlocal feature fusion and produce more attractive visual results. Conversely, GAB is used in cross-domain information interaction to jointly model similar features and obtain a larger perceptual field. For the super-resolution task in the training phase, a novel pre-training method is designed to enhance the model representation capabilities further and validate the proposed model’s effectiveness through many experiments. The experimental results show that HMA outperforms the state-of-the-art methods on the benchmark dataset. We provide code and models at https://***/korouuuuu/HMA.

关键词： Training Visualization Convolution Superresolution Stacking Benchmark testing Transformers

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