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DATID-3D: Diversity-Preserved Domain Adaptation Using Text-to-Image Diffusion for 3D Generative Model

DATID-3D: Diversity-Preserved Domain Adaptation Using Text-t...

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conference on computer vision and pattern recognition (CVPR)

作者： Gwanghyun Kim Se Young Chun Dept. of Electrical and Computer Engineering INMC & IPAI Seoul National University Republic of Korea

Recent 3D generative models have achieved remarkable performance in synthesizing high resolution photorealistic images with view consistency and detailed 3D shapes, but training them for diverse domains is challenging since it requires massive training images and their camera distribution information. Text-guided domain adaptation methods have shown impressive performance on converting the 2D generative model on one domain into the models on other domains with different styles by leveraging the CLIP (Contrastive Language-Image Pre-training), rather than collecting massive datasets for those domains. However, one drawback of them is that the sample diversity in the original generative model is not well-preserved in the domain-adapted generative models due to the deterministic nature of the CLIP text encoder. Text-guided domain adaptation will be even more challenging for 3D generative models not only because of catastrophic diversity loss, but also because of inferior text-image correspondence and poor image quality. Here we propose DATID-3D, a domain adaptation method tailored for 3D generative models using text-to-image diffusion models that can synthesize diverse images per text prompt without collecting additional images and camera information for the target domain. Unlike 3D extensions of prior text-guided domain adaptation methods, our novel pipeline was able to fine-tune the state-of-the-art 3D generator of the source domain to synthesize high resolution, multi-view consistent images in text-guided targeted domains without additional data, outperforming the existing text-guided domain adaptation methods in diversity and text-image correspondence. Furthermore, we propose and demonstrate diverse 3D image manipulations such as one-shot instance-selected adaptation and single-view manipulated 3D reconstruction to fully enjoy diversity in text.

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Transferring Unconditional to Conditional GANs with Hyper-Modulation

Transferring Unconditional to Conditional GANs with Hyper-Mo...

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

作者： Hé ctor Laria Yaxing Wang Joost van de Weijer Bogdan Raducanu Computer Vision Center Barcelona Spain Nankai University China

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487405

GANs have matured in recent years and are able to generate high-resolution, realistic images. However, the computational resources and the data required for the training of high-quality GANs are enormous, and the study of transfer learning of these models is therefore an urgent topic. Many of the available high-quality pretrained GANs are unconditional (like StyleGAN). For many applications, however, conditional GANs are preferable, because they provide more control over the generation process, despite often suffering more training difficulties. Therefore, in this paper, we focus on transferring from high-quality pretrained unconditional GANs to conditional GANs. This requires architectural adaptation of the pretrained GAN to perform the conditioning. To this end, we propose hyper-modulated generative networks that allow for shared and complementary supervision. To prevent the additional weights of the hypernetwork to overfit, with subsequent mode collapse on small target domains, we introduce a self-initialization procedure that does not require any real data to initialize the hypernetwork parameters. To further improve the sample efficiency of the transfer, we apply contrastive learning in the discriminator, which effectively works on very limited batch sizes. In extensive experiments, we validate the efficiency of the hypernetworks, self-initialization and contrastive loss for knowledge transfer on standard benchmarks. Our code is available at https://***/hecoding/Hyper-Modulation.

关键词： Training conferences Transfer learning Modulation Process control Generative adversarial networks Data models

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Efficient CNN Architecture for Multi-modal Aerial View Object Classification

Efficient CNN Architecture for Multi-modal Aerial View Objec...

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

作者： Casian Miron Alexandru Pasarica Radu Timofte MCC Resources S.R.L. "Gheorghe Asachi" Technical University Iasi Romania

The NTIRE 2021 workshop features a Multi-modal Aerial View Object Classification Challenge. Its focus is on multi-sensor imagery classification in order to improve the performance of automatic target recognition (ATR) systems. In this paper we describe our entry in this challenge, a method focused on efficiency and low computational time, while maintaining a high level of accuracy. The method is a convolutional neural network with 11 convolutions, 1 max pooling layers and 3 residual blocks which has a total of 373.130 parameters. The method ranks 3rd in the Track 2 (SAR+EO) of the challenge.

关键词： Training computer vision Target recognition conferences Systems architecture Graphics processing units computer architecture

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Efficient Multi-Purpose Cross-Attention Based Image Alignment Block for Edge Devices

Efficient Multi-Purpose Cross-Attention Based Image Alignmen...

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

作者： Bahri Batuhan Bilecen Alparslan Fiş ne Mustafa Ayazoğ lu Aselsan Research Ankara Turkey

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487405

Image alignment, also known as image registration, is a critical block used in many computer vision problems. One of the key factors in alignment is efficiency, as inefficient aligners can cause significant overhead to the overall problem. In the literature, there are some blocks that appear to do the alignment operation, although most do not focus on efficiency. Therefore, an image alignment block which can both work in time and/or space and can work on edge devices would be beneficial for almost all networks dealing with multiple images. Given its wide usage and importance, we propose an efficient, cross-attention-based, multipurpose image alignment block (XABA) suitable to work within edge devices. Using cross-attention, we exploit the relationships between features extracted from images. To make cross-attention feasible for real-time image alignment problems and handle large motions, we provide a pyramidal block based cross-attention scheme. This also captures local relationships besides reducing memory requirements and number of operations. Efficient XABA models achieve real-time requirements of running above 20 FPS performance on NVIDIA Jetson Xavier with 30W power consumption compared to other powerful computers. Used as a sub-block in a larger network, XABA also improves multi-image super-resolution network performance in comparison to other alignment methods.

关键词： Performance evaluation computer vision Power demand Image edge detection Superresolution Feature extraction Optical imaging

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Semantic Segmentation for Thermal Images: A Comparative Survey

Semantic Segmentation for Thermal Images: A Comparative Surv...

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

作者： Zü lfiye Kü tü k Gö rkem Algan Dept. of Image Proc. &#x0026 Computer Vis. Technologies Aselsan Inc. Turkey

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487405

Semantic segmentation is a challenging task since it requires excessively more low-level spatial information of the image compared to other computer vision problems. The accuracy of pixel-level classification can be affected by many factors, such as imaging limitations and the ambiguity of object boundaries in an image. Conventional methods exploit three-channel RGB images captured in the visible spectrum with deep neural networks (DNN). Thermal images can significantly contribute during the segmentation since thermal imaging cameras are capable of capturing details despite the weather and illumination conditions. Using infrared spectrum in semantic segmentation has many real-world use cases, such as autonomous driving, medical imaging, agriculture, defense industry, etc. Due to this wide range of use cases, designing accurate semantic segmentation algorithms with the help of infrared spectrum is an important challenge. One approach is to use both visible and infrared spectrum images as inputs. These methods can accomplish higher accuracy due to enriched input information, with the cost of extra effort for the alignment and processing of multiple inputs. Another approach is to use only thermal images, enabling less hardware cost for smaller use cases. Even though there are multiple surveys on semantic segmentation methods, the literature lacks a comprehensive survey centered explicitly around semantic segmentation using infrared spectrum. This work aims to fill this gap by presenting algorithms in the literature and categorizing them by their input images.

关键词： Image segmentation computer vision Costs Semantics Neural networks Lighting Robustness

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3D-POP - An Automated Annotation Approach to Facilitate Markerless 2D-3D Tracking of Freely Moving Birds with Marker-Based Motion Capture

3D-POP - An Automated Annotation Approach to Facilitate Mark...

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conference on computer vision and pattern recognition (CVPR)

作者： Hemal Naik Alex Hoi Hang Chan Junran Yang Mathilde Delacoux Iain D. Couzin Fumihiro Kano Máté Nagy Dept. of Collective Behavior and Dept. of Ecology of Animal Societies Max Planck Institute of Animal Behavior Dept. of Biology University of Konstanz Centre for the Advanced Study of Collective Behaviour University of Konstanz Computer Aided Medial Procedures Informatik Department Technische Universität München Dept. of Biological Physics Eötvös Loránd University MTA-ELTE 'Lendület’ Collective Behaviour Research Group Hungarian Academy of Sciences.

Recent advances in machine learning and computer vision are revolutionizing the field of animal behavior by enabling researchers to track the poses and locations of freely moving animals without any marker attachment. However, large datasets of annotated images of animals for markerless pose tracking, especially high-resolution images taken from multiple angles with accurate 3D annotations, are still scant. Here, we propose a method that uses a motion capture (mo-cap) system to obtain a large amount of annotated data on animal movement and posture (2D and 3D) in a semi-automatic manner. Our method is novel in that it extracts the 3D positions of morphological keypoints (e.g eyes, beak, tail) in reference to the positions of markers attached to the animals. Using this method, we obtained, and offer here, a new dataset - 3D-POP with approximately 300k annotated frames (4 million instances) in the form of videos having groups of one to ten freely moving birds from 4 different camera views in a 3.6m x 4.2m area. 3D-POP is the first dataset of flocking birds with accurate keypoint annotations in 2D and 3D along with bounding box and individual identities and will facilitate the development of solutions for problems of 2D to 3D markerless pose, trajectory tracking, and identification in birds.

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Learning Sparse Neural Networks Through Mixture-Distributed Regularization

Learning Sparse Neural Networks Through Mixture-Distributed ...

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

作者： Huang, Chang-Ti Chen, Jun-Cheng Wu, Ja-Ling Natl Taiwan Univ Taipei Taiwan Acad Sinica Taipei Taiwan

ISBN: (数字)9781728193601

ISBN: (纸本)9781728193601

L-0-norm regularization is one of the most efficient approaches to learn a sparse neural network. Due to its discrete nature, differentiable and approximate regularizations based on the concrete distribution [31] or its variants are proposed as alternatives;however, the concrete relaxation suffers from high-variance gradient estimates and is limited to its own concrete distribution. To address these issues, in this paper, we propose a more general framework for relaxing binary gates through mixture distributions. With the proposed method, any mixture pair of distributions converging to delta(0) and delta(1) can be applied to construct smoothed binary gates. We further introduce a reparameterization method for the smoothed binary gates drawn from mixture distributions to enable efficient gradient gradient-based optimization under the proposed deep learning algorithm. Extensive experiments are conducted, and the results show that the proposed approach achieves better performance in terms of pruned architectures, structured sparsity and the reduced number of floating point operations (FLOPs) as compared with other state-of-the-art sparsity-inducing methods.

关键词： Logic gates Training Neurons computer architecture Artificial neural networks Computational modeling Exponential distribution

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CFA: Constraint-based Finetuning Approach for Generalized Few-Shot Object Detection

CFA: Constraint-based Finetuning Approach for Generalized Fe...

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

作者： Karim Guirguis Ahmed Hendawy George Eskandar Mohamed Abdelsamad Matthias Kayser Jü rgen Beyerer Robert Bosch GmbH University of Stuttgart Karlsruhe Institute of Technology Fraunhofer IOSB

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487405

Few-shot object detection (FSOD) seeks to detect novel categories with limited data by leveraging prior knowledge from abundant base data. Generalized few-shot object detection (G-FSOD) aims to tackle FSOD without forgetting previously seen base classes and, thus, accounts for a more realistic scenario, where both classes are encountered during test time. While current FSOD methods suffer from catastrophic forgetting, G-FSOD addresses this limitation yet exhibits a performance drop on novel tasks compared to the state-of-the-art FSOD. In this work, we propose a constraint-based finetuning approach (CFA) to alleviate catastrophic forgetting, while achieving competitive results on the novel task without increasing the model capacity. CFA adapts a continual learning method, namely Average Gradient Episodic Memory (A-GEM) to G-FSOD. Specifically, more constraints on the gradient search strategy are imposed from which a new gradient update rule is derived, allowing for better knowledge exchange between base and novel classes. To evaluate our method, we conduct extensive experiments on MS-COCO and PASCAL-VOC datasets. Our method outperforms current FSOD and G-FSOD approaches on the novel task with minor degeneration on the base task. Moreover, CFA is orthogonal to FSOD approaches and operates as a plug-and-play module without increasing the model capacity or inference time.

关键词： Learning systems Adaptation models computer vision conferences Object detection Search problems pattern recognition

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Semantic Pose Verification for Outdoor Visual Localization with Self-supervised Contrastive Learning

Semantic Pose Verification for Outdoor Visual Localization w...

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

作者： Semih Orhan Jose J. Guerrero Yalı n Baş tanlar Department of Computer Engineering Izmir Institute of Technology Instituto de Investigaci&#x00F3 n en Ingenier&#x00ED a de Arag&#x00F3 n (I3A) Universidad de Zaragoza

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487405

Any city-scale visual localization system has to overcome long-term appearance changes, such as varying illumination conditions or seasonal changes between query and database images. Since semantic content is more robust to such changes, we exploit semantic information to improve visual localization. In our scenario, the database consists of gnomonic views generated from panoramic images (e.g. Google Street View) and query images are collected with a standard field-of-view camera at a different time. To improve localization, we check the semantic similarity between query and database images, which is not trivial since the position and viewpoint of the cameras do not exactly match. To learn similarity, we propose training a CNN in a self-supervised fashion with contrastive learning on a dataset of semantically segmented images. With experiments we showed that this semantic similarity estimation approach works better than measuring the similarity at pixel-level. Finally, we used the semantic similarity scores to verify the retrievals obtained by a state-of-the-art visual localization method and observed that contrastive learning-based pose verification increases top-1 recall value to 0.90 which corresponds to a 2% improvement.

关键词： Location awareness Training Visualization Image segmentation Semantics Feature extraction Cameras

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TRINS: Towards Multimodal Language Models that Can Read

TRINS: Towards Multimodal Language Models that Can Read

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conference on computer vision and pattern recognition (CVPR)

作者： Ruiyi Zhang Yanzhe Zhang Jian Chen Yufan Zhou Jiuxiang Gu Changyou Chen Tong Sun Adobe Research Georgia Institute of Technology State University of New York at Buffalo

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

Large multimodal language models have shown remarkable proficiency in understanding and editing images. However, a majority of these visually-tuned models struggle to comprehend the textual content embedded in images, primar-ily due to the limitation of training data. In this work, we introduce TRINS: a Text-Rich image 1 1 In this work, we use the phrase “text-rich images” to describe images with rich textual information, such as posters and book covers. INStruction dataset, with the objective of enhancing the reading ability of the multimodal large language model. TRINS is built upon LAION 2 2 Work done during Q3 2023. using hybrid data annotation strategies that include machine-assisted and human-assisted annotation process. It contains 39,153 text-rich images, captions, and 102,437 questions. Specifically, we show that the number of words per annotation in TRINS is significantly longer than that of related datasets, providing new challenges. Furthermore, we introduce a simple and effective architecture, called a Language-vision Reading Assistant (LaRA), which is good at understanding textual content within images. LaRA outperforms existing state-of-the-art multimodal large language models on the TRINS dataset as well as other classical benchmarks. Lastly, we conducted a comprehensive evaluation with TRINS on various text-rich image understanding and generation tasks, demonstrating its effectiveness.

关键词： Visualization computer vision Annotations Large language models Computational modeling Optical character recognition Training data

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