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BalaGAN: Cross-Modal Image Translation Between Imbalanced Domains

BalaGAN: Cross-Modal Image Translation Between Imbalanced Do...

引用

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

作者： Patashnik, Or Danon, Dov Zhang, Hao Cohen-Or, Daniel Tel Aviv Univ Tel Aviv Israel Simon Fraser Univ Burnaby BC Canada

ISBN: (纸本)9781665448994

State-of-the-art image translation methods tend to struggle in an imbalanced domain setting, where one image domain lacks richness and diversity. We introduce a new unsupervised translation network, BalaGAN, specifically designed to tackle the domain imbalance problem. We leverage the latent modalities of the richer domain to turn the image-to-image translation problem, between two imbalanced domains, into a multi-class translation problem, more resembling the style transfer setting. Specifically, we analyze the source domain and learn a decomposition of it into a set of latent modes or classes, without any supervision. This leaves us with a multitude of balanced crossdomain translation tasks, between all pairs of classes, including the target domain. During inference, the trained network takes as input a source image, as well as a reference style image from one of the modes as a condition, and produces an image which resembles the source on the pixel-wise level, but shares the same mode as the reference. We show that employing modalities within the dataset improves the quality of the translated images, and that BalaGAN outperforms strong baselines of both unconditioned and style-transfer-based image-to-image translation methods, in terms of image quality and diversity.

关键词： Training Image quality computer vision conferences pattern recognition Task analysis

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Compact and Effective Representations for Sketch-based Image Retrieval

Compact and Effective Representations for Sketch-based Image...

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

作者： Torres, Pablo Saavedra, Jose M. Univ Chile DCC Av Beauchef 851 Santiago Chile Impresee Inc 600 Calif St San Francisco CA USA

ISBN: (纸本)9781665448994

Sketch-based image retrieval (SBIR) has undergone an increasing interest in the community of computer vision bringing high impact in real applications. For instance, SBIR brings an increased benefit to eCommerce search engines because it allows users to formulate a query just by drawing what they need to buy. However, current methods showing high precision in retrieval work in a high dimensional space, which negatively affects aspects like memory consumption and time processing. Although some authors have also proposed compact representations, these drastically degrade the performance in a low dimension. Therefore in this work, we present different results of evaluating methods for producing compact embeddings in the context of sketch-based image retrieval. Our main interest is in strategies aiming to keep the local structure of the original space. The recent unsupervised local-topology preserving dimension reduction method UMAP fits our requirements and shows outstanding performance, improving even the precision achieved by SOTA methods. We evaluate six methods in two different datasets. We use Flickr15K and eCommerce datasets;the latter is another contribution of this work. We show that UMAP allows us to have feature vectors of 16 bytes improving precision by more than 35%.

关键词： Dimensionality reduction computer vision conferences Image retrieval Memory management Search engines pattern recognition

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Extremely Lightweight Quantization Robust Real-Time Single-Image Super Resolution for Mobile Devices

Extremely Lightweight Quantization Robust Real-Time Single-I...

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

作者： Ayazoglu, Mustafa Aselsan Res Ankara Turkey

ISBN: (纸本)9781665448994

Single-Image Super Resolution (SISR) is a classical computer vision problem and it has been studied for over decades. With the recent success of deep learning methods, recent work on SISR focuses solutions with deep learning methodologies and achieves state-of-the-art results. However most of the state-of-the-art SISR methods contain millions of parameters and layers, which limits their practical applications. In this paper, we propose a hardware (Synaptics Dolphin NPU) limitation aware, extremely lightweight quantization robust real-time super resolution network (XLSR). The proposed model's building block is inspired from root modules introduced in [15] for Image classification. We successfully applied root modules to SISR problem, further more to make the model uint8 quantization robust we used Clipped ReLU at the last layer of the network and achieved great balance between reconstruction quality and runtime. Furthermore, although the proposed network contains 30x fewer parameters than VDSR [16] its performance surpasses it on Div2K validation set. The network proved itself by winning Mobile AI 2021 Real-Time Single Image Super Resolution Challenge.

关键词： Performance evaluation Deep learning computer vision Quantization (signal) Image resolution Runtime Computational modeling

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ReMOVE: A Reference-free Metric for Object Erasure

ReMOVE: A Reference-free Metric for Object Erasure

引用

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

作者： Aditya Chandrasekar Goirik Chakrabarty Jai Bardhan Ramya Hebbalaguppe Prathosh AP TCS Research IISc Bangalore IIT Delhi

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

We introduce ReMOVE, a novel reference-free metric for assessing object erasure efficacy in diffusion-based image editing models post-generation. Unlike existing measures such as LPIPS and CLIPScore, ReMOVE addresses the challenge of evaluating inpainting without a reference image, common in practical scenarios. It effectively distinguishes between object removal and replacement. This is a key issue in diffusion models due to stochastic nature of image generation. Traditional metrics fail to align with the intuitive definition of inpainting, which aims for (1) seamless object removal within masked regions (2) while preserving the background continuity. ReMOVE not only correlates with state-of-the-art metrics and aligns with human perception but also captures the nuanced aspects of the inpainting process, providing a finer-grained evaluation of the generated outputs.

关键词： Measurement computer vision Image synthesis conferences Stochastic processes Diffusion models pattern recognition

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Coreset Selection for Object Detection

Coreset Selection for Object Detection

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

作者： Hojun Lee Suyoung Kim Junhoo Lee Jaeyoung Yoo Nojun Kwak Seoul National University NAVER WEBTOON AI

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Coreset selection is a method for selecting a small, representative subset of an entire dataset. It has been primarily researched in image classification, assuming there is only one object per image. However, coreset selection for object detection is more challenging as an image can contain multiple objects. As a result, much research has yet to be done on this topic. Therefore, we introduce a new approach, Coreset Selection for Object Detection (CSOD). CSOD generates imagewise and classwise representative feature vectors for multiple objects of the same class within each image. Subsequently, we adopt submodular optimization for considering both representativeness and diversity and utilize the representative vectors in the submodular optimization process to select a subset. When we evaluated CSOD on the Pascal VOC dataset, CSOD outperformed random selection by +6.4%p in AP 50 when selecting 200 images.

关键词： computer vision conferences Object detection Vectors pattern recognition Optimization Image classification

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Connecting NeRFs, Images, and Text

Connecting NeRFs, Images, and Text

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

作者： Francesco Ballerini Pierluigi Zama Ramirez Roberto Mirabella Samuele Salti Luigi Di Stefano University of Bologna

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Neural Radiance Fields (NeRFs) have emerged as a standard framework for representing 3D scenes and objects, introducing a novel data type for information exchange and storage. Concurrently, significant progress has been made in multimodal representation learning for text and image data. This paper explores a novel research direction that aims to connect the NeRF modality with other modalities, similar to established methodologies for images and text. To this end, we propose a simple framework that exploits pre-trained models for NeRF representations alongside multimodal models for text and image processing. Our framework learns a bidirectional mapping between NeRF embeddings and those obtained from corresponding images and text. This mapping unlocks several novel and useful applications, including NeRF zero-shot classification and NeRF retrieval from images or text.

关键词： Representation learning computer vision Three-dimensional displays Image processing conferences Neural radiance field pattern recognition

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Towards computer vision and Deep Learning Facilitated Pollination Monitoring for Agriculture

Towards Computer Vision and Deep Learning Facilitated Pollin...

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

作者： Ratnayake, Malika Nisal Dyer, Adrian G. Dorin, Alan Monash Univ Fac Informat Technol Clayton Vic Australia RMIT Univ Sch Media & Commun Melbourne Vic Australia Monash Univ Dept Physiol Clayton Vic Australia

ISBN: (纸本)9781665448994

Globally, pollinators affect 35% of agricultural land and play a key role in food production. Consequently, monitoring is useful to understand the contribution insects make towards crop pollination. Traditional sampling techniques used in insect monitoring have several drawbacks, including that they are labour intensive and potentially unreliable. Some of these drawbacks may be overcome using computer vision and deep learning-based approaches to automate pollination monitoring. In this paper, we present a pipeline for computer vision-based pollination monitoring and propose a novel algorithm, Polytrack, that tracks multiple insects simultaneously in complex agricultural environments. Our algorithm uses deep learning and foreground/background segmentation to detect and track insects. We achieved precision and recall rates of 0.975 and 0.972 respectively when monitoring honeybees foraging in our test sites within the polytunnels of an industrial strawberry farm. Polytrack includes a flower identification module to automate collection of insect-flower interaction data, and a low-resolution processing mode that reduces computational demands placed on the processor to bring the software towards the requirements of low-powered monitoring hardware.

关键词： Deep learning computer vision Visualization Insects Pipelines Production Agriculture

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VideoSAGE: Video Summarization with Graph Representation Learning

VideoSAGE: Video Summarization with Graph Representation Lea...

引用

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

作者： Jose M. Rojas Chaves Subarna Tripathi Intel Corporation Intel Labs

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

We propose a graph-based representation learning framework for video summarization. First, we convert an input video to a graph where nodes correspond to each of the video frames. Then, we impose sparsity on the graph by connecting only those pairs of nodes that are within a specified temporal distance. We then formulate the video summarization task as a binary node classification problem, precisely classifying video frames whether they should belong to the output summary video. A graph constructed this way aims to capture long-range interactions among video frames, and the sparsity ensures the model trains without hitting the memory and compute bottleneck. Experiments on two datasets(SumMe and TVSum) demonstrate the effectiveness of the proposed nimble model compared to existing state-of-the-art summarization approaches while being one order of magnitude more efficient in compute time and memory.

关键词： Representation learning computer vision Computational modeling conferences Memory management pattern recognition

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From Evaluation to Verification: Towards Task-oriented Relevance Metrics for Pedestrian Detection in Safety-critical Domains

From Evaluation to Verification: Towards Task-oriented Relev...

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

作者： Lyssenko, Maria Gladisch, Christoph Heinzemann, Christian Woehrle, Matthias Triebel, Rudolph Robert Bosch GmbH Corp Res Robert Bosch Campus 1 D-71272 Renningen Germany Tech Univ Munich Boltzmannstr 3 D-85748 Garching Germany German Aerosp Ctr DLR Munchener Str 20 D-82234 Wessling Germany

ISBN: (纸本)9781665448994

Whenever a visual perception system is employed in safety-critical applications such as automated driving, a thorough, task-oriented experimental evaluation is necessary to guarantee safe system behavior. While most standard evaluation methods in computer vision provide a good comparability on benchmarks, they tend to fall short on assessing the system performance that is actually relevant for the given task. In our work, we consider pedestrian detection as a highly relevant perception task, and we argue that standard measures such as Intersection over Union (IoU) give insufficient results, mainly because they are insensitive to important physical cues including distance, speed, and direction of motion. Therefore, we investigate so-called relevance metrics, where specific domain knowledge is exploited to obtain a task-oriented performance measure focusing on distance in this initial work. Our experimental setup is based on the CARLA simulator and allows a controlled evaluation of the impact of that domain knowledge. Our first results indicate a linear decrease of the IoU related to the pedestrians' distance, leading to the proposal of a first relevance metric that is also conditioned on the distance.

关键词： Measurement Meters computer vision System performance pattern recognition Proposals Motion measurement

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Roughness Analysis of Stainless Steel Bearing Using computer vision 6

Roughness Analysis of Stainless Steel Bearing Using Computer...

引用

6th International conference on Electrical, Control and Instrumentation Engineering, ICECIE 2024

作者： Das, Shounak Tarafdar, Arundhati Mondal, Prabir Computer Science and Engineering Jadavpur University Kolkata India School of Computer Science Data Science cluster UPES University Dehradun India Computer Science and Engineering National Institute of Technology Patna India

ISBN: (纸本)9798350380040

Current technology utilizes a sophisticated standard room measurement system to analyze the average roughness of Stainless Steel Bearing Surface. However, this process relies on random sampling at infrequent intervals, resulting in inefficiency and inadequate analysis of a large number of samples. To address this issue, we propose a solution using computer vision via laser scanning. The scanning process produces an amplified speckle pattern of the ring surface. This pattern is then analyzed to verify the number of edges that cross a predefined threshold and exhibit symmetrical patterns, particularly on smoothed ring surfaces. Remarkably, this system operates with a turnaround time of less than a second. Moreover, unlike the standard room setup, this technology can be seamlessly integrated directly into production/manufacturing units. © 2024 ieee.

关键词： Speckle

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