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Color Me Good: Branding in the Coloring Style of Movie Posters

Color Me Good: Branding in the Coloring Style of Movie Poste...

引用

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

作者： Agrawal, Rishabh Sivaprasad, Sarath Pedanekar, Niranjan TCS Res Tata Consultancy Serv Pune 411013 Maharashtra India

ISBN: (纸本)9781665448994

Brand logos are often rendered in a different style based on a context such as an event promotion. For example, Warner Bros. uses a different variety of their brand logo for different movies for promotion and aesthetic appeal. In this paper, we propose an automated method to render brand logos in the coloring style of branding material such as movie posters. For this, we adopt a photo-realistic neural style transfer method using movie posters as the style source. We propose a color-based image segmentation and matching method to assign style segments to logo segments. Using these, we render the well-known Warner Bros. logo in the coloring style of 141 movie posters. We also present survey results where 287 participants rate the machine-stylized logos for their representativeness and visual appeal.

关键词： Image segmentation Visualization computer vision Image color analysis conferences Semantics Brightness

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Finding Facial Forgery Artifacts with Parts-Based Detectors

Finding Facial Forgery Artifacts with Parts-Based Detectors

引用

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

作者： Schwarcz, Steven Chellappa, Rama Univ Maryland College Pk MD 20742 USA Johns Hopkins Univ Baltimore MD USA

ISBN: (纸本)9781665448994

Manipulated videos, especially those where the identity of an individual has been modified using deep neural networks, are becoming an increasingly relevant threat in the modern day. In this paper, we seek to develop a generalizable, explainable solution to detecting these manipulated videos. To achieve this, we design a series of forgery detection systems that each focus on one individual part of the face. These parts-based detection systems, which can be combined and used together in a single architecture, meet all of our desired criteria - they generalize effectively between datasets and give us valuable insights into what the network is looking at when making its decision. We thus use these detectors to perform detailed empirical analysis on the FaceForensics++, Celeb-DF, and Facebook Deep-fake Detection Challenge datasets, examining not just what the detectors find but also collecting and analyzing useful related statistics on the datasets themselves.

关键词： Deep learning computer vision Social networking (online) Face recognition conferences Neural networks Detectors

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Learning to Detect Phone-related Pedestrian Distracted Behaviors with Synthetic Data

Learning to Detect Phone-related Pedestrian Distracted Behav...

引用

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

作者： Hatay, Emre Ma, Jin Sun, Huiming Fang, Jianwu Gao, Zhiqiang Yu, Hongkai Cleveland State Univ Cleveland OH 44115 USA Changan Univ Xian Peoples R China

ISBN: (纸本)9781665448994

Due to the popularity and mobility of smart phones, phone-related pedestrian distracted behaviors, e.g., Texting, Game Playing, and Phone calls, have caused many traffic fatalities and accidents. As an advanced driver-assistance or autonomous-driving system, computer vision could be used to automatically detect distractions from cameras installed on the vehicle for useful safety intervention. The state-of-the-art method models this problem as a standard supervised learning method with a two-branch Convolutional Neural Network (CNN) followed by a voting on all image frames. In contrast, this paper proposes a new synthetic dataset named SYN-PPDB (448 synchronized video pairs of 53,760 computer game images) for this research problem and models it as a transfer learning problem from synthetic data to real data. A new deep learning model embedded with spatial-temporal feature learning and pose-aware transfer learning is proposed. Experimental results show that we could improve the state-of-the-art overall recognition accuracy from 84.27% to 96.67%.

关键词： computer vision Computational modeling Transfer learning Supervised learning Games pattern recognition Synchronization

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Cross-Domain Multi-task Learning for Object Detection and Saliency Estimation

Cross-Domain Multi-task Learning for Object Detection and Sa...

引用

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

作者： Khattar, Apoorv Hegde, Srinidhi Hebbalaguppe, Ramya TCS Res Mumbai Maharashtra India

ISBN: (纸本)9781665448994

Multi-task learning (MTL) is a learning paradigm that aims at joint optimization of multiple tasks using a single neural network for better performance and generalization. In practice, MTL rests on the inherent assumption of availability of common datasets with ground truth labels for each of the downstream tasks. However, collecting such a common annotated dataset is laborious for complex computer vision tasks such as the saliency estimation which would require the eye fixation points as the ground truth data. To this end, we propose a novel MTL framework in the absence of common annotated dataset for joint estimation of important downstream tasks in computer vision - object detection and saliency estimation. Unlike many state-of-the-art methods, that rely on common annotated datasets for training, we consider the annotations from different datasets for jointly training different tasks, calling this setting as cross-domain MTL. We adapt MUTAN [3] framework to fuse features from different datasets to learn domain invariant features capturing the relatedness of different tasks. We demonstrate the improvement in the performance and generalizability of our MTL architecture. We also show that the proposed MTL network offers a 13% reduction in memory footprint due to parameter sharing between the related tasks.

关键词： Training computer vision Neural networks Estimation Object detection computer architecture pattern recognition

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Generalized Foggy-Scene Semantic Segmentation by Frequency Decoupling

Generalized Foggy-Scene Semantic Segmentation by Frequency D...

引用

IEEE computer Society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Qi Bi Shaodi You Theo Gevers Computer Vision Research Group University of Amsterdam Amsterdam The Netherlands

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Foggy-scene semantic segmentation (FSSS) is highly challenging due to the diverse effects of fog on scene properties and the limited training data. Existing research has mainly focused on domain adaptation for FSSS, which has practical limitations when dealing with new scenes. In our paper, we introduce domain-generalized FSSS, which can work effectively on unknown distributions without extensive training. To address domain gaps, we propose a frequency decoupling (FreD) approach that separates fog-related effects (amplitude) from scene semantics (phase) in feature representations. Our method is compatible with both CNN and vision Transformer backbones and outperforms existing approaches in various scenarios.

关键词： Training computer vision Frequency-domain analysis Semantic segmentation conferences Semantics Training data

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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

引用

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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Generative Zero-shot Network Quantization

Generative Zero-shot Network Quantization

引用

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

作者： He, Xiangyu Lu, Jiahao Xu, Weixiang Hu, Qinghao Wang, Peisong Cheng, Jian Chinese Acad Sci Inst Automat Beijing Peoples R China

ISBN: (纸本)9781665448994

Convolutional neural networks are able to learn realistic image priors from numerous training samples in low-level image generation and restoration [66]. We show that, for high-level image recognition tasks, we can further reconstruct "realistic" images of each category by leveraging intrinsic Batch Normalization (BN) statistics without any training data. Inspired by the popular VAE/GAN methods, we regard the zero-shot optimization process of synthetic images as generative modeling to match the distribution of BN statistics. The generated images serve as a calibration set for the following zero-shot network quantizations. Our method meets the needs for quantizing models based on sensitive information, e.g., due to privacy concerns, no data is available. Extensive experiments on benchmark datasets show that, with the help of generated data, our approach consistently outperforms existing data-free quantization methods.

关键词： Training Quantization (signal) Image resolution Image synthesis Training data pattern recognition Image restoration

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Connecting NeRFs, Images, and Text

Connecting NeRFs, Images, and Text

引用

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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An Adversarial Approach for Explaining the Predictions of Deep Neural Networks

An Adversarial Approach for Explaining the Predictions of De...

引用

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

作者： Rahnama, Arash Tseng, Andrew Modzy Vienna VA 22182 USA

ISBN: (纸本)9781665448994

Machine learning models have been successfully applied to a wide range of applications including computer vision, natural language processing, and speech recognition. A successful implementation of these models however, usually relies on deep neural networks (DNNs) which are treated as opaque black-box systems due to their incomprehensible complexity and intricate internal mechanism. In this work, we present a novel algorithm for explaining the predictions of a DNN using adversarial machine learning. Our approach identifies the relative importance of input features in relation to the predictions based on the behavior of an adversarial attack on the DNN. Our algorithm has the advantage of being fast, consistent, and easy to implement and interpret. We present our detailed analysis that demonstrates how the behavior of an adversarial attack, given a DNN and a task, stays consistent for any input test data point proving the generality of our approach. Our analysis enables us to produce consistent and efficient explanations. We illustrate the effectiveness of our approach by conducting experiments using a variety of DNNs, tasks, and datasets. Finally, we compare our work with other well-known techniques in the current literature.

关键词： Deep learning computer vision Machine learning algorithms Computational modeling conferences Speech recognition Prediction algorithms

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