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StegaNeRV: Video Steganography using Implicit Neural Representation

StegaNeRV: Video Steganography using Implicit Neural Represe...

引用

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

作者： Monsij Biswal Tong Shao Kenneth Rose Peng Yin Sean McCarthy Department of Electrical and Computer Engineering University of California Santa Barbara Santa Barbara CA USA Dolby Laboratories Inc Sunnyvale CA USA

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Numerous studies have recently advanced the state-of-the art for representing videos through an implicit neural network (INR). As these models become increasingly ubiquitous, there is a growing demand for concealing data within INR reconstructed videos such as for storing content metadata and sensitive licensing information. In this paper, we explore a new space in video steganography, hiding a distinct image within each RGB frame output by an INR. We propose a joint training strategy of a U-Net based steganographic decoder with an INR model for video. Experimental results show that hidden images can be embedded and subsequently reconstructed with high fidelity while preserving the quality of the cover frames. Furthermore we demonstrate that by introducing an attention module which emphasizes hiding within the edges and rich texture patches in the cover frame, secret images can be reconstructed with superior quality and can also be concealed at greater resolutions.

关键词： Training Steganography Image resolution Neural networks Metadata Space exploration Decoding

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Emotic Masked Autoencoder on Dual-views with Attention Fusion for Facial Expression recognition

Emotic Masked Autoencoder on Dual-views with Attention Fusio...

引用

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

作者： Xuan-Bach Nguyen Hoang-Thien Nguyen Thanh-Huy Nguyen Nhu-Tai Do Quang Vinh Dinh University of Technology Ho Chi Minh City Vietnam Posts and Telecommunications Institute of Technology Ho Chi Minh City Vietnam University of Education Ho Chi Minh City Vietnam University of Economics Ho Chi Minh City-UEH Vietnam Vietnamese-German University Vietnam

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Facial Expression recognition (FER) is a critical task within computer vision with diverse applications across various domains. Addressing the challenge of limited FER datasets, which hampers the generalization capability of expression recognition models, is imperative for enhancing performance. Our paper presents an innovative approach integrating the MAE-Face self-supervised learning (SSL) method and multi-view Fusion Attention mechanism for expression classification, particularly showcased in the 6th Affective Behavior Analysis in-the-wild (ABAW) competition. By utilizing low-level feature information from the ipsilateral view (auxiliary view) before learning the high-level feature that emphasizes the shift in the human facial expression, our work seeks to provide a straightforward yet innovative way to improve the examined view (main view). We also suggest easy-to-implement and no-training frameworks aimed at highlighting key facial features to determine if such features can serve as guides for the model, focusing on pivotal local elements. The efficacy of this method is validated by improvements in model performance on the Aff-wild2 dataset, as observed in both training and validation contexts.

关键词： Training computer vision Attention mechanisms Computational modeling Face recognition Refining Focusing

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Low Latency Point Cloud Rendering with Learned Splatting

Low Latency Point Cloud Rendering with Learned Splatting

引用

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

作者： Yueyu Hu Ran Gong Qi Sun Yao Wang Tandon School of Engineering New York University Tsinghua University

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Point cloud is a critical 3D representation with many emerging applications. Because of the point sparsity and irregularity, high-quality rendering of point clouds is challenging and often requires complex computations to recover the continuous surface representation. On the other hand, to avoid visual discomfort, the motion-to-photon latency has to be very short, under 10 ms. Existing rendering solutions lack in either quality or speed. To tackle these challenges, we present a framework that unlocks interactive, free-viewing and high-fidelity point cloud rendering. We train a generic neural network to estimate 3D elliptical Gaussians from arbitrary point clouds and use differentiable surface splatting to render smooth texture and surface normal for arbitrary views. Our approach does not require per-scene optimization, and enable real-time rendering of dynamic point cloud. Experimental results demonstrate the proposed solution enjoys superior visual quality and speed, as well as generalizability to different scene content and robustness to compression artifacts.

关键词： Point cloud compression Visualization Three-dimensional displays Visual communication Neural networks Rendering (computer graphics) Robustness

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LLM-Seg: Bridging Image Segmentation and Large Language Model Reasoning

LLM-Seg: Bridging Image Segmentation and Large Language Mode...

引用

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

作者： Junchi Wang Lei Ke ETH Zurich

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Understanding human instructions to identify the target objects is vital for perception systems. In recent years, the advancements of Large Language Models (LLMs) have introduced new possibilities for image segmentation. In this work, we delve into reasoning segmentation, a novel task that enables segmentation system to reason and interpret implicit user intention via large language model reasoning and then segment the corresponding target. Our work on reasoning segmentation contributes on both the methodological design and dataset labeling. For the model, we propose a new framework named LLM-Seg. LLM-Seg effectively connects the current foundational Segmentation Anything Model and the LLM by mask proposals selection. For the dataset, we propose an automatic data generation pipeline and construct a new reasoning segmentation dataset named LLM-Seg40K. Experiments demonstrate that our LLM-Seg exhibits competitive performance compared with existing methods. Furthermore, our proposed pipeline can efficiently produce high-quality reasoning segmentation datasets. The LLM-Seg40K dataset, developed through this pipeline, serves as a new benchmark for training and evaluating various reasoning segmentation approaches. Our code, models and dataset are at https://***/wangjunchi/LLMSeg.

关键词： Training Image segmentation Large language models Design methodology Pipelines Cognition pattern recognition

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ConPro: Learning Severity Representation for Medical Images using Contrastive Learning and Preference Optimization

ConPro: Learning Severity Representation for Medical Images ...

引用

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

作者： Hong Nguyen Hoang Nguyen Melinda Chang Hieu Pham Shrikanth Narayanan Michael Pazzani University of Southern California Los Angeles United States Vinuni-Illinois Smart Health Center Hanoi Vietnam

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Understanding the severity of conditions shown in images in medical diagnosis is crucial, serving as a key guide for clinical assessment, treatment, as well as evaluating longitudinal progression. This paper proposes ConPrO: a novel representation learning method for severity assessment in medical images using Contrastive learning-integrated Preference Optimization. Different from conventional contrastive learning methods that maximize the distance between classes, ConPrO injects into the latent vector the distance preference knowledge between various severity classes and the normal class. We systematically examine the key components of our framework to illuminate how contrastive prediction tasks acquire valuable representations. We show that our representation learning framework offers valuable severity ordering in the feature space while outperforming previous state-of-the-art methods on classification tasks. We achieve a 6% and 20% relative improvement compared to a supervised and a self-supervised baseline, respectively. In addition, we derived discussions on severity indicators and related applications of preference comparison in the medical domain.

关键词： Representation learning Measurement Visualization Contrastive learning Prediction algorithms Vectors pattern recognition

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Sparse multi-view hand-object reconstruction for unseen environments

Sparse multi-view hand-object reconstruction for unseen envi...

引用

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

作者： Yik Lung Pang Changjae Oh Andrea Cavallaro Centre for Intelligent Sensing Queen Mary University of London Idiap Research Institute École Polytechnique Fédérale de Lausanne

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Recent works in hand-object reconstruction mainly focus on the single-view and dense multi-view settings. On the one hand, single-view methods can leverage learned shape priors to generalise to unseen objects but are prone to inaccuracies due to occlusions. On the other hand, dense multi-view methods are very accurate but cannot easily adapt to unseen objects without further data collection. In contrast, sparse multi-view methods can take advantage of the additional views to tackle occlusion, while keeping the computational cost low compared to dense multi-view methods. In this paper, we consider the problem of hand-object reconstruction with unseen objects in the sparse multi-view setting. Given multiple RGB images of the hand and object captured at the same time, our model SVHO combines the predictions from each view into a unified reconstruction without optimisation across views. We train our model on a synthetic hand-object dataset and evaluate directly on a real world recorded hand-object dataset with unseen objects. We show that while reconstruction of unseen hands and objects from RGB is challenging, additional views can help improve the reconstruction quality.

关键词： computer vision Shape Computational modeling conferences Predictive models Data collection pattern recognition

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Dformer: Learning Efficient Image Restoration with Perceptual Guidance

Dformer: Learning Efficient Image Restoration with Perceptua...

引用

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

作者： Nodirkhuja Khudjaev Roman Tsoy S M A Sharif Azamat Myrzabekov Seongwan Kim Jaeho Lee Opt-AI Inc. LG Sciencepark Seoul South Korea

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Image restoration tasks incorporate widespread real-world application. Apart from its significant practicability, generic deep image restoration methods still fail to handle complex tasks, like shadow removal, low-light enhancement, etc. This paper addresses the limitations of existing image restoration methods by introducing a novel deep architecture. The proposed model incorporates illumination mapping inspired by the Retinex theory within a double encoder-decoder network. Additionally, it utilizes a multi-head cross-attention mechanism to correlate input and reconstructed images to generate plausible and refined images. The proposed model employs a perceptual optimization strategy to tackle intricate restoration tasks effectively. It surpasses state-of-the-art methods in demanding tasks such as shadow removal, low-light image enhancement, and blind compress image enhancement, all while utilizing fewer trainable parameters. Our method is selected among the top solutions in the New Trends in Image Restoration and Enhancement’24 (NTIRE) challenge for shadow removal, securing a top position without resorting to score-boosting techniques such as ensembling.

关键词： Image coding Image edge detection Lighting Deep architecture Market research Image restoration pattern recognition

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FairSSD: Understanding Bias in Synthetic Speech Detectors

FairSSD: Understanding Bias in Synthetic Speech Detectors

引用

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

作者： Amit Kumar Singh Yadav Kratika Bhagtani Davide Salvi Paolo Bestagini Edward J. Delp Video and Image Processing Lab (VIPER) Purdue University West Lafayette Indiana USA Dipartimento di Elettronica Informazione e Bioingegneria Milano Italy

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Methods that can generate synthetic speech which is perceptually indistinguishable from speech recorded by a human speaker, are easily available. Several incidents report misuse of synthetic speech generated from these methods to commit fraud. To counter such misuse, many methods have been proposed to detect synthetic speech. Some of these detectors are more interpretable, can generalize to detect synthetic speech in the wild and are robust to noise. However, limited work has been done on understanding bias in these detectors. In this work, we examine bias in existing synthetic speech detectors to determine if they will unfairly target a particular gender, age and accent group. We also inspect whether these detectors will have a higher misclassification rate for bona fide speech from speech-impaired speakers w.r.t fluent speakers. Extensive experiments on 6 existing synthetic speech detectors using more than 0.9 million speech signals demonstrate that most detectors are gender, age and accent biased, and future work is needed to ensure fairness. To support future research, we release our evaluation dataset, models used in our study and source code at https://***/viper-purdue/fairssd.

关键词： computer vision Source coding conferences Noise Detectors pattern recognition Fraud

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Red-Teaming Segment Anything Model

Red-Teaming Segment Anything Model

引用

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

作者： Krzysztof Jankowski Bartlomiej Sobieski Mateusz Kwiatkowski Jakub Szulc Michał Janik Hubert Baniecki Przemysław Biecek University of Warsaw Warsaw University of Technology

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Foundation models have emerged as pivotal tools, tackling many complex tasks through pre-training on vast datasets and subsequent fine-tuning for specific applications. The Segment Anything Model is one of the first and most well-known foundation models for computer vision segmentation tasks. This work presents a multi-faceted red-teaming analysis that tests the Segment Anything Model against challenging tasks: (1) We analyze the impact of style transfer on segmentation masks, demonstrating that applying adverse weather conditions and raindrops to dashboard images of city roads significantly distorts generated masks. (2) We focus on assessing whether the model can be used for attacks on privacy, such as recognizing celebrities’ faces, and show that the model possesses some undesired knowledge in this task. (3) Finally, we check how robust the model is to adversarial attacks on segmentation masks under text prompts. We not only show the effectiveness of popular white-box attacks and resistance to black-box attacks but also introduce a novel approach - Focused Iterative Gradient Attack (FIGA) that combines white-box approaches to construct an efficient attack resulting in a smaller number of modified pixels. All of our testing methods and analyses indicate a need for enhanced safety measures in foundation models for image segmentation.

关键词： Image segmentation Analytical models Privacy computer vision Text recognition Computational modeling Urban areas

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Leveraging Large Language Models for Multimodal Search

Leveraging Large Language Models for Multimodal Search

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

作者： Oriol Barbany Michael Huang Xinliang Zhu Arnab Dhua CSIC-UPC Institut de Robòtica i Informàtica Industrial Visual Search & AR Amazon

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Multimodal search has become increasingly important in providing users with a natural and effective way to express their search intentions. Images offer fine-grained details of the desired products, while text allows for easily incorporating search modifications. However, some existing multimodal search systems are unreliable and fail to address simple queries. The problem becomes harder with the large variability of natural language text queries, which may contain ambiguous, implicit, and irrelevant information. Addressing these issues may require systems with enhanced matching capabilities, reasoning abilities, and context-aware query parsing and rewriting. This paper introduces a novel multimodal search model that achieves a new performance milestone on the Fashion200K dataset [19]. Additionally, we propose a novel search interface integrating Large Language Models (LLMs) to facilitate natural language interaction. This interface routes queries to search systems while conversationally engaging with users and considering previous searches. When coupled with our multimodal search model, it heralds a new era of shopping assistants capable of offering human-like interaction and enhancing the overall search experience.

关键词： computer vision Large language models conferences Natural languages Pipelines Image retrieval computer architecture

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