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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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Weakly Supervised Set-Consistency Learning Improves Morphological Profiling of Single-Cell Images

Weakly Supervised Set-Consistency Learning Improves Morpholo...

引用

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

作者： Heming Yao Phil Hanslovsky Jan-Christian Huetter Burkhard Hoeckendorf David Richmond Biology Research | AI Development (BRAID) gCS Genentech

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Optical Pooled Screening (OPS) is a powerful tool combining high-content microscopy with genetic engineering to investigate gene function in disease. The characterization of high-content images remains an active area of research and is currently undergoing rapid innovation through the application of self-supervised learning and vision transformers. In this study, we propose a set-level consistency learning algorithm, Set-DINO, that combines self-supervised learning with weak supervision to improve learned representations of perturbation effects in single-cell images. Our method leverages the replicate structure of OPS experiments (i.e., cells undergoing the same genetic perturbation, both within and across batches) as a form of weak supervision. We conduct extensive experiments on a large-scale OPS dataset with more than 5000 genetic perturbations, and demonstrate that Set-DINO helps mitigate the impact of confounders and encodes more biologically meaningful information. In particular, Set-DINO recalls known biological relationships with higher accuracy compared to commonly used methods for morphological profiling, suggesting that it can generate more reliable insights from drug target discovery campaigns leveraging OPS.

关键词： computer vision Technological innovation Biomedical optical imaging Perturbation methods Self-supervised learning Optical imaging Genetics

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Edge Guided Progressively Generative Image Outpainting

Edge Guided Progressively Generative Image Outpainting

引用

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

作者： Lin, Han Pagnucco, Maurice Song, Yang Univ New South Wales Sch Comp Sci & Engn Sydney NSW Australia

ISBN: (纸本)9781665448994

Deep-learning based generative models are proven to be capable for achieving excellent results in numerous image processing tasks with a wide range of applications. One significant improvement of deep-learning approaches compared to traditional approaches is their ability to regenerate semantically coherent images by only relying on an input with limited information. This advantage becomes even more crucial when the input size is only a very minor proportion of the output size. Such image expansion tasks can be more challenging as the missing area may originally contain many semantic features that are critical in judging the quality of an image. In this paper we propose an edge-guided generative network model for producing semantically consistent output from a small image input. Our experiments show the proposed network is able to regenerate high quality images even when some structural features are missing in the input.

关键词： Measurement computer vision Image edge detection Computational modeling Image processing conferences Semantics

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Classifier Guided Cluster Density Reduction for Dataset Selection

Classifier Guided Cluster Density Reduction for Dataset Sele...

引用

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

作者： Cheng Chang Keyu Long Zijian Li Himanshu Rai Layer 6 AI

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

In this paper, we address the challenge of selecting an optimal dataset from a source pool with annotations to enhance performance on a target dataset derived from a different source. This is important in scenarios where it is hard to afford on-the-fly dataset annotation and is also the theme of the second Visual Data Understanding (VDU) Challenge. Our solution, the Classifier Guided Cluster Density Reduction (CCDR) framework, operates in two stages. Initially, we employ a filtering technique to identify images that align with the target dataset’s distribution. Subsequently, we implement a graph-based cluster density reduction method, steered by a classifier that approximates the distance between the target distribution and source distribution. This classifier is trained to distinguish between images that resemble the target dataset and those that do not, facilitating the pruning process shown in Figure 1. Our approach maintains a balance between selecting pertinent images that match the target distribution and eliminating redundant ones that do not contribute to the enhancement of the detection model. We demonstrate the superiority of our method over various baselines in object detection tasks, particularly in optimizing the training set distribution on the region100 dataset. We have released our code here: https://***/himsR/DataCVChallenge-2024/tree/main

关键词： Training Visualization computer vision Codes Annotations Filtering conferences

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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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Blind Localization and Clustering of Anomalies in Textures

Blind Localization and Clustering of Anomalies in Textures

引用

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

作者： Andrei-Timotei Ardelean Tim Weyrich Friedrich-Alexander-Universität Erlangen-Nürnberg

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Anomaly detection and localization in images is a growing field in computer vision. In this area, a seemingly understudied problem is anomaly clustering, i.e., identifying and grouping different types of anomalies in a fully unsupervised manner. In this work, we propose a novel method for clustering anomalies in largely stationary images (textures) in a blind setting. That is, the input consists of normal and anomalous images without distinction and without labels. What contributes to the difficulty of the task is that anomalous regions are often small and may present only subtle changes in appearance, which can be easily overshadowed by the genuine variance in the texture. Moreover, each anomaly type may have a complex appearance distribution. We introduce a novel scheme for solving this task using a combination of blind anomaly localization and contrastive learning. By identifying the anomalous regions with high fidelity, we can restrict our focus to those regions of interest; then, contrastive learning is employed to increase the separability of different anomaly types and reduce the intra-class variation. Our experiments show that the proposed solution yields significantly better results compared to prior work, setting a new state of the art. Project page: ***/pub/***.

关键词： Location awareness computer vision Image texture Art conferences Pipelines Contrastive learning

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MMA-DFER: MultiModal Adaptation of unimodal models for Dynamic Facial Expression recognition in-the-wild

MMA-DFER: MultiModal Adaptation of unimodal models for Dynam...

引用

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

作者： Kateryna Chumachenko Alexandros Iosifidis Moncef Gabbouj Tampere University Tampere Finland Aarhus University Aarhus Denmark

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Dynamic Facial Expression recognition (DFER) has received significant interest in the recent years dictated by its pivotal role in enabling empathic and human-compatible technologies. Achieving robustness towards in-the-wild data in DFER is particularly important for real-world applications. One of the directions aimed at improving such models is multimodal emotion recognition based on audio and video data. Multimodal learning in DFER increases the model capabilities by leveraging richer, complementary data representations. Within the field of multimodal DFER, recent methods have focused on exploiting advances of self-supervised learning (SSL) for pre-training of strong multi-modal encoders [40]. Another line of research has focused on adapting pre-trained static models for DFER [8]. In this work, we propose a different perspective on the problem and investigate the advancement of multimodal DFER performance by adapting SSL-pre-trained disjoint unimodal encoders. We identify main challenges associated with this task, namely, intra-modality adaptation, cross-modal alignment, and temporal adaptation, and propose solutions to each of them. As a result, we demonstrate improvement over current state-of-the-art on two popular DFER benchmarks, namely DFEW [19] and MFAW [29].

关键词： Adaptation models Emotion recognition computer vision Face recognition conferences Computational modeling Self-supervised learning

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VMCML: Video and Music Matching via Cross-Modality Lifting

VMCML: Video and Music Matching via Cross-Modality Lifting

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

作者： Yi-Shan Lee Wei-Cheng Tseng Fu-En Wang Min Sun National Tsing Hua University University of Toronto Vector Institute

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

We propose a content-based system for matching video and background music. The system aims to address the challenges in music recommendation for new users or new music give short-form videos. To this end, we propose a cross-modal framework VMCML (Video and Music Matching via Cross-Modality Lifting) that finds a shared embedding space between video and music representations. To ensure the embedding space can be effectively shared by both representations, we leverage CosFace loss based on margin-based cosine similarity loss. Furthermore, to confirm the music is not the original sound of the video and that more than one video is matched to the same music, we follow the rule and collect videos and music from a well-known multi-media platform. That is because there are limitations of previous datasets. We establish a large-scale dataset called MSV, which provide 390 individual music and the corresponding matched 150,000 videos. We conduct extensive experiments on Youtube-8M and our MSV datasets. Our quantitative and qualitative results demonstrate the effectiveness of our proposed framework and achieve state-of-the-art video and music matching performance.

关键词： computer vision conferences Music Recommender systems pattern matching

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Multi-modal Arousal and Valence Estimation under Noisy Conditions

Multi-modal Arousal and Valence Estimation under Noisy Condi...

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

作者： Denis Dresvyanskiy Maxim Markitantov Jiawei Yu Heysem Kaya Alexey Karpov Ulm University Germany ITMO University Russia St. Petersburg Federal Research Center of the Russian Academy of Sciences St. Petersburg Russia Department of Information and Computing Sciences Utrecht University The Netherlands

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Automatic emotion recognition has gained significant attention over the past two decades due to the central role that emotions play in human communication. While multi-modal systems demonstrate high performances on laboratory-controlled data, their validity on non-lab-controlled, namely ‘in-the-wild’ data, remains a challenge. This work investigates audio-visual deep learning approaches for emotion recognition in-the-wild, with a particular focus on the effectiveness of architectures based on fine-tuned Convolutional Neural Networks (CNN) and Public Dimensional Emotion Model (PDEM) for video and audio modality, respectively. We explore and compare various temporal modeling techniques (e.g., transformer architectures) and fusion strategies by leveraging the embeddings from developed multi-stage trained modality-specific Deep Neural Networks (DNN). The results are reported on the Af-fWild2 dataset following the Affective Behavior Analysis in-the-Wild 2024 (ABAW’24) challenge protocol. Our investigation highlights the complexities of robust multi-modal emotion recognition in an unconstrained environment, providing insights into the usage of various deep learning architectures for tackling this challenging task.

关键词： Deep learning Emotion recognition Protocols conferences Estimation computer architecture Transformers

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PUDD: Towards Robust Multi-modal Prototype-based Deepfake Detection

PUDD: Towards Robust Multi-modal Prototype-based Deepfake De...

引用

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

作者： Alvaro Lopez Pellcier Yi Li Plamen Angelov School of Computing and Communications Lancaster University

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Deepfake techniques generate highly realistic data, making it challenging for humans to discern between actual and artificially generated images. Recent advancements in deep learning-based deepfake detection methods, particularly with diffusion models, have shown remarkable progress. However, there is a growing demand for real-world applications to detect unseen individuals, deepfake techniques, and scenarios. To address this limitation, we propose a Prototype-based Unified Framework for Deepfake Detection (PUDD). PUDD offers a detection system based on similarity, comparing input data against known prototypes for video classification and identifying potential deepfakes or previously unseen classes by analyzing drops in similarity. Our extensive experiments reveal three key findings: (1) PUDD achieves an accuracy of 95.1% on Celeb-DF, outperforming state-of-the-art deepfake detection methods; (2) PUDD leverages image classification as the upstream task during training, demonstrating promising performance in both image classification and deepfake detection tasks during inference; (3) PUDD requires only 2.7 seconds for retraining on new data and emits 10 5 times less carbon compared to the state-of-the-art model, making it significantly more environmentally friendly.

关键词： Training Deepfakes computer vision conferences Computational modeling Prototypes Diffusion models

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