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Large-capacity Image Steganography Based on Invertible Neural Networks

Large-capacity Image Steganography Based on Invertible Neura...

引用

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

作者： Lu, Shao-Ping Wang, Rong Zhong, Tao Rosin, Paul L. Nankai Univ CS TKLNDST Tianjin Peoples R China Cardiff Univ Sch Comp Sci & Informat Cardiff Wales

ISBN: (纸本)9781665445092

Many attempts have been made to hide information in images, where one main challenge is how to increase the payload capacity without the container image being detected as containing a message. In this paper, we propose a large-capacity Invertible Steganography Network (ISN) for image steganography. We take steganography and the recovery of hidden images as a pair of inverse problems on image domain transformation, and then introduce the forward and backward propagation operations of a single invertible network to leverage the image embedding and extracting problems. Sharing all parameters of our single ISN architecture enables us to efficiently generate both the container image and the revealed hidden image(s) with high quality. Moreover, in our architecture the capacity of image steganography is significantly improved by naturally increasing the number of channels of the hidden image branch. Comprehensive experiments demonstrate that with this significant improvement of the steganography payload capacity, our ISN achieves state-of-the-art in both visual and quantitative comparisons.

关键词： Backpropagation Steganography Visualization computer vision Inverse problems Neural networks computer architecture

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Towards Bridging Event Captioner and Sentence Localizer for Weakly Supervised Dense Event Captioning

Towards Bridging Event Captioner and Sentence Localizer for ...

引用

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

作者： Chen, Shaoxiang Jiang, Yu-Gang Fudan Univ Sch Comp Sci Shanghai Key Lab Intelligent Informat Proc Shanghai Peoples R China

ISBN: (纸本)9781665445092

Dense Event Captioning (DEC) aims to jointly localize and describe multiple events of interest in untrimmed videos, which is an advancement of the conventional video captioning task (generating a single sentence description for a trimmed video). Weakly Supervised Dense Event Captioning (WS-DEC) goes one step further by not relying on human-annotated temporal event boundaries. However, there are few methods trying to tackle this task, and how to connect localization and description remains an open problem. In this paper, we demonstrate that under weak supervision, the event captioning module and localization module should be more closely bridged in order to improve description performance. Different from previous approaches, in our method, the event captioner generates a sentence from a video segment and feeds it to the sentence localizer to reconstruct the segment, and the localizer produces word importance weights as a guidance for the captioner to improve event description. To further bridge the sentence localizer and event captioner, a concept learner is adopted as the basis of the sentence localizer, which can be utilized to construct an induced set of concept features to enhance video features and improve the event captioner. Finally, our proposed method outperforms state-of-the-art WS-DEC methods on the ActivityNet Captions dataset.

关键词： Location awareness Bridges computer vision Training data pattern recognition Feeds Task analysis

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Single Image Depth Prediction with Wavelet Decomposition

Single Image Depth Prediction with Wavelet Decomposition

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

作者： Ramamonjisoa, Michael Firman, Michael Watson, Jamie Lepetit, Vincent Turmukhambetov, Daniyar Univ Gustave Eiffel CNRS Ecole Ponts IMAGINELIGM Champs Sur Marne Marne La Vallee France Niantic San Francisco CA USA

ISBN: (纸本)9781665445092

We present a novel method for predicting accurate depths from monocular images with high efficiency. This optimal efficiency is achieved by exploiting wavelet decomposition, which is integrated in a fully differentiable encoder-decoder architecture. We demonstrate that we can reconstruct high-fidelity depth maps by predicting sparse wavelet coefficients. In contrast with previous works, we show that wavelet coefficients can be learned without direct supervision on coefficients. Instead we supervise only the final depth image that is reconstructed through the inverse wavelet transform. We additionally show that wavelet coefficients can be learned in fully self-supervised scenarios, without access to ground-truth depth. Finally, we apply our method to different state-of-the-art monocular depth estimation models, in each case giving similar or better results compared to the original model, while requiring less than half the multiplyadds in the decoder network.

关键词： Analytical models Neural networks Estimation computer architecture Wavelet analysis Decoding pattern recognition

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TransER: Hybrid Model and Ensemble-based Sequential Learning for Non-homogenous Dehazing

TransER: Hybrid Model and Ensemble-based Sequential Learning...

引用

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

作者： Trung Hoang Haichuan Zhang Amirsaeed Yazdani Vishal Monga School of Electrical Engineering and Computer Science Pennsylvania State University Unversity Park USA

Image dehazing is one of the most challenging imaging inverse problems that estimates the haze-free images from hazy ones. While recent transformer/convolutional neural network-based methods have shown excellent performance in handling both homogeneous and non-homogeneous dehazing problems, these networks are often trained end-to-end to estimate the haze-free image directly and require a large number of parameters. In this work, we propose a novel, lightweight two-stage deep network for non-homogeneous dehazing. In particular, our proposed method, denoted as TransER, consists of two separate deep neural networks which are TransConv Fusion Dehaze (TFD) model in Stage I and Lightweight Ensemble Reconstruction (LER) network in Stage II. The first model (TFD) using transformer-based encoder and decoders generates two estimates of the haze-free image: a parameter-based dehazed output based on the physical modeling of the problem and a pseudo haze-free output generated directly by the model in an end-to-end fashion. LER in stage II reconstructs the final dehazed output fusing the two estimates from stage I. We incorporate knowledge distillation to develop a teacher network with the same architecture as LER, allowing it to supervise the intermediate features. Extensive experiments performed on challenging real and synthetic scene image datasets (NTIRE 2019-2023, and RESIDE-indoor) demonstrate that TransER can outperform many state-of-the-art competing methods while using a significantly lower number of parameters. The source code is available at https://***/trungpsu1210/TransER.

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Semi-Supervised Action recognition with Temporal Contrastive Learning

Semi-Supervised Action Recognition with Temporal Contrastive...

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

作者： Singh, Ankit Chakraborty, Omprakash Varshney, Ashutosh Panda, Rameswar Feris, Rogerio Saenko, Kate Das, Abir IIT Madras Chennai Tamil Nadu India IIT Kharagpur Kharagpur W Bengal India MIT IBM Watson AI Lab Cambridge MA USA Boston Univ Boston MA 02215 USA

ISBN: (纸本)9781665445092

Learning to recognize actions from only a handful of labeled videos is a challenging problem due to the scarcity of tediously collected activity labels. We approach this problem by learning a two-pathway temporal contrastive model using unlabeled videos at two different speeds leveraging the fact that changing video speed does not change an action. Specifically, we propose to maximize the similarity between encoded representations of the same video at two different speeds as well as minimize the similarity between different videos played at different speeds. This way we use the rich supervisory information in terms of 'time' that is present in otherwise unsupervised pool of videos. With this simple yet effective strategy of manipulating video playback rates, we considerably outperform video extensions of sophisticated state-of-the-art semi-supervised image recognition methods across multiple diverse benchmark datasets and network architectures. Interestingly, our proposed approach benefits from out-of-domain unlabeled videos showing generalization and robustness. We also perform rigorous ablations and analysis to validate our approach.

关键词： computer vision Image recognition Semantics Network architecture Benchmark testing Robustness pattern recognition

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FACESEC: A Fine-grained Robustness Evaluation Framework for Face recognition Systems

FACESEC: A Fine-grained Robustness Evaluation Framework for ...

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

作者： Tong, Liang Chen, Zhengzhang Ni, Jingchao Cheng, Wei Song, Dongjin Chen, Haifeng Vorobeychik, Yevgeniy Washington Univ St Louis MO 14263 USA NEC Labs Amer Princeton NJ 08540 USA Univ Connecticut Storrs CT USA

ISBN: (纸本)9781665445092

We present FACESEC, a framework for fine-grained robustness evaluation of face recognition systems. FACESEC evaluation is performed along four dimensions of adversarial modeling: the nature of perturbation (e.g., pixel-level or face accessories), the attacker's system knowledge (about training data and learning architecture), goals (dodging or impersonation), and capability (tailored to individual inputs or across sets of these). We use FACESEC to study five face recognition systems in both closed-set and open-set settings, and to evaluate the state-of-the-art approach for defending against physically realizable attacks on these. We find that accurate knowledge of neural architecture is significantly more important than knowledge of the training data in black-box attacks. Moreover, we observe that open-set face recognition systems are more vulnerable than closed-set systems under different types of attacks. The efficacy of attacks for other threat model variations, however, appears highly dependent on both the nature of perturbation and the neural network architecture. For example, attacks that involve adversarial face masks are usually more potent, even against adversarially trained models, and the ArcFace architecture tends to be more robust than the others.

关键词： computer vision Systematics Face recognition Perturbation methods Neural networks Training data computer architecture

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The Blessings of Unlabeled Background in Untrimmed Videos

The Blessings of Unlabeled Background in Untrimmed Videos

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

作者： Liu, Yuan Chen, Jingyuan Chen, Zhenfang Deng, Bing Huang, Jianqiang Zhang, Hanwang Alibaba Grp Hangzhou Peoples R China Univ Hong Kong Hong Kong Peoples R China Nanyang Technol Univ Singapore Singapore

ISBN: (纸本)9781665445092

Weakly-supervised Temporal Action Localization (WTAL) aims to detect the action segments with only video-level action labels in training. The key challenge is how to distinguish the action of interest segments from the background, which is unlabelled even on the video-level. While previous works treat the background as "curses", we consider it as "blessings". Specifically, we first use causal analysis to point out that the common localization errors are due to the unobserved confounder that resides ubiquitously in visual recognition. Then, we propose a Temporal Smoothing PCA-based (TS-PCA) deconfounder, which exploits the unlabelled background to model an observed substitute for the unobserved confounder, to remove the confounding effect. Note that the proposed deconfounder is model-agnostic and non-intrusive, and hence can be applied in any WTAL method without model re-designs. Through extensive experiments on four state-of-the-art WTAL methods, we show that the deconfounder can improve all of them on the public datasets: THUMOS-14 and ActivityNet-1.3(1).

关键词： Location awareness Training Visualization computer vision Smoothing methods Computational modeling pattern recognition

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ELSA: Exploiting Layer-wise N:M Sparsity for vision Transformer Acceleration

ELSA: Exploiting Layer-wise N:M Sparsity for Vision Transfor...

引用

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

作者： Ning-Chi Huang Chi-Chih Chang Wei-Cheng Lin Endri Taka Diana Marculescu Kai-Chiang Wu National Yang Ming Chiao Tung University University of Texas at Austin

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

N:M sparsity is an emerging model compression method supported by more and more accelerators to speed up sparse matrix multiplication in deep neural networks. Most existing N:M sparsity methods compress neural networks with a uniform setting for all layers in a network or heuristically determine the layer-wise configuration by considering the number of parameters in each layer. However, very few methods have been designed for obtaining a layer-wise customized N:M sparse configuration for vision transformers (ViTs), which usually consist of transformer blocks involving the same number of *** this work, to address the challenge of selecting suitable sparse configuration for ViTs on N:M sparsity-supporting accelerators, we propose ELSA, Exploiting Layer-wise N:M Sparsity for ViTs. Considering not only all N:M sparsity levels supported by a given accelerator but also the expected throughput improvement, our methodology can reap the benefits of accelerators supporting mixed sparsity by trading off negligible accuracy loss with both memory usage and inference time reduction for ViT models. For instance, our approach achieves a noteworthy 2.9× reduction in FLOPs to both Swin-B and DeiT-B with only a marginal degradation of accuracy on ImageNet. Our code is publicly available at https://***/ningchihuang/ELSA.

关键词： Training Degradation computer vision Accuracy Transformers Throughput Software

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Efficient local correlation volume for unsupervised optical flow estimation on small moving objects in large satellite images

Efficient local correlation volume for unsupervised optical ...

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

作者： Sarra Khairi Etienne Meunier Renaud Fraisse Patrick Bouthemy Inria Rennes France Airbus Defence & Space Toulouse France

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

With the advent of deep learning methods, performance and efficiency of optical flow estimation has significantly increased, especially for supervised models. However, they do not generalize well to more specific data involving small moving objects in large images, such as high-resolution aerial or satellite sequences. In addition, annotation and realistic simulation are difficult for these contents, which calls for unsupervised alternatives. Yet, the latter are still less accurate than their supervised counterparts. In this paper, we introduce an unsupervised local optical flow estimation method adapted to small moving objects in large-size images by involving no downsampling of the feature maps. We adopt a local correlation search and implement it in an original way with a per-shift computation, which minimizes memory consumption and speed up inference computation for large-scale images. We also design a loss function combining similarity, smoothness and sparsity constraints. We demonstrate the performance of our SMOFlow method on real stabilized aerial videos fully representative of future satellite conditions. SMOFlow favorably compares to other methods. Our SMOFlow method is able to accurately capture the motion of small objects in large images, while efficiently reducing memory consumption.

关键词： computer vision Solid modeling Correlation Satellites Memory management Estimation Satellite images

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DemosaicFormer: Coarse-to-Fine Demosaicing Network for HybridEVS Camera

DemosaicFormer: Coarse-to-Fine Demosaicing Network for Hybri...

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

作者： Senyan Xu Zhijing Sun Jiaying Zhu Yurui Zhu Xueyang Fu Zheng-Jun Zha University of Science and Technology of China

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Hybrid Event-Based vision Sensor (HybridEVS) is a novel sensor integrating traditional frame-based and event-based sensors, offering substantial benefits for applications requiring low-light, high dynamic range, and low-latency environments, such as smartphones and wearable devices. Despite its potential, the lack of Image signal processing (ISP) pipeline specifically designed for HybridEVS poses a significant challenge. To address this challenge, in this study, we propose a coarse-to-fine framework named DemosaicFormer which comprises coarse demosaicing and pixel correction. Coarse demosaicing network is designed to produce a preliminary high-quality estimate of the RGB image from the HybridEVS raw data while the pixel correction network enhances the performance of image restoration and mitigates the impact of defective pixels. Our key innovation is the design of a Multi-Scale Gating Module (MSGM) applying the integration of cross-scale features, which allows feature information to flow between different scales. Additionally, the adoption of progressive training and data augmentation strategies further improves model’s robustness and effectiveness. Experimental results show superior performance against the existing methods both qualitatively and visually, and our DemosaicFormer achieves the best performance in terms of all the evaluation metrics in the MIPI 2024 challenge on Demosaic for Hybridevs Camera. The code is available at this repository.

关键词： Training Measurement Technological innovation vision sensors Cameras Data augmentation Robustness

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