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CondenseNet V2: Sparse Feature Reactivation for Deep Networks

CondenseNet V2: Sparse Feature Reactivation for Deep Network...

引用

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

作者： Yang, Le Jiang, Haojun Cai, Ruojin Wang, Yulin Song, Shiji Huang, Gao Tian, Qi Tsinghua Univ Dept Automat Beijing Natl Res Ctr Informat Sci & Technol BNRis Beijing Peoples R China Cornell Univ Ithaca NY 14853 USA Huawei Cloud & AI Ithaca NY USA

ISBN: (纸本)9781665445092

Reusing features in deep networks through dense connectivity is an effective way to achieve high computational efficiency. The recent proposed CondenseNet [14] has shown that this mechanism can befiirther improved if redundant features are removed. In this paper, we propose an alternative approach named sparse feature reactivation (SFR), aiming at actively increasing the utility of features for reusing. In the proposed network, named CondenseNetV2, each layer can simultaneously learn to 1) selectively reuse a set of most important features from preceding layers;and 2) actively update a set of preceding features to increase their utility for later layers. Our experiments show that the proposed models achieve promising performance on image classification (ImageNet and CIFAR) and object detection (MS COCO) in terms of both theoretical efficiency and practical speed.

关键词： Training computer vision Computational modeling Object detection computer architecture Search problems pattern recognition

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Protecting Intellectual Property of Generative Adversarial Networks from Ambiguity Attacks

Protecting Intellectual Property of Generative Adversarial N...

引用

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

作者： Ong, Ding Sheng Chan, Chee Seng Ng, Kam Woh Fan, Lixin Yang, Qiang Univ Malaya Kuala Lumpur Malaysia WeBank AI Lab Kuala Lumpur Malaysia Hong Kong Univ Sci & Technol Hong Kong Peoples R China

ISBN: (纸本)9781665445092

Ever since Machine Learning as a Service emerges as a viable business that utilizes deep learning models to generate lucrative revenue, Intellectual Property Right (IPR) has become a major concern because these deep learning models can easily be replicated, shared, and re-distributed by any unauthorized third parties. To the best of our knowledge, one of the prominent deep learning models - Generative Adversarial Networks (GANs) which has been widely used to create photorealistic image are totally unprotected despite the existence of pioneering IPR protection methodology for Convolutional Neural Networks (CNNs). This paper therefore presents a complete protection framework in both black-box and white-box settings to enforce IPR protection on GANs. Empirically, we show that the proposed method does not compromise the original GANs performance (i.e. image generation, image super-resolution, style transfer), and at the same time, it is able to withstand both removal and ambiguity attacks against embedded watermarks. Codes are available at https://***/dingsheng-ong/ipr-gan.

关键词： Deep learning Knowledge engineering computer vision Image synthesis Superresolution Intellectual property Watermarking

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Bilevel Online Adaptation for Out-of-Domain Human Mesh Reconstruction

Bilevel Online Adaptation for Out-of-Domain Human Mesh Recon...

引用

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

作者： Guan, Shanyan Xu, Jingwei Wang, Yunbo Ni, Bingbing Yang, Xiaokang Shanghai Jiao Tong Univ Shanghai 200240 Peoples R China Shanghai Jiao Tong Univ AI Inst MoE Key Lab Artificial Intelligence Shanghai Peoples R China

ISBN: (纸本)9781665445092

This paper considers a new problem of adapting a pretrained model of human mesh reconstruction to out-of-domain streaming videos. However, most previous methods based on the parametric SMPL model [36] underperform in new domains with unexpected, domain-specific attributes, such as camera parameters, lengths of bones, backgrounds, and occlusions. Our general idea is to dynamically fine-tune the source model on test video streams with additional temporal constraints, such that it can mitigate the domain gaps without over-fitting the 2D information of individual test frames. A subsequent challenge is how to avoid conflicts between the 2D and temporal constraints. We propose to tackle this problem using a new training algorithm named Bilevel Online Adaptation (BOA), which divides the optimization process of overall multi-objective into two steps of weight probe and weight update in a training iteration. We demonstrate that BOA leads to state-of-the-art results on two human mesh reconstruction benchmarks(1).

关键词： Training Adaptation models computer vision Benchmark testing Reconstruction algorithms Cameras pattern recognition

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PointNetLK Revisited

PointNetLK Revisited

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

作者： Li, Xueqian Pontes, Jhony Kaesemodel Lucey, Simon Argo AI Pittsburgh PA 15222 USA Univ Adelaide Adelaide SA Australia Carnegie Mellon Univ Pittsburgh PA 15213 USA

ISBN: (纸本)9781665445092

We address the generalization ability of recent learning-based point cloud registration methods. Despite their success, these approaches tend to have poor performance when applied to mismatched conditions that are not well-represented in the training set, such as unseen object categories, different complex scenes, or unknown depth sensors. In these circumstances, it has often been better to rely on classical non-learning methods (e.g., Iterative Closest Point), which have better generalization ability. Hybrid learning methods, that use learning for predicting point correspondences and then a deterministic step for alignment, have offered some respite, but are still limited in their generalization abilities. We revisit a recent innovation-PointNetLK [1]-and show that the inclusion of an analytical Jacobian can exhibit remarkable generalization properties while reaping the inherent fidelity benefits of a learning framework. Our approach not only outperforms the state-of-the-art in mismatched conditions but also produces results competitive with current learning methods when operating on real-world test data close to the training set.

关键词： Training Jacobian matrices Solid modeling Three-dimensional displays Robustness Sensors pattern recognition

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Latent Flow Diffusion for Deepfake Video Generation

Latent Flow Diffusion for Deepfake Video Generation

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

作者： Aashish Chandra K Aashutosh A V Srijan Das Abhijit Das Birla Institute of Technology & Science Pilani India University of North Carolina Charlotte USA

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Image-to-video generation with conditional identity swap popularly known as deepfake, aims to synthesize a new video for the target identity guided by an image of the target and a video of the source identity. The biggest challenge of these tasks lies in the simultaneous generation of realistic spatial appearance and temporal dynamics corresponding to the given target image and source video. In this paper, we propose a deepfake generation technique using novel latent flow diffusion (LFD) that includes an optical flow sequence in the latent space based on a given source video to warp the given target image. Compared to previous works on video diffusion, our proposed LFD can swap the spatial details maintaining temporal information by utilizing the spatial content of the given target image and employing the latent flow of the source video. Our model consists of three stages: a Flow predictor model captures the optical flow of the source video, two-fold Transformer encoding layers predict the driving frame and a conditioned image-to-video generator guided by the driving frame generates the final deep fake video. We conducted multiple experiments and our proposed model has consistently outperformed prior video diffusion models for deepfake generation.

关键词： Deepfakes computer vision Image motion analysis Image coding Predictive models Transformers Generators

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Drop the GAN: In Defense of Patches Nearest Neighbors as Single Image Generative Models

Drop the GAN: In Defense of Patches Nearest Neighbors as Sin...

引用

2022 ieee/cvf conference on computer vision and pattern recognition, CVPR 2022

作者： Granot, Niv Feinstein, Ben Shocher, Assaf Bagon, Shai Irani, Michal The Weizmann Institute of Science Dept. of Computer Science and Applied Math Israel Israel

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Image manipulation dates back long before the deep learning era. The classical prevailing approaches were based on maximizing patch similarity between the input and generated output. Recently, single-image GANs were introduced as a superior and more sophisticated solution to image manipulation tasks. Moreover, they offered the opportunity not only to manipulate a given image, but also to generate a large and diverse set of different outputs from a single natural image. This gave rise to new tasks, which are considered 'GAN-only'. However, despite their impressiveness, single-image GANs require long training time (usually hours) for each image and each task and often suffer from visual artifacts. In this paper we revisit the classical patch-based methods, and show that - unlike previously believed - classical methods can be adapted to tackle these novel 'GAN-only' tasks. Moreover, they do so better and faster than single-image GAN-based methods. More specifically, we show that: (i) by introducing slight modifications, classical patch-based methods are able to unconditionally generate diverse images based on a single natural image;(ii) the generated output visual quality exceeds that of single-image GANs by a large margin (confirmed both quantitatively and qualitatively);(iii) they are orders of magnitude faster (runtime reduced from hours to seconds). 22This project received funding from the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme (grant agreement No 788535), and the Carolito Stiftung. Dr Bagon is a Robin Chemers Neustein AI Fellow. © 2022 ieee.

关键词： Training Deep learning Visualization Technological innovation computer vision Runtime Europe

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3D Human Pose Estimation with Occlusions: Introducing BlendMimic3D Dataset and GCN Refinement

3D Human Pose Estimation with Occlusions: Introducing BlendM...

引用

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

作者： Filipa Lino Carlos Santiago Manuel Marques LARSyS Instituto Superior Técnico Institute for Systems and Robotics Portugal

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

In the field of 3D Human Pose Estimation (HPE), accurately estimating human pose, especially in scenarios with occlusions, is a significant challenge. This work identifies and addresses a gap in the current state of the art in 3D HPE concerning the scarcity of data and strategies for handling occlusions. We introduce our novel BlendMimic3D dataset, designed to mimic real-world situations where occlusions occur for seamless integration in 3D HPE algorithms. Additionally, we propose a 3D pose refinement block, employing a Graph Convolutional Network (GCN) to enhance pose representation through a graph model. This GCN block acts as a plug-and-play solution, adaptable to various 3D HPE frameworks without requiring retraining them. By training the GCN with occluded data from Blend-Mimic3D, we demonstrate significant improvements in resolving occluded poses, with comparable results for non-occluded ones. Project web page is available at https://***/BlendMimic3D/.

关键词： Training Solid modeling computer vision Three-dimensional displays Graph convolutional networks conferences Pose estimation

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Unknown Sample Discovery for Source Free Open Set Domain Adaptation

Unknown Sample Discovery for Source Free Open Set Domain Ada...

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

作者： Chowdhury Sadman Jahan Andreas Savakis Center for Imaging Science Rochester Institute of Technology Rochester NY USA Department of Computer Engineering Rochester Institute of Technology Rochester NY USA

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Open Set Domain Adaptation (OSDA) aims to adapt a model trained on a source domain to a target domain that undergoes distribution shift and contains samples from novel classes outside the source domain. Test-time or source-free OSDA (SF-OSDA) techniques eliminate the need to access source domain samples, but current SF-OSDA methods utilize only the known classes in the target domain for adaptation, and require access to the entire target domain even during inference after adaptation, to distinguish between known and unknown samples. In this paper, we introduce Unknown Sample Discovery (USD) as an SF-OSDA method that utilizes a temporally ensembled teacher model to conduct known-unknown target sample separation and adapts the student model to the target domain over all classes using co-training and temporal consistency between the teacher and the student. USD promotes Jensen-Shannon distance (JSD) as an effective measure for known-unknown sample separation. Our teacher-student framework significantly reduces error accumulation resulting from imperfect known-unknown sample separation, while curriculum guidance helps to reliably learn the distinction between target known and target unknown subspaces. USD appends the target model with an unknown class node, thus readily classifying a target sample into any of the known or unknown classes in subsequent post-adaptation inference stages. Empirical results show that USD is superior to existing SF-OSDA methods and is competitive with current OSDA models that require both source and target domains during adaptation.

关键词： Adaptation models computer vision conferences Computational modeling Data models pattern recognition Reliability

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Seeing Through the Data: A Statistical Evaluation of Prohibited Item Detection Benchmark Datasets for X-ray Security Screening

Seeing Through the Data: A Statistical Evaluation of Prohibi...

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

作者： Brian K. S. Isaac-Medina Seyma Yucer Neelanjan Bhowmik Toby P. Breckon Department of Computer Science Durham University UK Department of Engineering Durham University UK

The rapid progress in automatic prohibited object detection within the context of X-ray security screening, driven forward by advances in deep learning, has resulted in the first internationally-recognized, application-focused object detection performance standard (ECAC Common Testing Methodology for Automated Prohibited Item Detection Systems). However, the ever-increasing volume of detection work in this application area is highly reliant on a limited set of large-scale benchmark detection datasets that are specific to this domain. This study provides a comprehensive quantitative analysis of the underlying distribution of the prohibited item instances in three of the most prevalent X-ray security imagery benchmark and how these correlate against the detection performance of six state-of-the-art object detectors spanning multiple contemporary object detection paradigms. We focus on object size, location and aspect ratio within the image in addition to looking at global properties such as image colour distribution. Our results show a clear correlation between false negative (missed) detections and object size with the distribution of undetected items being statistically smaller in size than those typically found in the corresponding dataset as a whole. For false positive detections, the size distribution of such false alarm instances is shown to differ from the corresponding dataset test distribution in all cases. Furthermore, we observe that one-stage, anchor-free object detectors may be more vulnerable to the detection of heavily occluded or cluttered objects than other approaches whilst the detection of smaller prohibited item instances such as bullets remains more challenging than other object types.

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Learning Multi-Scale Photo Exposure Correction

Learning Multi-Scale Photo Exposure Correction

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

作者： Afifi, Mahmoud Derpanis, Konstantinos G. Ommer, Bjoern Brown, Michael S. Samsung AI Ctr SAIC Toronto ON Canada York Univ N York ON Canada Heidelberg Univ Heidelberg Germany

ISBN: (纸本)9781665445092

Capturing photographs with wrong exposures remains a major source of errors in camera-based imaging. Exposure problems are categorized as either: (i) overexposed, where the camera exposure was too long, resulting in bright and washed-out image regions, or (ii) underexposed, where the exposure was too short, resulting in dark regions. Both under- and overexposure greatly reduce the contrast and visual appeal of an image. Prior work mainly focuses on underexposed images or general image enhancement. In contrast, our proposed method targets both over- and underexposure errors in photographs. We formulate the exposure correction problem as two main sub-problems: (i) color enhancement and (ii) detail enhancement. Accordingly, we propose a coarse-to-fine deep neural network (DNN) model, trainable in an end-to-end manner, that addresses each subproblem separately. A key aspect of our solution is a new dataset of over 24,000 images exhibiting the broadest range of exposure values to date with a corresponding properly exposed image. Our method achieves results on par with existing state-of-the-art methods on underexposed images and yields significant improvements for images suffering from overexposure errors.

关键词： Deep learning Visualization computer vision Image color analysis Cameras pattern recognition Image enhancement

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