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Audio Provenance Analysis in Heterogeneous Media Sets

Audio Provenance Analysis in Heterogeneous Media Sets

引用

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

作者： Milica Gerhardt Luca Cuccovillo Patrick Aichroth Fraunhofer Institute for Digital Media Technology IDMT Ilemanu Germany

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

This paper introduces a framework for Audio Provenance Analysis, addressing the complex challenge of ana-lyzing heterogeneous sets of audio items without requiring any prior knowledge of their content. Our framework applies a novel approach that combines partial audio matching and phylogeny techniques. It constructs directed acyclic graphs to capture the origins and the evolution of content within near-duplicate audio clusters, identifying the least altered versions and tracing the reuse of content within these clusters. The approach is evaluated for two selected application scenarios, demonstrating that it can accurately determine the direction of content reuse and identify parent-child relationships, while also offering a dedicated dataset for benchmarking future research in this area.

关键词： Directed acyclic graph computer vision conferences Media Benchmark testing Phylogeny pattern recognition

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Domain Consensus Clustering for Universal Domain Adaptation

Domain Consensus Clustering for Universal Domain Adaptation

引用

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

作者： Li, Guangrui Kang, Guoliang Zhu, Yi Wei, Yunchao Yang, Yi Univ Technol Sydney ReLER Lab AAII Sydney NSW Australia Carnegie Mellon Univ Pittsburgh PA 15213 USA Amazon Web Serv Seattle WA USA

ISBN: (纸本)9781665445092

In this paper, we investigate Universal Domain Adaptation (UniDA) problem, which aims to transfer the knowledge from source to target under unaligned label space. The main challenge of UniDA lies in how to separate common classes (i.e., classes shared across domains), from private classes (i.e., classes only exist in one domain). Previous works treat the private samples in the target as one generic class but ignore their intrinsic structure. Consequently, the resulting representations are not compact enough in the latent space and can be easily confused with common samples. To better exploit the intrinsic structure of the target domain, we propose Domain Consensus Clustering (DCC), which exploits the domain consensus knowledge to discover discriminative clusters on both common samples and private ones. Specifically, we draw the domain consensus knowledge from two aspects to facilitate the clustering and the private class discovery, i.e., the semantic-level consensus, which identifies the cycle-consistent clusters as the common classes, and the sample-level consensus, which utilizes the cross-domain classification agreement to determine the number of clusters and discover the private classes. Based on DCC, we are able to separate the private classes from the common ones, and differentiate the private classes themselves. Finally, we apply a class-aware alignment technique on identified common samples to minimize the distribution shift, and a prototypical regularizer to inspire discriminative target clusters. Experiments on four benchmarks demonstrate DCC significantly outperforms previous state-of-the-arts.

关键词： computer vision Benchmark testing pattern recognition

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Soteria: Provable Defense against Privacy Leakage in Federated Learning from Representation Perspective

Soteria: Provable Defense against Privacy Leakage in Federat...

引用

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

作者： Sun, Jingwei Li, Ang Wang, Binghui Yang, Huanrui Li, Hai Chen, Yiran Duke Univ Dept Elect & Comp Engn Durham NC 27706 USA

ISBN: (纸本)9781665445092

Federated learning (FL) is a popular distributed learning framework that can reduce privacy risks by not explicitly sharing private data. However, recent works have demonstrated that sharing model updates makes FL vulnerable to inference attack. In this work, we show our key observation that the data representation leakage from gradients is the essential cause of privacy leakage in FL. We also provide an analysis of this observation to explain how the data presentation is leaked. Based on this observation, we propose a defense called Soteria against model inversion attack in FL. The key idea of our defense is learning to perturb data representation such that the quality of the reconstructed data is severely degraded, while FL performance is maintained. In addition, we derive a certified robustness guarantee to FL and a convergence guarantee to FedAvg, after applying our defense. To evaluate our defense, we conduct experiments on MNIST and CIFAR10 for defending against the DLG attack and GS attack. Without sacrificing accuracy, the results demonstrate that our proposed defense can increase the mean squared error between the reconstructed data and the raw data by as much as 160x for both DLG attack and GS attack, compared with baseline defense methods. Therefore, the privacy of the FL system is significantly improved. Our code can be found at https://***/jeremy313/Soteria.

关键词： Privacy Data privacy computer vision Distance learning Distributed databases Collaborative work Robustness

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Few-Shot Segmentation Without Meta-Learning: A Good Transductive Inference Is All You Need?

Few-Shot Segmentation Without Meta-Learning: A Good Transduc...

引用

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

作者： Boudiaf, Malik Kervadec, Hoel Masud, Ziko Imtiaz Piantanida, Pablo Ben Ayed, Ismail Dolz, Jose ETS Montreal Montreal PQ Canada Univ Paris Saclay CNRS Cent Supelec Paris France

ISBN: (纸本)9781665445092

We show that the way inference is performed in few-shot segmentation tasks has a substantial effect on performances-an aspect often overlooked in the literature in favor of the meta-learning paradigm. We introduce a transductive inference for a given query image, leveraging the statistics of its unlabeled pixels, by optimizing a new loss containing three complementary terms: i) the cross-entropy on the labeled support pixels;ii) the Shannon entropy of the posteriors on the unlabeled query-image pixels;and iii) a global KL-divergence regularizer based on the proportion of the predicted foreground. As our inference uses a simple linear classifier of the extracted features, its computational load is comparable to inductive inference and can be used on top of any base training. Foregoing episodic training and using only standard cross-entropy training on the base classes, our inference yields competitive performances on standard benchmarks in the 1-shot scenarios. As the number of available shots increases, the gap in performances widens: on PASCAL-5(i), our method brings about 5% and 6% improvements over the state-of-the-art, in the 5- and 10-shot scenarios, respectively. Furthermore, we introduce a new setting that includes domain shifts, where the base and novel classes are drawn from different datasets. Our method achieves the best performances in this more realistic setting. Our code is freely available online: https://***/mboudiaf/RePRI-for-Few-Shot-Segmentation.

关键词： Training Image segmentation computer vision Codes Benchmark testing Feature extraction Entropy

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Backdoor Attacks Against Deep Learning Systems in the Physical World

Backdoor Attacks Against Deep Learning Systems in the Physic...

引用

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

作者： Wenger, Emily Passananti, Josephine Bhagoji, Arjun Nitin Yao, Yuanshun Zheng, Haitao Zhao, Ben Y. Univ Chicago Dept Comp Sci Chicago IL 60637 USA

ISBN: (纸本)9781665445092

Backdoor attacks embed hidden malicious behaviors into deep learning models, which only activate and cause misclassifications on model inputs containing a specific "trigger." Existing works on backdoor attacks and defenses, however, mostly focus on digital attacks that apply digitally generated patterns as triggers. A critical question remains unanswered: "can backdoor attacks succeed using physical objects as triggers, thus making them a credible threat against deep learning systems in the real world?" We conduct a detailed empirical study to explore this question for facial recognition, a critical deep learning task. Using 7 physical objects as triggers, we collect a custom dataset of 3205 images of 10 volunteers and use it to study the feasibility of "physical" backdoor attacks under a variety of real-world conditions. Our study reveals two key findings. First, physical backdoor attacks can be highly successful if they are carefully configured to overcome the constraints imposed by physical objects. In particular, the placement of successful triggers is largely constrained by the target model's dependence on key facial features. Second, four of today's state-of-the-art defenses against (digital) backdoors are ineffective against physical backdoors, because the use of physical objects breaks core assumptions used to construct these defenses. Our study confirms that (physical) backdoor attacks are not a hypothetical phenomenon but rather pose a serious real-world threat to critical classification tasks. We need new and more robust defenses against backdoors in the physical world.

关键词： Deep learning computer vision Face recognition Robustness Task analysis Facial features

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Human-like Controllable Image Captioning with Verb-specific Semantic Roles

Human-like Controllable Image Captioning with Verb-specific ...

引用

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

作者： Chen, Long Jiang, Zhihong Xiao, Jun Liu, Wei Zhejiang Univ Hangzhou Peoples R China Tencent AI Lab Bellevue WA USA Columbia Univ New York NY 10027 USA Tencent Data Platform New York NY USA

ISBN: (纸本)9781665445092

Controllable Image Captioning (CIC) - generating image descriptions following designated control signals- has received unprecedented attention over the last few years. To emulate the human ability in controlling caption generation, current CIC studies focus exclusively on control signals concerning objective properties, such as contents of interest or descriptive patterns. However, we argue that almost all existing objective control signals have overlooked two indispensable characteristics of an ideal control signal: 1) Event-compatible: all visual contents referred to in a single sentence should be compatible with the described activity. 2) Sample-suitable: the control signals should be suitable for a specific image sample. To this end, we propose a new control signal for CIC: Verb-specific Semantic Roles (VSR). VSR consists of a verb and some semantic roles, which represents a targeted activity and the roles of entities involved in this activity. Given a designated VSR, we first train a grounded semantic role labeling (GSRL) model to identify and ground all entities for each role. Then, we propose a semantic structure planner (SSP) to learn human-like descriptive semantic structures. Lastly, we use a roleshift captioning model to generate the captions. Extensive experiments and ablations demonstrate that our framework can achieve better controllability than several strong baselines on two challenging CIC benchmarks. Besides, we can generate multi-level diverse captions easily.

关键词： Visualization computer vision Codes Semantics Benchmark testing Controllability pattern recognition

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Searching by Generating: Flexible and Efficient One-Shot NAS with Architecture Generator

Searching by Generating: Flexible and Efficient One-Shot NAS...

引用

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

作者： Huang, Sian-Yao Chu, Wei-Ta Natl Cheng Kung Univ Tainan Taiwan

ISBN: (纸本)9781665445092

In one-shot NAS, sub-networks need to be searched from the supernet to meet different hardware constraints. However, the search cost is high and N times of searches are needed for N different constraints. In this work, we propose a novel search strategy called architecture generator to search sub-networks by generating them, so that the search process can be much more efficient and flexible. With the trained architecture generator, given target hardware constraints as the input, N good architectures can be generated for N constraints by just one forward pass without re-searching and supernet retraining. Moreover, we propose a novel single-path supernet, called unified supernet, to further improve search efficiency and reduce GPU memory consumption of the architecture generator. With the architecture generator and the unified supernet, we propose a flexible and efficient one-shot NAS framework, called Searching by Generating NAS (SGNAS). With the pre-trained supernt, the search time of SGNAS forN different hardware constraints is only 5 GPU hours, which is 4N times faster than previous SOTA single-path methods. After training from scratch, the top1-accuracy of SGNAS on ImageNet is 77.1%, which is comparable with the SOTAs.

关键词： Training computer vision Costs Codes Memory management Graphics processing units Search problems

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MetaCorrection: Domain-aware Meta Loss Correction for Unsupervised Domain Adaptation in Semantic Segmentation

MetaCorrection: Domain-aware Meta Loss Correction for Unsupe...

引用

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

作者： Guo, Xiaoqing Yang, Chen Li, Baopu Yuan, Yixuan City Univ Hong Kong Hong Kong Peoples R China Baidu USA Sunnyvale CA USA

ISBN: (纸本)9781665445092

Unsupervised domain adaptation (UDA) aims to transfer the knowledge from the labeled source domain to the unlabeled target domain. Existing self-training based UDA approaches assign pseudo labels for target data and treat them as ground truth labels to fully leverage unlabeled target data for model adaptation. However;the generated pseudo labels from the model optimized on the source domain inevitably contain noise due to the domain gap. To tackle this issue, we advance a MetaCorrection framework, where a Domain-aware Meta-learning strategy is devised to benefit Loss Correction (DMLC) for UDA semantic segmentation. In particular, we model the noise distribution of pseudo labels in target domain by introducing a noise transition matrix (NTM) and construct meta data set with domain-invariant source data to guide the estimation of NTM. Through the risk minimization on the meta data set, the optimized NTM thus can correct the noisy issues in pseudo labels and enhance the generalization ability of the model on the target data. Considering the capacity gap between shallow and deep features, we fitrther employ the proposed DMLC strategy to provide matched and compatible supervision signals for different level features, thereby ensuring deep adaptation. Extensive experimental results highlight the effectiveness of our method' against existing state-of-the-art methods on three benchmarks.

关键词： Bridges Adaptation models computer vision Computational modeling Semantics Estimation Metadata

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Online Multiple Object Tracking with Cross-Task Synergy

Online Multiple Object Tracking with Cross-Task Synergy

引用

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

作者： Guo, Song Wang, Jingya Wang, Xinchao Tao, Dacheng Univ Sydney Sydney NSW Australia ShanghaiTech Univ Shanghai Peoples R China Natl Univ Singapore Singapore Singapore Stevens Inst Technol Hoboken NJ 07030 USA

ISBN: (纸本)9781665445092

Modern online multiple object tracking (MOT) methods usually focus on two directions to improve tracking performance. One is to predict new positions in an incoming frame based on tracking information from previous frames, and the other is to enhance data association by generating more discriminative identity embeddings. Some works combined both directions within one framework but handled them as two individual tasks, thus gaining little mutual benefits. In this paper, we propose a novel unified model with synergy between position prediction and embedding association. The two tasks are linked by temporal-aware target attention and distractor attention, as well as identity-aware memory aggregation model. Specifically, the attention modules can make the prediction focus more on targets and less on distractors, therefore more reliable embeddings can be extracted accordingly for association. On the other hand, such reliable embeddings can boost identity-awareness through memory aggregation, hence strengthen attention modules and suppress drifts. In this way, the synergy between position prediction and embedding association is achieved, which leads to strong robustness to occlusions. Extensive experiments demonstrate the superiority of our proposed model over a wide range of existing methods on MOTChallenge benchmarks. Our code and models are publicly available at https://***/songguocod/TADAM.

关键词： computer vision Codes Interference Predictive models Benchmark testing Reliability engineering Robustness

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Look Closer to Segment Better: Boundary Patch Refinement for Instance Segmentation

Look Closer to Segment Better: Boundary Patch Refinement for...

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

作者： Tang, Chufeng Chen, Hang Li, Xiao Li, Jianmin Zhang, Zhaoxiang Hu, Xiaolin Tsinghua Univ THU Bosch JCML Ctr State Key Lab Intelligent Technol & Syst Dept Comp Sci & TechnolInst AIBNRist Beijing Peoples R China Chinese Acad Sci Inst Automat Beijing Peoples R China Univ Chinese Acad Sci Beijing Peoples R China Chinese Acad Sci Ctr Artificial Intelligence & Robot HKISI Beijing Peoples R China

ISBN: (纸本)9781665445092

Tremendous efforts have been made on instance segmentation but the mask quality is still not satisfactory. The boundaries of predicted instance masks are usually imprecise due to the low spatial resolution of feature maps and the imbalance problem caused by the extremely low proportion of boundary pixels. To address these issues, we propose a conceptually simple yet effective post-processing refinement framework to improve the boundary quality based on the results of any instance segmentation model, termed BPR. Following the idea of looking closer to segment boundaries better, we extract and refine a series of small boundary patches along the predicted instance boundaries. The refinement is accomplished by a boundary patch refinement network at higher resolution. The proposed BPR framework yields significant improvements over the Mask R-CNN baseline on Cityscapes benchmark, especially on the boundary-aware metrics. Moreover, by applying the BPR framework to the "PolyTransform + SegFix" baseline, we reached 1st place on the Cityscapes leaderboard. Code is available at https://***/tinyalpha/BPR.

关键词： Measurement computer vision Codes Benchmark testing Business process re-engineering pattern recognition Spatial resolution

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