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检索条件"任意字段=Conference on Computer Vision and Pattern Recognition"

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CENet: Consolidation-and-Exploration Network for Continuous Domain Adaptation

CENet: Consolidation-and-Exploration Network for Continuous ...

引用

IEEE/CVF conference on computer vision and pattern recognition (CVPR)

作者： Zhang, Chi Cheng, Yalu Wei, Pengxu He, Hongliang Chen, Jie Peking Univ Sch Elect & Comp Engn Shenzhen Peoples R China Peng Cheng Lab Shenzhen Peoples R China Sun Yat Sen Univ Guangzhou Peoples R China

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Unsupervised Domain Adaptation (UDA) deals with transferring knowledge from labeled source domains to an unlabeled target domain under domain shift. However, this does not reflect the breadth of scenarios that arise in real-world applications since source domains could increase. A plausible conjecture is: can we train a life-long learning model learned on continuous source domains given the target without the presence of labels? We formalize this task as the Continuous Domain Adaptation (CDA) and empirically show that conventional domain adaptation methods may suffer severe generalization deterioration due to the limited incremental transferability and negative transfer. To tackle this issue, we propose a novel sample-to-sample framework-Consolidationand-Exploration Network (CENet) to facilitate incremental transferring. This method underscores both the qualitative and quantitative relationship between samples. Moreover, we conduct comprehensive experiments to evaluate the effectiveness of each component in our pair-based method. Extensive experiments show that our approach achieves significant improvement over related state-of-the-art methods.

关键词： Representation learning Adaptation models computer vision Computational modeling conferences Reliability engineering Data models

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A Tale of Two CILs: The Connections between Class Incremental Learning and Class Imbalanced Learning, and Beyond

A Tale of Two CILs: The Connections between Class Incrementa...

引用

IEEE/CVF conference on computer vision and pattern recognition (CVPR)

作者： He, Chen Wang, Ruiping Chen, Xilin Chinese Acad Sci Inst Comp Technol Key Lab Intelligent Informat Proc CAS Beijing 100190 Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China

ISBN: (纸本)9781665448994

Catastrophic forgetting, the main challenge of Class Incremental Learning, is closely related to the classifier's bias due to imbalanced data, and most researchers resort to empirical techniques to remove the bias. Such anti-bias tricks share many ideas with the field of Class Imbalanced Learning, which encourages us to reflect on why these tricks work, and how we can design more principled solutions from a different perspective. In this paper, we comprehensively analyze the connections and seek possible collaborations between these two fields, i.e. Class Incremental Learning and Class Imbalanced Learning. Specifically, we first provide a panoramic view of recent bias correction tricks from the perspective of handling class imbalance. Then, we show that an adapted post-scaling technique which originates from Class Imbalanced Learning is on par with or even outperforms SOTA Class Incremental Learning method. Visualization via violin plots and polar charts further sheds light on how SOTA methods address the class imbalance problem from a more intuitive geometric perspective. These findings may encourage further infiltration between the two closely connected fields, but also raise concerns about whether it is correct that Class Incremental Learning degenerates into a class imbalance problem.

关键词： Learning systems computer vision conferences Collaboration pattern recognition

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From Pixels to Graphs: Open-Vocabulary Scene Graph Generation with vision-Language Models

From Pixels to Graphs: Open-Vocabulary Scene Graph Generatio...

引用

IEEE/CVF conference on computer vision and pattern recognition (CVPR)

作者： Li, Rongjie Zhang, Songyang Lin, Dahua Chen, Kai He, Xuming ShanghaiTech Univ Sch Informat Sci & Technol Shanghai Peoples R China Shanghai AI Lab Shanghai Peoples R China Shanghai Engn Res Ctr Intelligent Vis & Imaging Shanghai Peoples R China

ISBN: (纸本)9798350353006

Scene graph generation (SGG) aims to parse a visual scene into an intermediate graph representation for down-stream reasoning tasks. Despite recent advancements, existing methods struggle to generate scene graphs with novel visual relation concepts. To address this challenge, we introduce a new open-vocabulary SGG framework based on sequence generation. Our framework leverages vision-language pre-trained models (VLM) by incorporating an image-to-graph generation paradigm. Specifically, we generate scene graph sequences via image-to-text generation with VLM and then construct scene graphs from these sequences. By doing so, we harness the strong capabilities of VLM for open-vocabulary SGG and seamlessly integrate explicit relational modeling for enhancing the VL tasks. Experimental results demonstrate that our design not only achieves superior performance with an open vocabulary but also enhances downstream vision-language task performance through explicit relation modeling knowledge.

关键词： Scene Graph Generation Scene Understanding vision-language

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Rethinking Visual Geo-localization for Large-Scale Applications

Rethinking Visual Geo-localization for Large-Scale Applicati...

引用

IEEE/CVF conference on computer vision and pattern recognition (CVPR)

作者： Berton, Gabriele Masone, Carlo Caputo, Barbara Politecn Torino Turin Italy CINI Turin Italy

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Visual Geo-localization (VG) is the task of estimating the position where a given photo was taken by comparing it with a large database of images of known locations. To investigate how existing techniques would perform on a real-world city-wide VG application, we build San Francisco eXtra Large, a new dataset covering a whole city and providing a wide range of challenging cases, with a size 30x bigger than the previous largest dataset for visual geo-localization. We find that current methods fail to scale to such large datasets, therefore we design a new highly scalable training technique, called CosPlace, which casts the training as a classification problem avoiding the expensive mining needed by the commonly used contrastive learning. We achieve state-of-the-art performance on a wide range of datasets and find that CosPlace is robust to heavy domain changes. Moreover, we show that, compared to the previous state-of-the-art, CosPlace requires roughly 80% less GPU memory at train time, and it achieves better results with 8x smaller descriptors, paving the way for city-wide real-world visual geo-localization. Dataset, code and trained models are available for research purposes at https://***/gmberton/CosPlace.

关键词： Training Visualization computer vision Codes Urban areas Memory management Graphics processing units

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Learning to Predict Activity Progress by Self-Supervised Video Alignment

Learning to Predict Activity Progress by Self-Supervised Vid...

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IEEE/CVF conference on computer vision and pattern recognition (CVPR)

作者： Donahue, Gerard Elhamifar, Ehsan Northwestern Univ Boston MA 02115 USA

ISBN: (纸本)9798350353006

In this paper, we tackle the problem of self-supervised video alignment and activity progress prediction using in-the-wild videos. Our proposed self-supervised representation learning method carefully addresses different action orderings, redundant actions, and background frames to generate improved video representations compared to previous methods. Our model generalizes temporal cycleconsistency learning to allow for more flexibility in determining cycle-consistent neighbors. More specifically, to handle repeated actions, we propose a multi-neighbor cycle consistency and a multi-cycle-back regression loss by finding multiple soft nearest neighbors using a Gaussian Mixture Model. To handle background and redundant frames, we introduce a context-dependent drop function in our framework, discouraging the alignment of droppable frames. On the other hand, to learn from videos of multiple activities jointly, we propose a multi-head crosstask network, allowing us to embed a video and estimate progress without knowing its activity label. Experiments on multiple datasets show that our method outperforms the state-of-the-art for video alignment and progress prediction. (1)

关键词： computer vision in the wild procedural learning progress progress prediction representation learning self-supervised self-supervised representation learning unconstrained unconstrained videos Video Alignment video understanding

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Multi-label Classification with Partial Annotations using Class-aware Selective Loss

Multi-label Classification with Partial Annotations using Cl...

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IEEE/CVF conference on computer vision and pattern recognition (CVPR)

作者： Ben-Baruch, Emanuel Ridnik, Tal Friedman, Itamar Ben-Cohen, Avi Zamir, Nadav Noy, Asaf Zelnik-Manor, Lihi Alibaba Grp DAMO Acad Hangzhou Peoples R China

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Large-scale multi-label classification datasets are commonly, and perhaps inevitably, partially annotated. That is, only a small subset of labels are annotated per sample. Different methods for handling the missing labels induce different properties on the model and impact its accuracy. In this work, we analyze the partial labeling problem, then propose a solution based on two key ideas. First, un-annotated labels should be treated selectively according to two probability quantities: the class distribution in the overall dataset and the specific label likelihood for a given data sample. We propose to estimate the class distribution using a dedicated temporary model, and we show its improved efficiency over a naive estimation computed using the dataset's partial annotations. Second, during the training of the target model, we emphasize the contribution of annotated labels over originally un-annotated labels by using a dedicated asymmetric loss. With our novel approach, we achieve state-of-the-art results on OpenImages dataset (e.g. reaching 87.3 mAP on V6). In addition, experiments conducted on LVIS and simulated-COCO demonstrate the effectiveness of our approach. Code is available at https://***/Alibaba-MIIL/PartialLabelingCSL.

关键词： Training Deep learning computer vision Codes Annotations Computational modeling Estimation

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A Unified Query-based Paradigm for Point Cloud Understanding

A Unified Query-based Paradigm for Point Cloud Understanding

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IEEE/CVF conference on computer vision and pattern recognition (CVPR)

作者： Yang, Zetong Jiang, Li Sun, Yanan Schiele, Bernt Jia, Jiaya CUHK Hong Kong Peoples R China MPI Informat Saarbrucken Germany HKUST Hong Kong Peoples R China SmartMore Hong Kong Peoples R China

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

3D point cloud understanding is an important component in autonomous driving and robotics. In this paper, we present a novel Embedding-Querying paradigm (EQ-Paradigm) for 3D understanding tasks including detection, segmentation and classification. EQ-Paradigm is a unified paradigm that enables combination of existing 3D backbone architectures with different task heads. Under the EQ-Paradigm, the input is first encoded in the embedding stage with an arbitrary feature extraction architecture, which is independent of tasks and heads. Then, the querying stage enables the encoded features for diverse task heads. This is achieved by introducing an intermediate representation, i.e., Q-representation, in the querying stage to bridge the embedding stage and task heads. We design a novel Q-Net as the querying stage network. Extensive experimental results on various 3D tasks show that EQ-Paradigm in tandem with Q-Net is a general and effective pipeline, which enables flexible collaboration of backbones and heads. It further boosts performance of state-of-the-art methods.

关键词： Point cloud compression Three-dimensional displays Shape Pipelines Semantics computer architecture Feature extraction

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Data-Free Model Extraction

Data-Free Model Extraction

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IEEE/CVF conference on computer vision and pattern recognition (CVPR)

作者： Truong, Jean-Baptiste Maini, Pratyush Walls, Robert J. Papernot, Nicolas Worcester Polytech Inst Worcester MA 01609 USA Indian Inst Technol Delhi Delhi India Univ Toronto Toronto ON Canada Vector Inst Toronto ON Canada

ISBN: (纸本)9781665445092

Current model extraction attacks assume that the adversary has access to a surrogate dataset with characteristics similar to the proprietary data used to train the victim model. This requirement precludes the use of existing model extraction techniques on valuable models, such as those trained on rare or hard to acquire datasets. In contrast, we propose data free model extraction methods that do not require a surrogate dataset. Our approach adapts techniques from the area of data free knowledge transfer for model extraction. As part of our study, we identify that the choice of loss is critical to ensuring that the extracted model is an accurate replica of the victim model. Furthermore, we address difficulties arising from the adversary's limited access to the victim model in a black-box setting. For example, we recover the model's logits from its probability predictions to approximate gradients. We find that the proposed data free model extraction approach achieves high-accuracy with reasonable query complexity - 0.99x and 0.92 x the victim model accuracy on SVHN and CIFAR-10 datasets given 2M and 20M queries respectively.

关键词： Adaptation models computer vision Computational modeling Intellectual property Predictive models Data models Complexity theory

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Perturbing Attention Gives You More Bang for the Buck: Subtle Imaging Perturbations That Efficiently Fool Customized Diffusion Models

Perturbing Attention Gives You More Bang for the Buck: Subtl...

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IEEE/CVF conference on computer vision and pattern recognition (CVPR)

作者： Xu, Jingyao Lu, Yuetong Li, Yandong Lu, Siyang Wang, Dongdong Wei, Xiang Beijing Jiaotong Univ Beijing Peoples R China Google Res Mountain View CA USA Univ Cent Florida Orlando FL 32816 USA

ISBN: (纸本)9798350353006

Diffusion models ( DMs) embark a new era of generative modeling and offer more opportunities for efficient generating high- quality and realistic data samples. However, their widespread use has also brought forth new challenges in model security, which motivates the creation of more effective adversarial attackers on DMs to understand its vulnerability. We propose CAAT, a simple but generic and efficient approach that does not require costly training to effectively fool latent diffusion models (LDMs). The approach is based on the observation that cross-attention layers exhibits higher sensitivity to gradient change, allowing for leveraging subtle perturbations on published images to significantly corrupt the generated images. We show that a subtle perturbation on an image can significantly impact the cross-attention layers, thus changing the mapping between text and image during the fine-tuning of customized diffusion models. Extensive experiments demonstrate that CAAT is compatible with diverse diffusion models and out- performs baseline attack methods in a more effective (more noise) and efficient (twice as fast as Anti-DreamBooth and Mist) manner.

关键词： Adversarial Attack computer vision

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Background Activation Suppression for Weakly Supervised Object Localization

Background Activation Suppression for Weakly Supervised Obje...

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IEEE/CVF conference on computer vision and pattern recognition (CVPR)

作者： Wu, Pingyu Zhai, Wei Cao, Yang Univ Sci & Technol China Hefei Peoples R China Hefei Comprehens Natl Sci Ctr Inst Artificial Intelligence Hefei Peoples R China

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Weakly supervised object localization (WSOL) aims to localize objects using only image-level labels. Recently a new paradigm has emerged by generating a foreground prediction map (FPM) to achieve localization task. Existing FPM-based methods use cross-entropy (CE) to evaluate the foreground prediction map and to guide the learning of generator. We argue for using activation value to achieve more efficient learning. It is based on the experimental observation that, for a trained network, CE converges to zero when the foreground mask covers only part of the object region. While activation value increases until the mask expands to the object boundary, which indicates that more object areas can be learned by using activation value. In this paper;we propose a Background Activation Suppression (BAS) method. Specifically, an Activation Map Constraint module (AMC) is designed to facilitate the learning of generator by suppressing the background activation value. Meanwhile, by using the foreground region guidance and the area constraint, BAS can learn the whole region of the object. In the inference phase, we consider the prediction maps of different categories together to obtain the final localization results. Extensive experiments show that BAS achieves significant and consistent improvement over the baseline methods on the CUB-200-2011 and ILSVRC datasets.

关键词： Location awareness computer vision Correlation Codes Generators pattern recognition Task analysis

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