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

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GRAM: Global Reasoning for Multi-Page VQA

GRAM: Global Reasoning for Multi-Page VQA

引用

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

作者： Blau, Tsachi Fogel, Sharon Ronen, Roi Goltst, Alona Per, Shahar Tsi Ben Avraham, Elad Aberdam, Aviad Ganz, Roy Litman, Ron Technion Haifa Israel AWS AI Labs Shanghai Peoples R China

ISBN: (纸本)9798350353006

The increasing use of transformer-based large language models brings forward the challenge of processing long sequences. In document visual question answering (DocVQA), leading methods focus on the single-page setting, while documents can span hundreds of pages. We present GRAM, a method that seamlessly extends pre-trained single-page models to the multi-page setting, without requiring computationally-heavy pretraining. To do so, we leverage a single-page encoder for local page-level understanding, and enhance it with document-level designated layers and learnable tokens, facilitating the flow of information across pages for global reasoning. To enforce our model to utilize the newly introduced document tokens, we propose a tailored bias adaptation method. For additional computational savings during decoding, we introduce an optional compression stage using our compression-transformer(C-Former), reducing the encoded sequence length, thereby allowing a tradeoff between quality and latency. Extensive experiments showcase GRAM's state-of-the-art performance on the benchmarks for multi-page DocVQA, demonstrating the effectiveness of our approach.

关键词： Document Understanding Long Sequence Processing vision Language Models

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Deep Quantization: Encoding Convolutional Activations with Deep Generative Model 30

Deep Quantization: Encoding Convolutional Activations with D...

引用

30th IEEE/CVF conference on computer vision and pattern recognition (CVPR)

作者： Qiu, Zhaofan Yao, Ting Mei, Tao Univ Sci & Technol China Hefei Anhui Peoples R China Microsoft Res Beijing Peoples R China

ISBN: (纸本)9781538604571

Deep convolutional neural networks (CNNs) have proven highly effective for visual recognition, where learning a universal representation from activations of convolutional layer plays a fundamental problem. In this paper, we present Fisher Vector encoding with Variational Auto-Encoder (FV-VAE), a novel deep architecture that quantizes the local activations of convolutional layer in a deep generative model, by training them in an end-to-end manner. To incorporate FV encoding strategy into deep generative models, we introduce Variational Auto-Encoder model, which steers a variational inference and learning in a neural network which can be straightforwardly optimized using standard stochastic gradient method. Different from the FV characterized by conventional generative models (e.g., Gaussian Mixture Model) which parsimoniously fit a discrete mixture model to data distribution, the proposed FV-VAE is more flexible to represent the natural property of data for better generalization. Extensive experiments are conducted on three public datasets, i.e., UCF101, ActivityNet, and CUB-200-2011 in the context of video action recognition and fine-grained image classification, respectively. Superior results are reported when compared to state-of-theart representations. Most remarkably, our proposed FVVAE achieves to-date the best published accuracy of 94.2% on UCF101.

关键词： Visualization Encoding computer architecture Training Quantization (signal) Computational modeling Optimization computer Architecture Computational modeling Visualization Functional training Variational Inference Training coding Neural network data distribution Image classification Mixture models Quantization

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Discovering Visual patterns in Art Collections with Spatially-consistent Feature Learning 32

Discovering Visual Patterns in Art Collections with Spatiall...

引用

32nd IEEE/CVF conference on computer vision and pattern recognition (CVPR)

作者： Shen, Xi Efros, Alexei A. Aubry, Mathieu Ecole Ponts ParisTech UMR 8049 LIGM Marne La Vallee France Univ Calif Berkeley Berkeley CA 94720 USA

ISBN: (纸本)9781728132938

Our goal in this paper is to discover near duplicate patterns in large collections of artworks. This is harder than standard instance mining due to differences in the artistic media (oil, pastel, drawing, etc), and imperfections inherent in the copying process. Our key technical insight is to adapt a standard deep feature to this task by fine-tuning it on the specific art collection using self-supervised learning. More specifically, spatial consistency between neighbouring feature matches is used as supervisory fine-tuning signal. The adapted feature leads to more accurate style -invariant matching, and can he used with a standard discovery approach, based on geometric verification, to identify duplicate patterns in the dataset. The approach is evaluated on several different datasets and shows surprisingly good qualitative discovery results. For quantitative evaluation of the method, we annotated 273 near duplicate details in a dataset of 1587 artworks attributed to Jan Brueghel and his workshop. Beyond artworks, we also demonstrate improvement on localization on the 040n-15K photo dataset as well as on historical photograph localization on the Large Time Lags Location (LTLL) dataset.

关键词： Categorization Deep Learning Grouping and Shape recognition: Detection Representation Learning Retrieval Segmentation vision Applications and S

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CLIP-Event: Connecting Text and Images with Event Structures

CLIP-Event: Connecting Text and Images with Event Structures

引用

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

作者： Li, Manling Xu, Ruochen Wang, Shuohang Zhou, Luowei Lin, Xudong Zhu, Chenguang Zeng, Michael Ji, Heng Chang, Shih-Fu Univ Illinois Champaign IL 61820 USA Microsoft Res Redmond WA USA Columbia Univ New York NY 10027 USA Microsoft Redmond WA USA

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

vision-language (V+L) pretraining models have achieved great success in supporting multimedia applications by understanding the alignments between images and text. While existing vision-language pretraining models primarily focus on understanding objects in images or entities in text, they often ignore the alignment at the level of events and their argument structures. In this work, we propose a contrastive learning framework to enforce vision-language pretraining models to comprehend events and associated argument (participant) roles. To achieve this, we take advantage of text information extraction technologies to obtain event structural knowledge, and utilize multiple prompt functions to contrast difficult negative descriptions by manipulating event structures. We also design an event graph alignment loss based on optimal transport to capture event argument structures. In addition, we collect a large event-rich dataset (106,875 images) for pretraining, which provides a more challenging image retrieval benchmark to assess the understanding of complicated lengthy sentences(1). Experiments show that our zero-shot CLIP-Event outperforms the state-of-the-art supervised model in argument extraction on Multimedia Event Extraction, achieving more than 5% absolute F-score gain in event extraction, as well as significant improvements on a variety of downstream tasks under zero-shot settings.

关键词： computer vision Codes Computational modeling Image retrieval Benchmark testing Information retrieval pattern recognition

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MEDIC: Remove Model Backdoors via Importance Driven Cloning

MEDIC: Remove Model Backdoors via Importance Driven Cloning

引用

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

作者： Xu, Qiuling Tao, Guanhong Honorio, Jean Liu, Yingqi An, Shengwei Shen, Guangyu Cheng, Siyuan Zhang, Xiangyu Purdue Univ W Lafayette IN 47907 USA

ISBN: (纸本)9798350301298

We develop a novel method to remove injected backdoors in deep learning models. It works by cloning the benign behaviors of a trojaned model to a new model of the same structure. It trains the clone model from scratch on a very small subset of samples and aims to minimize a cloning loss that denotes the differences between the activations of important neurons across the two models. The set of important neurons varies for each input, depending on their magnitude of activations and their impact on the classification result. We theoretically show our method can better recover benign functions of the backdoor model. Meanwhile, we prove our method can be more effective in removing backdoors compared with fine-tuning. Our experiments show that our technique can effectively remove nine different types of backdoors with minor benign accuracy degradation, outperforming the state-of-the-art backdoor removal techniques that are based on fine-tuning, knowledge distillation, and neuron pruning.(1)

关键词： ethics in vision fairness*accountability privacy Transparency

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Adversarial Feature Augmentation for Unsupervised Domain Adaptation 31

Adversarial Feature Augmentation for Unsupervised Domain Ada...

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31st IEEE/CVF conference on computer vision and pattern recognition (CVPR)

作者： Volpi, Riccardo Morerio, Pietro Savarese, Silvio Murino, Vittorio Ist Italiano Tecnol Pattern Anal & Comp Vis Genoa Italy Stanford Univ Stanford Vis & Learning Lab Stanford CA 94305 USA Univ Verona Comp Sci Dept Verona Italy

ISBN: (纸本)9781538664209

Recent works showed that Generative Adversarial Networks (GANs) can be successfully applied in unsupervised domain adaptation, where, given a labeled source dataset and an unlabeled target dataset, the goal is to train powerful classifiers for the target samples. In particular, it was shown that a GAN objective function can be used to learn target features indistinguishable from the source ones. In this work, we extend this framework by (i) forcing the learned feature extractor to be domain-invariant, and (ii) training it through data augmentation in the feature space, namely performing feature augmentation. While data augmentation in the image space is a well established technique in deep learning, feature augmentation has not yet received the same level of attention. We accomplish it by means of a feature generator trained by playing the GAN minimax game against source features. Results show that both enforcing domain-invariance and performing feature augmentation lead to superior or comparable performance to state-of-the-art results in several unsupervised domain adaptation benchmarks.

关键词： Feature extraction Gallium nitride Training Games Generative adversarial networks Generators Neural networks

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Debiased Subjective Assessment of Real-World Image Enhancement

Debiased Subjective Assessment of Real-World Image Enhanceme...

引用

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

作者： Cao, Peibei Wang, Zhangyang Ma, Kede City Univ Hong Kong Hong Kong Peoples R China Univ Texas Austin Austin TX 78712 USA

ISBN: (纸本)9781665445092

In real-world image enhancement, it is often challenging (if not impossible) to acquire ground-truth data, preventing the adoption of distance metrics for objective quality assessment. As a result, one often resorts to subjective quality assessment, the most straightforward and reliable means of evaluating image enhancement. Conventional subjective testing requires manually pre-selecting a small set of visual examples, which may suffer from three sources of biases: 1) sampling bias due to the extremely sparse distribution of the selected samples in the image space;2) algorithmic bias due to potential overfitting the selected samples;3) subjective bias due to further potential cherry-picking test results. This eventually makes the field of real-world image enhancement more of an art than a science. Here we take steps towards debiasing conventional subjective assessment by automatically sampling a set of adaptive and diverse images for subsequent testing. This is achieved by casting sample selection into a joint maximization of the discrepancy between the enhancers and the diversity among the selected input images. Careful visual inspection on the resulting enhanced images provides a debiased ranking of the enhancement algorithms. We demonstrate our subjective assessment method using three popular and practically demanding image enhancement tasks: dehazing, super-resolution, and low-light enhancement.

关键词： Photography Measurement Visualization Machine vision Superresolution Quality assessment pattern recognition

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Ambiguous Medical Image Segmentation using Diffusion Models

Ambiguous Medical Image Segmentation using Diffusion Models

引用

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

作者： Rahman, Aimon Valanarasu, Jeya Maria Jose Hacihaliloglu, Ilker Patel, Vishal M. Johns Hopkins Univ Baltimore MD 21218 USA Univ British Columbia Vancouver BC Canada

ISBN: (纸本)9798350301298

Collective insights from a group of experts have always proven to outperform an individual's best diagnostic for clinical tasks. For the task of medical image segmentation, existing research on AI-based alternatives focuses more on developing models that can imitate the best individual rather than harnessing the power of expert groups. In this paper, we introduce a single diffusion model-based approach that produces multiple plausible outputs by learning a distribution over group insights. Our proposed model generates a distribution of segmentation masks by leveraging the inherent stochastic sampling process of diffusion using only minimal additional learning. We demonstrate on three different medical image modalities- CT, ultrasound, and MRI that our model is capable of producing several possible variants while capturing the frequencies of their occurrences. Comprehensive results show that our proposed approach outperforms existing state-of-the-art ambiguous segmentation networks in terms of accuracy while preserving naturally occurring variation. We also propose a new metric to evaluate the diversity as well as the accuracy of segmentation predictions that aligns with the interest of clinical practice of collective insights. Implementation code: https://***/aimansnigdha/Ambiguous-Medical-Image-Segmentation-using-Diffusion-Models.

关键词： cell microscopy Medical and biological vision

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Style Transformer for Image Inversion and Editing

Style Transformer for Image Inversion and Editing

引用

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

作者： Hu, Xueqi Huang, Qiusheng Shi, Zhengyi Li, Siyuan Gao, Changxin Sun, Li Li, Qingli East China Normal Univ Shanghai Key Lab Multidimens Informat Proc Shanghai Peoples R China East China Normal Univ Key Lab Adv Theory & Applicat Stat & Data Sci Shanghai Peoples R China Huazhong Univ Sci & Technol Wuhan Peoples R China

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Existing GAN inversion methods fail to provide latent codes for reliable reconstruction and flexible editing simultaneously. This paper presents a transformer-based image inversion and editing model for pretrained StyleGAN which is not only with less distortions, but also of high quality and flexibility for editing. The proposed model employs a CNN encoder to provide multi-scale image features as keys and values. Meanwhile it regards the style code to be determined for different layers of the generator as queries. It first initializes query tokens as learnable parameters and maps them into W+ space. Then the multi-stage alternate self-and cross-attention are utilized, updating queries with the purpose of inverting the input by the generator. Moreover, based on the inverted code, we investigate the referenceand label-based attribute editing through a pretrained latent classifier, and achieve flexible image-to-image translation with high quality results. Extensive experiments are carried out, showing better performances on both inversion and editing tasks within StyleGAN. Codes are available at https://***/sapphire497/style-transformer.

关键词： Deep learning computer vision Codes Face recognition Transformers Distortion Generators

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TBP-Former: Learning Temporal Bird's-Eye-View Pyramid for Joint Perception and Prediction in vision-Centric Autonomous Driving

TBP-Former: Learning Temporal Bird's-Eye-View Pyramid for Jo...

引用

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

作者： Fang, Shaoheng Wang, Zi Zhong, Yiqi Ge, Junhao Chen, Siheng Shanghai Jiao Tong Univ Cooperat Medianet Innovat Ctr Shanghai Peoples R China Univ Southern Calif Dept Comp Sci Los Angeles CA USA Shanghai AI Lab Shanghai Peoples R China

ISBN: (纸本)9798350301298

vision-centric joint perception and prediction (PnP) has become an emerging trend in autonomous driving research. It predicts the future states of the traffic participants in the surrounding environment from raw RGB images. However, it is still a critical challenge to synchronize features obtained at multiple camera views and timestamps due to inevitable geometric distortions and further exploit those spatial-temporal features. To address this issue, we propose a temporal bird's-eye-view pyramid transformer (TBP-Former) for vision-centric PnP, which includes two novel designs. First, a pose-synchronized BEV encoder is proposed to map raw image inputs with any camera pose at any time to a shared and synchronized BEV space for better spatial-temporal synchronization. Second, a spatial-temporal pyramid transformer is introduced to comprehensively extract multi-scale BEV features and predict future BEV states with the support of spatial priors. Extensive experiments on nuScenes dataset show that our proposed framework overall outperforms all state-of-the-art vision-based prediction methods. Code is available at: https://***/MediaBrain-SJTU/TBP-Former

关键词： Autonomous driving

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