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检索条件"任意字段=2016 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2016"

共 21008 条记录，以下是591-600 订阅

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Low-Rank Few-Shot Adaptation of vision-Language Models

Low-Rank Few-Shot Adaptation of Vision-Language Models

引用

ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Zanella, Maxime Ben Ayed, Ismail UCLouvain Louvain Belgium UMons Mons Belgium ETS Montreal Montreal PQ Canada

ISBN: (纸本)9798350365474

Recent progress in the few-shot adaptation of vision-Language Models (VLMs) has further pushed their generalization capabilities, at the expense of just a few labeled samples within the target downstream task. However, this promising, already quite abundant few-shot literature has focused principally on prompt learning and, to a lesser extent, on adapters, overlooking the recent advances in Parameter-Efficient Fine-Tuning (PEFT). Furthermore, existing few-shot learning methods for VLMs often rely on heavy training procedures and/or carefully chosen, taskspecific hyper-parameters, which might impede their applicability. In response, we introduce Low-Rank Adaptation (LoRA) in few-shot learning for VLMs, and show its potential on 11 datasets, in comparison to current state-ofthe-art prompt- and adapter-based approaches. Surprisingly, our simple CLIP-LoRA method exhibits substantial improvements, while reducing the training times and keeping the same hyper-parameters in all the target tasks, i.e., across all the datasets and numbers of shots. Certainly, our surprising results do not dismiss the potential of prompt-learning and adapter-based research. However, we believe that our strong baseline could be used to evaluate progress in these emergent subjects in few-shot VLMs.

关键词： adapter few-shot LoRA PEFT prompt vision-Language

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AVFormer: Injecting vision into Frozen Speech Models for Zero-Shot AV-ASR

AVFormer: Injecting Vision into Frozen Speech Models for Zer...

引用

ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Seo, Paul Hongsuck Nagrani, Arsha Schmid, Cordelia Google Res Mountain View CA 94043 USA

ISBN: (纸本)9798350301298

Audiovisual automatic speech recognition (AV-ASR) aims to improve the robustness of a speech recognition system by incorporating visual information. Training fully supervised multimodal models for this task from scratch, however is limited by the need for large labelled audiovisual datasets (in each downstream domain of interest). We present AVFormer, a simple method for augmenting audio-only models with visual information, at the same time performing lightweight domain adaptation. We do this by (i) injecting visual embeddings into a frozen ASR model using lightweight trainable adaptors. We show that these can be trained on a small amount of weakly labelled video data with minimum additional training time and parameters. (ii) We also introduce a simple curriculum scheme during training which we show is crucial to enable the model to jointly process audio and visual information effectively;and finally (iii) we show that our model achieves state of the art zero-shot results on three different AV-ASR benchmarks (How2, VisSpeech and Ego4D), while also crucially preserving decent performance on traditional audio-only speech recognition benchmarks (LibriSpeech). Qualitative results show that our model effectively leverages visual information for robust speech recognition.

关键词： Multi-modal learning

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Look, Listen, and Attack: Backdoor Attacks Against Video Action recognition

Look, Listen, and Attack: Backdoor Attacks Against Video Act...

引用

ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Hammoud, Hasan Abed Al Kader Liu, Shuming Alkhrashi, Mohammed AlBalawi, Fahad Ghanem, Bernard KAUST Thuwal Saudi Arabia SDAIA Riyadh Saudi Arabia Taif Univ Taif Saudi Arabia

ISBN: (纸本)9798350365474

Deep neural networks (DNNs) are vulnerable to a class of attacks called "backdoor attacks", which create an association between a backdoor trigger and a target label the attacker is interested in exploiting. A backdoored DNN performs well on clean test images, yet persistently predicts an attacker-defined label for any sample in the presence of the backdoor trigger. Although backdoor attacks have been extensively studied in the image domain, there are very few works that explore such attacks in the video domain, and they tend to conclude that image backdoor attacks are less effective in the video domain. In this work, we revisit the traditional backdoor threat model and incorporate additional video-related aspects to that model. We show that poisoned-label image backdoor attacks could be extended temporally in two ways, statically and dynamically, leading to highly effective attacks in the video domain. In addition, we explore natural video backdoors to highlight the seriousness of this vulnerability in the video domain. And, for the first time, we study multi-modal (audiovisual) backdoor attacks against video action recognition models, where we show that attacking a single modality is enough for achieving a high attack success rate.

关键词： action recognition adversarial attacks backdoor backdoor attacks video understanding

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MetaCLUE: Towards Comprehensive Visual Metaphors Research

MetaCLUE: Towards Comprehensive Visual Metaphors Research

引用

ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Akula, Arjun R. Driscoll, Brendan Narayana, Pradyumna Changpinyo, Soravit Jia, Zhiwei Damle, Suyash Pruthi, Garima Basu, Sugato Guibas, Leonidas Freeman, William T. Li, Yuanzhen Jampani, Varun Google Mountain View CA 94043 USA

ISBN: (纸本)9798350301298

Creativity is an indispensable part of human cognition and also an inherent part of how we make sense of the world. Metaphorical abstraction is fundamental in communicating creative ideas through nuanced relationships between abstract concepts such as feelings. While computer vision benchmarks and approaches predominantly focus on understanding and generating literal interpretations of images, metaphorical comprehension of images remains relatively unexplored. Towards this goal, we introduce MetaCLUE, a set of vision tasks on visual metaphor. We also collect high-quality and rich metaphor annotations (abstract objects, concepts, relationships along with their corresponding object boxes) as there do not exist any datasets that facilitate the evaluation of these tasks. We perform a comprehensive analysis of state-of-the-art models in vision and language based on our annotations, highlighting strengths and weaknesses of current approaches in visual metaphor classification, localization, understanding (retrieval, question answering, captioning) and generation (text-to-image synthesis) tasks. We hope this work provides a concrete step towards developing AI systems with human-like creative capabilities. Project page: https://***

关键词： and reasoning language vision

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A-CAP: Anticipation Captioning with Commonsense Knowledge

A-CAP: Anticipation Captioning with Commonsense Knowledge

引用

ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Duc Minh Vo Quoc-An Luong Sugimoto, Akihiro Nakayama, Hideki Univ Tokyo Tokyo Japan Grad Univ Adv Studies Hayama Kanagawa Japan Natl Inst Informat Tokyo Japan

ISBN: (纸本)9798350301298

Humans possess the capacity to reason about the future based on a sparse collection of visual cues acquired over time. In order to emulate this ability, we introduce a novel task called Anticipation Captioning, which generates a caption for an unseen oracle image using a sparsely temporally-ordered set of images. To tackle this new task, we propose a model called A-CAP, which incorporates commonsense knowledge into a pre-trained vision-language model, allowing it to anticipate the caption. Through both qualitative and quantitative evaluations on a customized visual storytelling dataset, A-CAP outperforms other image captioning methods and establishes a strong baseline for anticipation captioning. We also address the challenges inherent in this task.

关键词： and reasoning language vision

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Strategies to Improve Real-World Applicability of Laparoscopic Anatomy Segmentation Models

Strategies to Improve Real-World Applicability of Laparoscop...

引用

ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Kolbinger, Fiona R. He, Jiangpeng Ma, Jinge Zhu, Fengqing Purdue Univ W Lafayette IN 47907 USA

ISBN: (纸本)9798350365474

Accurate identification and localization of anatomical structures of varying size and appearance in laparoscopic imaging are necessary to leverage the potential of computer vision techniques for surgical decision support. Segmentation performance of such models is traditionally reported using metrics of overlap such as IoU. However, imbalanced and unrealistic representation of classes in the training data and suboptimal selection of reported metrics have the potential to skew nominal segmentation performance and thereby ultimately limit clinical translation. In this work, we systematically analyze the impact of class characteristics (i.e., organ size differences), training and test data composition (i.e., representation of positive and negative examples), and modeling parameters (i.e., foreground-to-background class weight) on eight segmentation metrics: accuracy, precision, recall, IoU, F1 score (Dice Similarity Coefficient), specificity, Hausdorff Distance, and Average Symmetric Surface Distance. Our findings support two adjustments to account for data biases in surgical data science: First, training on datasets that are similar to the clinical real-world scenarios in terms of class distribution, and second, class weight adjustments to optimize segmentation model performance with regard to metrics of particular relevance in the respective clinical setting.

关键词： Class Imbalance computer-Assisted Surgery Laparoscopic Surgery Semantic Segmentation Surgical Data Science

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vision Transformers Are Good Mask Auto-Labelers

Vision Transformers Are Good Mask Auto-Labelers

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ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Lan, Shiyi Yang, Xitong Yu, Zhiding Wu, Zuxuan Alvarez, Jose M. Anandkumar, Anima NVIDIA Santa Clara CA 95051 USA Meta AI FAIR London England Fudan Univ Shanghai Peoples R China CALTECH Pasadena CA USA

ISBN: (纸本)9798350301298

We propose Mask Auto-Labeler (MAL), a high-quality Transformer-based mask auto-labeling framework for instance segmentation using only box annotations. MAL takes box-cropped images as inputs and conditionally generates their mask pseudo-labels. We show that vision Transformers are good mask auto-labelers. Our method significantly reduces the gap between auto-labeling and human annotation regarding mask quality. Instance segmentation models trained using the MAL-generated masks can nearly match the performance of their fully-supervised counterparts, retaining up to 97.4% performance of fully supervised models. The best model achieves 44.1% mAP on COCO instance segmentation (test-dev 2017), outperforming state-of-the-art box-supervised methods by significant margins. Qualitative results indicate that masks produced by MAL are, in some cases, even better than human annotations.

关键词： grouping and shape analysis Segmentation

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SQUID: Deep Feature In-Painting for Unsupervised Anomaly Detection

SQUID: Deep Feature In-Painting for Unsupervised Anomaly Det...

引用

ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Xiang, Tiange Zhang, Yixiao Lu, Yongyi Yuille, Alan L. Zhang, Chaoyi Cai, Weidong Zhou, Zongwei Univ Sydney Camperdown NSW Australia Johns Hopkins Univ Baltimore MD USA

ISBN: (纸本)9798350301298

Radiography imaging protocols focus on particular body regions, therefore producing images of great similarity and yielding recurrent anatomical structures across patients. To exploit this structured information, we propose the use of Space-aware Memory Queues for In-painting and Detecting anomalies from radiography images (abbreviated as SQUID). We show that SQUID can taxonomize the ingrained anatomical structures into recurrent patterns;and in the inference, it can identify anomalies (unseen/modified patterns) in the image. SQUID surpasses 13 state-of-the-art methods in unsupervised anomaly detection by at least 5 points on two chest X-ray benchmark datasets measured by the Area Under the Curve (AUC). Additionally, we have created a new dataset (DigitAnatomy), which synthesizes the spatial correlation and consistent shape in chest anatomy. We hope DigitAnatomy can prompt the development, evaluation, and interpretability of anomaly detection methods.

关键词： cell microscopy Medical and biological vision

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KiUT: Knowledge-injected U-Transformer for Radiology Report Generation

KiUT: Knowledge-injected U-Transformer for Radiology Report ...

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ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Huang, Zhongzhen Zhang, Xiaofan Zhang, Shaoting Shanghai Jiao Tong Univ Shanghai Peoples R China Shanghai AI Lab Shanghai Peoples R China SenseTime Res Hong Kong Peoples R China

ISBN: (纸本)9798350301298

Radiology report generation aims to automatically generate a clinically accurate and coherent paragraph from the X-ray image, which could relieve radiologists from the heavy burden of report writing. Although various image caption methods have shown remarkable performance in the natural image field, generating accurate reports for medical images requires knowledge of multiple modalities, including vision, language, and medical terminology. We propose a Knowledge-injected U-Transformer (KiUT) to learn multi-level visual representation and adaptively dis-till the information with contextual and clinical knowledge for word prediction. In detail, a U-connection schema between the encoder and decoder is designed to model interactions between different modalities. And a symptom graph and an injected knowledge distiller are developed to assist the report generation. Experimentally, we outperform state-of-the-art methods on two widely used benchmark datasets: IU-Xray and MIMIC-CXR. Further experimental results prove the advantages of our architecture and the complementary benefits of the injected knowledge.

关键词： cell microscopy Medical and biological vision

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Spectral Bayesian Uncertainty for Image Super-resolution

Spectral Bayesian Uncertainty for Image Super-resolution

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ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Liu, Tao Cheng, Jun Tan, Shan Huazhong Univ Sci & Technol Wuhan Peoples R China

ISBN: (纸本)9798350301298

Recently deep learning techniques have significantly advanced image super-resolution (SR). Due to the black-box nature, quantifying reconstruction uncertainty is crucial when employing these deep SR networks. Previous approaches for SR uncertainty estimation mostly focus on capturing pixel-wise uncertainty in the spatial domain. SR uncertainty in the frequency domain which is highly related to image SR is seldom explored. In this paper, we propose to quantify spectral Bayesian uncertainty in image SR. To achieve this, a Dual-Domain Learning (DDL) framework is first proposed. Combined with Bayesian approaches, the DDL model is able to estimate spectral uncertainty accurately, enabling a reliability assessment for high frequencies reasoning from the frequency domain perspective. Extensive experiments under non-ideal premises are conducted and demonstrate the effectiveness of the proposed spectral uncertainty. Furthermore, we propose a novel Spectral Uncertainty based Decoupled Frequency (SUDF) training scheme for perceptual SR. Experimental results show the proposed SUDF can evidently boost perceptual quality of SR results without sacrificing much pixel accuracy.

关键词： Low-level vision

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