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

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Florence-2: Advancing a Unified Representation for a Variety of vision Tasks

Florence-2: Advancing a Unified Representation for a Variety...

引用

conference on computer vision and pattern recognition (CVPR)

作者： Bin Xiao Haiping Wu Weijian Xu Xiyang Dai Houdong Hu Yumao Lu Michael Zeng Ce Liu Lu Yuan Microsoft

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

We introduce Florence-2, a novel vision foundation model with a unified, prompt-based representation for various computer vision and vision-language tasks. While existing large vision models excel in transfer learning, they struggle to perform diverse tasks with simple instructions, a capability that implies handling the complexity of various spatial hierarchy and semantic granularity. Florence-2 was designed to take text-prompt as task instructions and generate desirable results in text forms, whether it be captioning, object detection, grounding or segmentation. This multi-task learning setup demands large-scale, high-quality annotated data. To this end, we co-developed FLD-5B that consists of 5.4 billion comprehensive visual annotations on 126 million images, using an iterative strategy of automated image annotation and model refinement. We adopted a sequence-to-sequence structure to train Florence-2 to perform versatile and comprehensive vision tasks. Extensive evaluations on numerous tasks demonstrated Florence-2 to be a strong vision foundation model contender with un-precedented zero-shot and fine-tuning capabilities.

关键词： Visualization computer vision Grounding Annotations Semantics Transfer learning Object detection

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PupilTAN: A Few-Shot Adversarial Pupil Localizer

PupilTAN: A Few-Shot Adversarial Pupil Localizer

引用

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

作者： Poulopoulos, Nikolaos Psarakis, Emmanouil Z. Kosmopoulos, Dimitrios Univ Patras Comp Engn & Informat Patras Greece

ISBN: (纸本)9781665448994

The eye center localization is a challenging problem faced by many computer vision applications. The challenges typically stem from the scene variability, such as, the wide range of shapes, the lighting conditions, the view angles and the occlusions. Nowadays, the increasing interest on deep neural networks requires a large volume of training data. However, a significant issue is the dependency on labeled data, which are expensive to obtain and susceptible to errors. To address these issues, we propose a deep network, dubbed PupilTAN, that performs image-to-heatmap Translation and an Adversarial training framework that solves the eye localization problem in a few-shot unsupervised way. The key idea is to estimate, by using only a few ground-truth shots, the heatmaps centers' pdf and use it as a generator to create random heatmaps that follow the same probability distribution of the real ones. We showcase that training the deep network with these artificial heatmaps in an adversarial framework not only makes us less dependent on labeled data, but also leads to a significant accuracy improvement. The proposed network achieves real-time performance in a general-purpose computer environment and improves the state-of-the-art accuracy for both MUCT and BioID datasets, even compared with supervised techniques. Furthermore, our model is robust even in the case of reducing its size of up to 1/16 of the original network (0.2M parameters), demonstrating comparable accuracy to the state-of-the-art with high practical value to real-time applications.

关键词： Location awareness Heating systems Training computer vision Shape Training data Real-time systems

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Honeybee: Locality-Enhanced Projector for Multimodal LLM

Honeybee: Locality-Enhanced Projector for Multimodal LLM

引用

conference on computer vision and pattern recognition (CVPR)

作者： Junbum Cha Wooyoung Kang Jonghwan Mun Byungseok Roh Kakao Brain

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

In Multimodal Large Language Models (MLLMs), a visual projector plays a crucial role in bridging pre-trained vision encoders with LLMs, enabling profound visual understanding while harnessing the LLMs' robust capabilities. Despite the importance of the visual projector, it has been relatively less explored. In this study, we first identify two essential projector properties: (i) flexibility in managing the number of visual tokens, crucial for MLLMs' over-all efficiency, and (ii) preservation of local context from visual features, vital for spatial understanding. Based on these findings, we propose a novel projector design that is both flexible and locality-enhanced, effectively satisfying the two desirable properties. Additionally, we present comprehensive strategies to effectively utilize multiple and multifaceted instruction datasets. Through extensive experiments, we examine the impact of individual design choices. Finally, our proposed MLLM, Honeybee, remarkably outperforms previous state-of-the-art methods across various benchmarks, including MME, MMBench, SEED-Bench, and LLaVA-Bench, achieving significantly higher efficiency. Code and models are available at https://***/kakaobrain/honeybee.

关键词： Visualization computer vision Codes Large language models Benchmark testing pattern recognition Tuning

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On the Robustness of Monte Carlo Dropout Trained with Noisy Labels

On the Robustness of Monte Carlo Dropout Trained with Noisy ...

引用

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

作者： Goel, Purvi Li Chen Facebook Menlo Pk CA 94025 USA

ISBN: (纸本)9781665448994

The memorization effect of deep learning hinders its performance to effectively generalize on test set when learning with noisy labels. Prior study has discovered that epistemic uncertainty techniques are robust when trained with noisy labels compared with neural networks without uncertainty estimation. They obtain prolonged memorization effect and better generalization performance under the adversarial setting of noisy labels. Due to its superior performance amongst other selected epistemic uncertainty methods under noisy labels, we focus on Monte Carlo Dropout (MCDropout) and investigate why it is robust when trained with noisy labels. Through empirical studies on datasets MNIST, CIFAR-10, Animal-10n, we deep dive into three aspects of MCDropout under noisy label setting: 1. efficacy: understanding the learning behavior and test accuracy of MCDropout when training set contains artificially generated or naturally embedded label noise;2. representation volatility: studying the responsiveness of neurons by examining the mean and standard deviation on each neuron's activation;3. network sparsity: investigating the network support of MCDropout in comparison with deterministic neural networks. Our findings suggest that MCDropout further sparsifies and regularizes the deterministic neural networks and thus provides higher robustness against noisy labels.

关键词： Training Uncertainty Monte Carlo methods Neurons Estimation Robustness pattern recognition

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Partition-Guided GANs

Partition-Guided GANs

引用

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

作者： Armandpour, Mohammadreza Sadeghian, Ali Li, Chunyuan Zhou, Mingyuan Texas A&M Univ College Stn TX 77843 USA Univ Florida Gainesville FL 32611 USA Microsoft Res Redmond WA USA Univ Texas Austin Austin TX 78712 USA

ISBN: (纸本)9781665445092

Despite the success of Generative Adversarial Networks (GANs), their training suffers from several well-known problems, including mode collapse and difficulties learning a disconnected set of manifolds. In this paper, we break down the challenging task of learning complex high dimensional distributions, supporting diverse data samples, to simpler sub-tasks. Our solution relies on designing a partitioner that breaks the space into smaller regions, each having a simpler distribution, and training a different generator for each partition. This is done in an unsupervised manner without requiring any labels. We formulate two desired criteria for the space partitioner that aid the training of our mixture of generators: 1) to produce connected partitions and 2) provide a proxy of distance between partitions and data samples, along with a direction for reducing that distance. These criteria are developed to avoid producing samples from places with non-existent data density, and also facilitate training by providing additional direction to the generators. We develop theoretical constraints for a space partitioner to satisfy the above criteria. Guided by our theoretical analysis, we design an effective neural architecture for the space partitioner that empirically assures these conditions. Experimental results on various standard benchmarks show that the proposed unsupervised model outperforms several recent methods.

关键词： Training Manifolds computer vision computer architecture Generative adversarial networks Generators pattern recognition

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FADES: Fair Disentanglement with Sensitive Relevance

FADES: Fair Disentanglement with Sensitive Relevance

引用

conference on computer vision and pattern recognition (CVPR)

作者： Taeuk Jang Xiaoqian Wang Purdue University West Lafayette IN USA

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

Learning fair representation in deep learning is essential to mitigate discriminatory outcomes and enhance trustworthiness. However, previous research has been commonly established on inappropriate assumptions prone to unrealistic counterfactuals and performance degradation. Although some proposed alternative approaches, such as employing correlation-aware causal graphs or proxies for mutual information, these methods are less practical and not applicable in general. In this work, we propose FAir DisEntanglement with Sensitive relevance (FADES), a novel approach that leverages conditional mutual information from the information theory perspective to address these challenges. We employ sensitive relevant code to direct correlated information between target labels and sensitive attributes by imposing conditional independence, allowing better separation of the features of interest in the latent space. Utilizing an intuitive disentangling approach, FADES consistently achieves superior performance and fairness both quantitatively and qualitatively with its straightforward structure. Specifically, the proposed method outperforms existing works in downstream classification and counterfactual generations on various benchmarks.

关键词： Training Degradation Deep learning computer vision Codes Benchmark testing pattern recognition

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StyleCineGAN: Landscape Cinemagraph Generation Using a Pre-trained StyleGAN

StyleCineGAN: Landscape Cinemagraph Generation Using a Pre-t...

引用

conference on computer vision and pattern recognition (CVPR)

作者： Jongwoo Choi Kwanggyoon Seo Amirsaman Ashtari Junyong Noh Visual Media Lab KAIST

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

We propose a method that can generate cinemagraphs automatically from a still landscape image using a pre-trained StyleGAN. Inspired by the success of recent un-conditional video generation, we leverage a powerful pre-trained image generator to synthesize high-quality cinema-graphs. Unlike previous approaches that mainly utilize the latent space of a pre-trained StyleGAN, our approach utilizes its deep feature space for both GAN inversion and cin-emagraph generation. Specifically, we propose multi-scale deep feature warping (MSDFW), which warps the intermediate features of a pre-trained StyleGAN at different resolutions. by using MSDFW, the generated cinemagraphs are of high resolution and exhibit plausible looping animation. We demonstrate the superiority of our method through user studies and quantitative comparisons with state-of-the-art cinemagraph generation methods and a video generation method that uses a pre-trained StyleGAN.

关键词： Training computer vision Semantics Animation Generators pattern recognition Spatial resolution

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Curriculum Graph Co-Teaching for Multi-Target Domain Adaptation

Curriculum Graph Co-Teaching for Multi-Target Domain Adaptat...

引用

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

作者： Roy, Subhankar Krivosheev, Evgeny Zhong, Zhun Sebe, Nicu Ricci, Elisa Univ Trento Trento TN Italy Fdn Bruno Kessler Povo TN Italy

ISBN: (纸本)9781665445092

In this paper we address multi-target domain adaptation (MTDA), where given one labeled source dataset and multiple unlabeled target datasets that differ in data distributions, the task is to learn a robust predictor for all the target domains. We identify two key aspects that can help to alleviate multiple domain-shifts in the MTDA: feature aggregation and curriculum learning. To this end, we propose Curriculum Graph Co-Teaching (CGCT) that uses a dual classifier head, with one of them being a graph convolutional network (GCN) which aggregates features from similar samples across the domains. To prevent the classifiers from over-fitting on its own noisy pseudo-labels we develop a co-teaching strategy with the dual classifier head that is assisted by curriculum learning to obtain more reliable pseudo-labels. Furthermore, when the domain labels are available, we propose Domain-aware Curriculum Learning (DCL), a sequential adaptation strategy that first adapts on the easier target domains, followed by the harder ones. We experimentally demonstrate the effectiveness of our proposed frameworks on several benchmarks and advance the state-of-the-art in the MTDA by large margins (e.g. +5.6% on the DomainNet).

关键词： Deep learning computer vision computer network reliability PROM Collaboration pattern recognition Reliability

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NTIRE 2021 Multi-modal Aerial View Object Classification Challenge

NTIRE 2021 Multi-modal Aerial View Object Classification Cha...

引用

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

作者： Liu, Jerrick Inkawhich, Nathan Nina, Oliver Timofte, Radu Duan, Yuru Li, Gongzhe Geng, Xueli Cai, Huanqia Air Force Res Lab Albuquerque NM 87117 USA Univ Illinois Urbana IL 61801 USA Duke Univ Durham NC 27706 USA Swiss Fed Inst Technol Comp Vis Lab Zurich Switzerland Northwestern Polytech Univ Changan Campus Xian Shaanxi Peoples R China Beihang Univ Beijing Peoples R China Xidian Univ Key Lab Intelligent Percept & Image Understanding Xian Peoples R China Dengzhuang South Rd Beijing Peoples R China

ISBN: (纸本)9781665448994

In this paper, we introduce the first Challenge on Multi-modal Aerial View Object Classification (MAVOC) in conjunction with the NTIRE 2021 workshop at CVPR. This challenge is composed of two different tracks using EO and SAR imagery. Both EO and SAR sensors possess different advantages and drawbacks. The purpose of this competition is to analyze how to use both sets of sensory information in complementary ways. We discuss the top methods submitted for this competition and evaluate their results on our blind test set. Our challenge results show significant improvement of more than 15% accuracy from our current baselines for each track of the competition.

关键词： Image sensors conferences Radar tracking Radar polarimetry Sensors pattern recognition Task analysis

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Encoding in Style: a StyleGAN Encoder for Image-to-Image Translation

Encoding in Style: a StyleGAN Encoder for Image-to-Image Tra...

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

作者： Richardson, Elad Alaluf, Yuval Patashnik, Or Nitzan, Yotam Azar, Yaniv Shapiro, Stav Cohen-Or, Daniel Penta AI Tel Aviv Israel Tel Aviv Univ Tel Aviv Israel

ISBN: (纸本)9781665445092

We present a generic image-to-image translation framework, pixel2style2pixel (pSp). Our pSp framework is based on a novel encoder network that directly generates a series of style vectors which are fed into a pretrained StyleGAN generator, forming the extended W+ latent space. We first show that our encoder can directly embed real images into W+, with no additional optimization. Next, we propose utilizing our encoder to directly solve image-to-image translation tasks, defining them as encoding problems from some input domain into the latent domain. By deviating from the standard "invert first, edit later" methodology used with previous StyleGAN encoders, our approach can handle a variety of tasks even when the input image is not represented in the StyleGAN domain. We show that solving translation tasks through StyleGAN significantly simplifies the training process, as no adversary is required, has better support for solving tasks without pixel-to-pixel correspondence, and inherently supports multi-modal synthesis via the resampling of styles. Finally, we demonstrate the potential of our framework on a variety of facial image-to-image translation tasks, even when compared to state-of-the-art solutions designed specifically for a single task, and further show that it can be extended beyond the human facial domain.

关键词： Training computer vision Image coding computer architecture Generators pattern recognition Decoding

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