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Doubling down: sparse grounding with an additional, almost-matching caption for detection-oriented multimodal pretraining

Doubling down: sparse grounding with an additional, almost-m...

引用

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

作者： Nebbia, Giacomo Kovashka, Adriana Univ Pittsburgh Pittsburgh PA 15260 USA

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

A common paradigm in deep learning applications for computer vision is self-supervised pretraining followed by supervised fine-tuning on a target task. In the self-supervision step, a model is trained in a supervised fashion, but the source of supervision needs to be implicitly defined by the data. Image-caption alignment is often used as such a source of implicit supervision in multimodal pretraining, and grounding (i.e., matching word tokens with visual tokens) is one way to exploit it. We introduce a strategy to take advantage of an underexplored structure in image-caption datasets: the relationship between captions matched with different images but mentioning the same objects. Given an image-caption pair, we find an additional caption that mentions one of the objects the first caption mentions, and we impose a sparse grounding between the image and the second caption so that only a few word tokens are grounded in the image. Our goal is to learn a better feature representation for the objects mentioned by both captions, encouraging grounding between the additional caption and the image to focus on the common objects only. We report superior grounding performance when comparing our approach with a previously-published pretraining strategy, and we show the benefit of our proposed double-caption grounding on two downstream detection tasks: supervised detection and open-vocabulary detection.

关键词： Deep learning computer vision Visualization Grounding conferences Computational modeling Data models

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Doppelganger Saliency: Towards More Ethical Person Re-Identification

Doppelganger Saliency: Towards More Ethical Person Re-Identi...

引用

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

作者： RichardWebster, Brandon Hu, Brian Fieldhouse, Keith Hoogs, Anthony Kitware Inc 1712 Route 9Suite 300 Clifton Pk NY 12065 USA

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Modern surveillance systems have become increasingly dependent on artificial intelligence to provide actionable information for real-time decision making. A critical question relates to how these systems handle difficult ethical dilemmas, such as the re-identification of similar looking individuals. Potential misidentification of individuals can have severe negative consequences, as evidenced by recent headlines of individuals who were wrongly targeted for crimes they did not commit based on false matches. A computer vision-based saliency algorithm is proposed to help identify pixel-level differences in pairs of images containing visually similar individuals, which we term "doppelgangers." The computed saliency maps can alert human users of the presence of doppelg angers and provide important visual evidence to reduce the potential of false matches in these high-stakes situations. We show both qualitative and quantitative saliency results on doppelg angers found in a video-based person re-identification dataset (MARS) using three different state-of-the-art models. Our results suggest that this novel use of visual saliency can improve overall outcomes by helping human users in the person re-identification setting, while assuring the ethical and trusted operation of surveillance systems.

关键词： Ethics Visualization computer vision Surveillance conferences Computational modeling Decision making

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CAMS: CAnonicalized Manipulation Spaces for Category-Level Functional Hand-Object Manipulation Synthesis

CAMS: CAnonicalized Manipulation Spaces for Category-Level F...

引用

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

作者： Zheng, Juntian Zheng, Qingyuan Fang, Lixing Liu, Yun Yi, Li Tsinghua Univ IIIIS Beijing Peoples R China Shanghai Artificial Intelligence Lab Shanghai Peoples R China Shanghai Qi Zhi Inst Shanghai Peoples R China

ISBN: (纸本)9798350301298

In this work, we focus on a novel task of category-level functional hand-object manipulation synthesis covering both rigid and articulated object categories. Given an object geometry, an initial human hand pose as well as a sparse control sequence of object poses, our goal is to generate a physically reasonable hand-object manipulation sequence that performs like human beings. To address such a challenge, we first design CAnonicalized Manipulation Spaces (CAMS), a two-level space hierarchy that canonicalizes the hand poses in an object-centric and contact-centric view. Benefiting from the representation capability of CAMS, we then present a two-stage framework for synthesizing human-like manipulation animations. Our framework achieves state-of-the-art performance for both rigid and articulated categories with impressive visual effects. Codes and video results can be found at our project home-page: https://***/.

关键词： vision + graphics

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Exploring Robustness Connection between Artificial and Natural Adversarial Examples

Exploring Robustness Connection between Artificial and Natur...

引用

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

作者： Agarwal, Akshay Ratha, Nalini Vatsa, Mayank Singh, Richa Buffalo Buffalo NY 14260 USA IIT Jodhpur Jodhpur Rajasthan India

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Although recent deep neural network algorithm has shown tremendous success in several computer vision tasks, their vulnerability against minute adversarial perturbations has raised a serious concern. In the early days of crafting these adversarial examples, artificial noises are optimized through the network and added in the images to decrease the confidence of the classifiers against the true class. However, recent efforts are showcasing the presence of natural adversarial examples which can also be effectively used to fool the deep neural networks with high confidence. In this paper, for the first time, we have raised the question that whether there is any robustness connection between artificial and natural adversarial examples. The possible robustness connection between natural and artificial adversarial examples is studied in the form that whether an adversarial example detector trained on artificial examples can detect the natural adversarial examples. We have analyzed several deep neural networks for the possible detection of artificial and natural adversarial examples in seen and unseen settings to set up a robust connection. The extensive experimental results reveal several interesting insights to defend the deep classifiers whether vulnerable against natural or artificially perturbed examples. We believe these findings can pave a way for the development of unified resiliency because defense against one attack is not sufficient for real-world use cases.

关键词： Deep learning computer vision Neural networks Semantics Transformers Robustness Convolutional neural networks

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MPAF: Model Poisoning Attacks to Federated Learning based on Fake Clients

MPAF: Model Poisoning Attacks to Federated Learning based on...

引用

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

作者： Cao, Xiaoyu Gong, Neil Zhenqiang Duke Univ Durham NC 27706 USA

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Existing model poisoning attacks to federated learning assume that an attacker has access to a large fraction of compromised genuine clients. However, such assumption is not realistic in production federated learning systems that involve millions of clients. In this work, we propose the first Model Poisoning Attack based on Fake clients called MPAF. Specifically, we assume the attacker injects fake clients to a federated learning system and sends carefully crafted fake local model updates to the cloud server during training, such that the learnt global model has low accuracy for many indiscriminate test inputs. Towards this goal, our attack drags the global model towards an attacker-chosen base model that has low accuracy. Specifically, in each round of federated learning, the fake clients craft fake local model updates that point to the base model and scale them up to amplify their impact before sending them to the cloud server. Our experiments show that MPAF can significantly decrease the test accuracy of the global model, even if classical defenses and norm clipping are adopted, highlighting the need for more advanced defenses.

关键词： Training computer vision Computational modeling conferences Production Collaborative work pattern recognition

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ElasticFace: Elastic Margin Loss for Deep Face recognition

ElasticFace: Elastic Margin Loss for Deep Face Recognition

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

作者： Boutros, Fadi Damer, Naser Kirchbuchner, Florian Kuijper, Arjan Fraunhofer Inst Comp Graph Res IGD D-64283 Darmstadt Germany Tech Univ Darmstadt Dept Comp Sci D-64289 Germany Germany

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Learning discriminative face features plays a major role in building high-performing face recognition models. The recent state-of-the-art face recognition solutions proposed to incorporate a fixed penalty margin on commonly used classification loss function, softmax loss, in the normalized hypersphere to increase the discriminative power of face recognition models, by minimizing the intra-class variation and maximizing the inter-class variation. Marginal penalty softmax losses, such as ArcFace and CosFace, assume that the geodesic distance between and within the different identities can be equally learned using a fixed penalty margin. However, such a learning objective is not realistic for real data with inconsistent inter-and intra-class variation, which might limit the discriminative and generalizability of the face recognition model. In this paper, we relax the fixed penalty margin constrain by proposing elastic penalty margin loss (ElasticFace) that allows flexibility in the push for class separability. The main idea is to utilize random margin values drawn from a normal distribution in each training iteration. This aims at giving the decision boundary chances to extract and retract to allow space for flexible class separability learning. We demonstrate the superiority of our ElasticFace loss over ArcFace and CosFace losses, using the same geometric transformation, on a large set of mainstream benchmarks. From a wider perspective, our ElasticFace has advanced the state-of-the-art face recognition performance on seven out of nine mainstream benchmarks. All training codes, pre-trained models, training logs will be publicly released (1).

关键词： Training computer vision Codes Face recognition conferences Buildings Training data

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Inverse Rendering of Translucent Objects using Physical and Neural Renderers

Inverse Rendering of Translucent Objects using Physical and ...

引用

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

作者： Li, Chenhao Trung Thanh Ngo Nagahara, Hajime Osaka Univ Inst Databil Sci Suita Osaka Japan

ISBN: (纸本)9798350301298

In this work, we propose an inverse rendering model that estimates 3D shape, spatially-varying reflectance, homogeneous subsurface scattering parameters, and an environment illumination jointly from only a pair of captured images of a translucent object. In order to solve the ambiguity problem of inverse rendering, we use a physically-based renderer and a neural renderer for scene reconstruction and material editing. Because two renderers are differentiable, we can compute a reconstruction loss to assist parameter estimation. To enhance the supervision of the proposed neural renderer, we also propose an augmented loss. In addition, we use a flash and no-flash image pair as the input. To supervise the training, we constructed a large-scale synthetic dataset of translucent objects, which consists of 117K scenes. Qualitative and quantitative results on both synthetic and real-world datasets demonstrated the effectiveness of the proposed model. Code and Data are available at https://***/ligoudaner377/homo_translucent

关键词： vision + graphics

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3D Highlighter: Localizing Regions on 3D Shapes via Text Descriptions

3D Highlighter: Localizing Regions on 3D Shapes via Text Des...

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

作者： Decatur, Dale Lang, Itai Hanocka, Rana Univ Chicago Chicago IL 60637 USA

ISBN: (纸本)9798350301298

We present 3D Highlighter, a technique for localizing semantic regions on a mesh using text as input. A key feature of our system is the ability to interpret "out-of-domain" localizations. Our system demonstrates the ability to reason about where to place non-obviously related concepts on an input 3D shape, such as adding clothing to a bare 3D animal model. Our method contextualizes the text description using a neural field and colors the corresponding region of the shape using a probability-weighted blend. Our neural optimization is guided by a pre-trained CLIP encoder, which bypasses the need for any 3D datasets or 3D annotations. Thus, 3D Highlighter is highly flexible, general, and capable of producing localizations on a myriad of input shapes. Our code is publicly available at https: //***/threedle/3DHighlighter.

关键词： vision + graphics

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Logical Implications for Visual Question Answering Consistency

Logical Implications for Visual Question Answering Consisten...

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

作者： Tascon-Morales, Sergio Marquez-Neila, Pablo Sznitman, Raphael Univ Bern Bern Switzerland

ISBN: (纸本)9798350301298

Despite considerable recent progress in Visual Question Answering (VQA) models, inconsistent or contradictory answers continue to cast doubt on their true reasoning capabilities. However, most proposed methods use indirect strategies or strong assumptions on pairs of questions and answers to enforce model consistency. Instead, we propose a novel strategy intended to improve model performance by directly reducing logical inconsistencies. To do this, we introduce a new consistency loss term that can be used by a wide range of the VQA models and which relies on knowing the logical relation between pairs of questions and answers. While such information is typically not available in VQA datasets, we propose to infer these logical relations using a dedicated language model and use these in our proposed consistency loss function. We conduct extensive experiments on the VQA Introspect and DME datasets and show that our method brings improvements to state-of-the-art VQA models while being robust across different architectures and settings.

关键词： language reasoning vision

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Interactive and Explainable Region-guided Radiology Report Generation

Interactive and Explainable Region-guided Radiology Report G...

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

作者： Tanida, Tim Muller, Philip Kaissis, Georgios Rueckert, Daniel Tech Univ Munich Munich Germany Helmholtz Zentrum Munich Munich Germany Imperial Coll London London England

ISBN: (纸本)9798350301298

The automatic generation of radiology reports has the potential to assist radiologists in the time-consuming task of report writing. Existing methods generate the full report from image-level features, failing to explicitly focus on anatomical regions in the image. We propose a simple yet effective region-guided report generation model that detects anatomical regions and then describes individual, salient regions to form the final report. While previous methods generate reports without the possibility of human intervention and with limited explainability, our method opens up novel clinical use cases through additional interactive capabilities and introduces a high degree of transparency and explainability. Comprehensive experiments demonstrate our method's effectiveness in report generation, outperforming previous state-of-the-art models, and highlight its interactive capabilities. The code and checkpoints are available at https://***/ttanida/rgrg.

关键词： cell microscopy Medical and biological vision

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