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Difficulty Estimation with Action Scores for computer vision Tasks

Difficulty Estimation with Action Scores for Computer Vision...

引用

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

作者： Arriaga, Octavio Palacio, Sebastian Valdenegro-Toro, Matias Univ Bremen Bremen Germany German Res Ctr Artificial Intelligence Kaiserslautern Germany Univ Groningen Groningen Netherlands

ISBN: (纸本)9798350302493

As more machine learning models are now being applied in real world scenarios it has become crucial to evaluate their difficulties and biases. In this paper we present an unsupervised method for calculating a difficulty score based on the accumulated loss per epoch. Our proposed method does not require any modification to the model, neither any external supervision, and it can be easily applied to a wide range of machine learning tasks. We provide results for the tasks of image classification, image segmentation, and object detection. We compare our score against similar metrics and provide theoretical and empirical evidence of their difference. Furthermore, we show applications of our proposed score for detecting incorrect labels, and test for possible biases.

关键词： Object detection

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Quantifying Extrinsic Curvature in Neural Manifolds

Quantifying Extrinsic Curvature in Neural Manifolds

引用

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

作者： Acosta, Francisco Sanborn, Sophia Duc, Khanh Dao Madhav, Manu Miolane, Nina UC Santa Barbara Phys Santa Barbara CA 93106 USA UC Santa Barbara Elect & Comp Engn Santa Barbara CA USA UC Santa Barbara Math Santa Barbara CA USA UC Santa Barbara Santa Barbara CA USA

ISBN: (纸本)9798350302493

The neural manifold hypothesis postulates that the activity of a neural population forms a low-dimensional manifold whose structure reflects that of the encoded task variables. In this work, we combine topological deep generative models and extrinsic Riemannian geometry to introduce a novel approach for studying the structure of neural manifolds. This approach (i) computes an explicit parameterization of the manifolds and (ii) estimates their local extrinsic curvature-hence quantifying their shape within the neural state space. Importantly, we prove that our methodology is invariant with respect to transformations that do not bear meaningful neuroscience information, such as permutation of the order in which neurons are recorded. We show empirically that we correctly estimate the geometry of synthetic manifolds generated from smooth deformations of circles, spheres, and tori, using realistic noise levels. We additionally validate our methodology on simulated and real neural data, and show that we recover geometric structure known to exist in hippocampal place cells. We expect this approach to open new avenues of inquiry into geometric neural correlates of perception and behavior.

关键词： computer vision

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Learning unbiased classifiers from biased data with meta-learning

Learning unbiased classifiers from biased data with meta-lea...

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

作者： Ragonesi, Ruggero Morerio, Pietro Murino, Vittorio Ist Italiano Tecnol Pattern Anal & Comp Vis PAVIS Genoa Italy Univ Verona Dept Comp Sci Verona Italy

ISBN: (纸本)9798350302493

It is well known that large deep architectures are powerful models when adequately trained, but may exhibit undesirable behavior leading to confident incorrect predictions, even when evaluated on slightly different test examples. Test data characterized by distribution shifts (from training data distribution), outliers, and adversarial samples are among the types of data affected by this problem. This situation worsens whenever data are biased, meaning that predictions are mostly based on spurious correlations present in the data. Unfortunately, since such correlations occur in the most of data, a model is prevented from correctly generalizing the considered classes. In this work, we tackle this problem from a meta-learning perspective. Considering the dataset as composed of unknown biased and unbiased samples, we first identify these two subsets by a pseudo-labeling algorithm, even if coarsely. Subsequently, we apply a bi-level optimization algorithm in which, in the inner loop, we look for the best parameters guiding the training of the two subsets, while in the outer loop, we train the final model taking benefit from augmented data generated using Mixup. Properly tuning the contributions of biased and unbiased data, together with the regularization introduced by the mixed data has proved to be an effective training strategy to learn unbiased models, showing superior generalization capabilities. Experimental results on synthetically and realistically biased datasets surpass state-of-the-art performance, as compared to existing methods.

关键词： computer vision

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vision-language models for decoding provider attention during neonatal resuscitation

Vision-language models for decoding provider attention durin...

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

作者： Parodi, Felipe Matelsky, Jordan K. Regla-Vargas, Alejandra Foglia, Elizabeth E. Lim, Charis Weinberg, Danielle Kording, Konrad P. Herrick, Heidi M. Platt, Michael L. Univ Penn Dept Neurosci Philadelphia PA 19104 USA Univ Penn Dept Bioengn Philadelphia PA 19104 USA Univ Penn Dept Sociol Philadelphia PA 19104 USA Univ Penn Dept Mkt Philadelphia PA 19104 USA Univ Penn Dept Psychol 3815 Walnut St Philadelphia PA 19104 USA Univ Penn Dept Pediat Div Neonatol Perelman Sch Med Philadelphia PA 19104 USA Childrens Hosp Philadelphia Dept Pediat Div Neonatol Philadelphia PA 19104 USA Johns Hopkins Univ Appl Phys Lab Baltimore MD 21218 USA

ISBN: (纸本)9798350365474

Neonatal resuscitations demand an exceptional level of attentiveness from providers, who must process multiple streams of information simultaneously. Gaze strongly influences decision making;thus, understanding where a provider is looking during neonatal resuscitations could inform provider training, enhance real-time decision support, and improve the design of delivery rooms and neonatal intensive care units (NICUs). Current approaches to quantifying neonatal providers' gaze rely on manual coding or simulations, which limit scalability and utility. Here, we introduce an automated, real-time, deep learning approach capable of decoding provider gaze into semantic classes directly from first-person point-of-view videos recorded during live resuscitations. Combining state-of-the-art, real-time segmentation with vision-language models, our low-shot pipeline attains 91% classification accuracy in identifying gaze targets without training. Upon fine-tuning, the performance of our gaze-guided vision transformer exceeds 98% accuracy in semantic gaze analysis, approaching human-level precision. This system, capable of real-time inference, enables objective quantification of provider attention dynamics during live neonatal resuscitation. Our approach offers a scalable solution that seamlessly integrates with existing infrastructure for data-scarce gaze analysis, thereby offering new opportunities for understanding and refining clinical decision making.

关键词： Resuscitation

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Scene Graph Driven Text-Prompt Generation for Image Inpainting

Scene Graph Driven Text-Prompt Generation for Image Inpainti...

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

作者： Shukla, Tripti Maheshwari, Paridhi Singh, Rajhans Shukla, Ankita Kulkarni, Kuldeep Turaga, Pavan Adobe Res India San Jose CA 95110 USA Stanford Univ Stanford CA USA Arizona State Univ Tempe AZ USA

ISBN: (纸本)9798350302493

Scene editing methods are undergoing a revolution, driven by text-to-image synthesis methods. Applications in media content generation have benefited from a careful set of engineered text prompts, that have been arrived at by the artists by trial and error. There is a growing need to better model prompt generation, for it to be useful for a broad range of consumer-grade applications. We propose a novel method for text prompt generation for the explicit purpose of consumer-grade image inpainting, i.e. insertion of new objects into missing regions in an image. Our approach leverages existing inter-object relationships to generate plausible textual descriptions for the missing object, that can then be used with any text-to-image generator. Given an image and a location where a new object is to be inserted, our approach first converts the given image to an intermediate scene graph. Then, we use graph convolutional networks to 'expand' the scene graph by predicting the identity and relationships of the new object to be inserted, with respect to the existing objects in the scene. The output of the expanded scene graph is cast into a textual description, which is then processed by a text-to-image generator, conditioned on the given image, to produce the final inpainted image. We conduct extensive experiments on the Visual Genome dataset, and show through qualitative and quantitative metrics that our method is superior to other methods.

关键词： computer vision

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Training Strategies for vision Transformers for Object Detection

Training Strategies for Vision Transformers for Object Detec...

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

作者： Singh, Apoorv Motional Boston MA 02210 USA

ISBN: (纸本)9798350302493

vision-based Transformer have shown huge application in the perception module of autonomous driving in terms of predicting accurate 3D bounding boxes, owing to their strong capability in modeling long-range dependencies between the visual features. However Transformers, initially designed for language models, have mostly focused on the performance accuracy, and not so much on the inference-time budget. For a safety critical system like autonomous driving, real-time inference at the on-board compute is an absolute necessity. This keeps our object detection algorithm under a very tight run-time budget. In this paper, we evaluated a variety of strategies to optimize on the inference-time of vision transformers based object detection methods keeping a close-watch on any performance variations. Our chosen metric for these strategies is accuracy-runtime joint optimization. Moreover, for actual inference-time analysis we profile our strategies with float32 and float16 precision with TensorRT module. This is the most common format used by the industry for deployment of their Machine Learning networks on the edge devices. We showed that our strategies are able to improve inference-time by 63% at the cost of performance drop of mere 3% for our problem-statement defined in Sec. 3. These strategies brings down vision Transformers detectors [3, 15, 18, 19, 36] inference-time even less than traditional single-image based CNN detectors like FCOS [17, 25, 33]. We recommend practitioners use these techniques to deploy Transformers based hefty multi-view networks on a budge-constrained robotic platform.

关键词： Autonomous vehicles

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Spectral Transfer Guided Active Domain Adaptation For Thermal Imagery

Spectral Transfer Guided Active Domain Adaptation For Therma...

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

作者： Ustun, Berkcan Kaya, Ahmet Kagan Ayerden, Ezgi Cakir Altinel, Fazil Aselsan Inc Res Ctr Yenimahalle Turkiye Middle East Tech Univ Dept Elect & Elect Engn Ankara Turkiye

ISBN: (纸本)9798350302493

The exploitation of visible spectrum datasets has led deep networks to show remarkable success. However, real-world tasks include low-lighting conditions which arise performance bottlenecks for models trained on large-scale RGB image datasets. Thermal IR cameras are more robust against such conditions. Therefore, the usage of thermal imagery in real-world applications can be useful. Unsupervised domain adaptation (UDA) allows transferring information from a source domain to a fully unlabeled target domain. Despite substantial improvements in UDA, the performance gap between UDA and its supervised learning counterpart remains significant. By picking a small number of target samples to annotate and using them in training, active domain adaptation tries to mitigate this gap with minimum annotation expense. We propose an active domain adaptation method in order to examine the efficiency of combining the visible spectrum and thermal imagery modalities. When the domain gap is considerably large as in the visible-to-thermal task, we may conclude that the methods without explicit domain alignment cannot achieve their full potential. To this end, we propose a spectral transfer guided active domain adaptation method to select the most informative unlabeled target samples while aligning source and target domains. We used the large-scale visible spectrum dataset MS-COCO as the source domain and the thermal dataset FLIR ADAS as the target domain to present the results of our method. Extensive experimental evaluation demonstrates that our proposed method outperforms the state-of-the-art active domain adaptation methods. The code and models are publicly available.(1)

关键词： computer vision

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KBody: Balanced monocular whole-body estimation

KBody: Balanced monocular whole-body estimation

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2023 ieee/CVF conference on computer vision and pattern recognition workshops, cvprw 2023

作者： Zioulis, Nikolaos O'Brien, James F. Klothed Technologies Inc. United States Uc Berkeley United States

ISBN: (纸本)9798350302493

KBody is a method for fitting a low-dimensional body model to an image. It follows a predict-and-optimize approach, relying on data-driven model estimates for the constraints that will be used to solve for the body's parameters. Compared to other approaches, it introduces virtual joints to identify higher quality correspondences and disentangles the optimization between the pose and shape parameters to achieve a more balanced result in terms of pose and shape capturing capacity, as well as pixel alignment. © 2023 ieee.

关键词： computer vision

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vision + Language Applications: A Survey

Vision + Language Applications: A Survey

引用

2023 ieee/CVF conference on computer vision and pattern recognition workshops, cvprw 2023

作者： Zhou, Yutong Shimada, Nobutaka Ritsumeikan University Shiga Japan

ISBN: (纸本)9798350302493

Text-to-image generation has attracted significant interest from researchers and practitioners in recent years due to its widespread and diverse applications across various industries. Despite the progress made in the domain of vision and language research, the existing literature remains relatively limited, particularly with regard to advancements and applications in this field. This paper explores a relevant research track within multimodal applications, including text, vision, audio, and others. In addition to the studies discussed in this paper, we are also committed to continually updating the latest relevant papers, datasets, application projects and corresponding information at https://***/Yutong-Zhou-cv/Awesome-Text-to-Image. © 2023 ieee.

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Deep Prototypical-Parts Ease Morphological Kidney Stone Identification and are Competitively Robust to Photometric Perturbations

Deep Prototypical-Parts Ease Morphological Kidney Stone Iden...

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

作者： Flores-Araiza, Daniel Lopez-Tiro, Francisco El-Beze, Jonathan Hubert, Jacques Gonzalez, Miguel Ruiz, Gilberto Ochoa Daul, Christian Tecnol Monterrey Sch Engn Mexico City DF Mexico CHU Nancy Serv Urol Brabois Nancy France Univ Lorraine CRAN UMR 7039 Nancy France

ISBN: (纸本)9798350302493

Identifying the type of kidney stones can allow urologists to determine their cause of formation, improving the prescription of appropriate treatments to diminish future relapses. Currently, the associated ex-vivo diagnosis (known as Morpho-constitutional Analysis, MCA) is time-consuming, expensive and requires a great deal of experience, as it requires a visual analysis component that is highly operator dependant. Recently, machine learning methods have been developed for in-vivo endoscopic stone recognition. Deep Learning (DL) based methods outperform non-DL methods in terms of accuracy but lack explainability. Despite this trade-off, when it comes to making high-stakes decisions, its important to prioritize understandable computer-Aided Diagnosis (CADx) that suggests a course of action based on reasonable evidence, rather than a model prescribing a course of action. In this proposal, we learn Prototypical Parts (PPs) per kidney stone subtype, which are used by the DL model to generate an output classification. Using PPs in the classification task enables case-based reasoning explanations for such output, thus making the model interpretable. In addition, we modify global visual characteristics to describe their relevance to the PPs and the sensitivity of our models performance. With this, we provide explanations with additional information at the sample, class and model levels in contrast to previous works. Although our implementations average accuracy is lower than state-of-the-art (SOTA) non-interpretable DL models by 1.5%, our models perform 2.8% better on perturbed images with a lower standard deviation, without adversarial training. Thus, Learning PPs has the potential to create more robust DL models. Code at: https://***/DanielF29/Prototipical_Parts

关键词： computer aided diagnosis

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