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Localized Shortcut Removal

Localized Shortcut Removal

引用

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

作者： Müller, Nicolas M. Jacobs, Jochen Williams, Jennifer Böttinger, Konstantin Fraunhofer AISEC Germany TU Munich Germany University of Southampton United Kingdom

ISBN: (纸本)9798350302493

Machine learning is a data-driven field, and the quality of the underlying datasets plays a crucial role in learning success. However, high performance on held-out test data does not necessarily indicate that a model generalizes or learns anything meaningful. This is often due to the existence of machine learning shortcuts - features in the data that are predictive but unrelated to the problem at hand. To address this issue for datasets where the shortcuts are smaller and more localized than true features, we propose a novel approach to detect and remove them. We use an adversarially trained lens to detect and eliminate highly predictive but semantically unconnected clues in images. In our experiments on both synthetic and real-world data, we show that our proposed approach reliably identifies and neutralizes such shortcuts without causing degradation of model performance on clean data. We believe that our approach can lead to more meaningful and generalizable machine learning models, especially in scenarios where the quality of the underlying datasets is crucial. © 2023 ieee.

关键词： computer vision

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Decentralized Learning with Multi-Headed Distillation

Decentralized Learning with Multi-Headed Distillation

引用

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

作者： Zhmogiov, Andrey Sandler, Mark Miller, Nolan Kristiansen, Gus Vladymyrov, Max Google AI 1600 Amphitheatre Pkwy Mountain View CA 94043 USA

ISBN: (纸本)9798350301298

Decentralized learning with private data is a central problem in machine learning. We propose a novel distillation-based decentralized learning technique that allows multiple agents with private non-iid data to learn from each other, without having to share their data, weights or weight updates. Our approach is communication efficient, utilizes an unlabeled public dataset and uses multiple auxiliary heads for each client, greatly improving training efficiency in the case of heterogeneous data. This approach allows individual models to preserve and enhance performance on their private tasks while also dramatically improving their performance on the global aggregated data distribution. We study the effects of data and model architecture heterogeneity and the impact of the underlying communication graph topology on learning efficiency and show that our agents can significantly improve their performance compared to learning in isolation.

关键词： Efficient and scalable vision

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Visual Programming: Compositional visual reasoning without training

Visual Programming: Compositional visual reasoning without t...

引用

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

作者： Gupta, Tanmay Kembhavi, Aniruddha PRIOR Allen Inst AI Seattle WA 98103 USA

ISBN: (纸本)9798350301298

We present VISPROG, a neuro-symbolic approach to solving complex and compositional visual tasks given natural language instructions. VISPROG avoids the need for any task-specific training. Instead, it uses the in-context learning ability of large language models to generate python-like modular programs, which are then executed to get both the solution and a comprehensive and interpretable rationale. Each line of the generated program may invoke one of several off-the-shelf computer vision models, image processing subroutines, or python functions to produce intermediate outputs that may be consumed by subsequent parts of the program. We demonstrate the flexibility of VISPROG on 4 diverse tasks - compositional visual question answering, zero-shot reasoning on image pairs, factual knowledge object tagging, and language-guided image editing. We believe neuro-symbolic approaches like VISPROG are an exciting avenue to easily and effectively expand the scope of AI systems to serve the long tail of complex tasks that people may wish to perform.

关键词： and reasoning language vision

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Plateau-reduced Differentiable Path Tracing

Plateau-reduced Differentiable Path Tracing

引用

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

作者： Fischer, Michael Ritschel, Tobias UCL London England

ISBN: (纸本)9798350301298

Current differentiable renderers provide light transport gradients with respect to arbitrary scene parameters. However, the mere existence of these gradients does not guarantee useful update steps in an optimization. Instead, inverse rendering might not converge due to inherent plateaus, i.e., regions of zero gradient, in the objective function. We propose to alleviate this by convolving the high-dimensional rendering function, that maps scene parameters to images, with an additional kernel that blurs the parameter space. We describe two Monte Carlo estimators to compute plateau-reduced gradients efficiently, i.e., with low variance, and show that these translate into net-gains in optimization error and runtime performance. Our approach is a straightforward extension to both black-box and differentiable renderers and enables optimization of problems with intricate light transport, such as caustics or global illumination, that existing differentiable renderers do not converge on. Our code is at ***/mfischerucl/prdpt.

关键词： vision + graphics

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LayoutDM: Discrete Diffusion Model for Controllable Layout Generation

LayoutDM: Discrete Diffusion Model for Controllable Layout G...

引用

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

作者： Inoue, Naoto Kikuchi, Kotaro Simo-Serra, Edgar Otani, Mayu Yamaguchi, Kota CyberAgent Tokyo Japan Waseda Univ Tokyo Japan

ISBN: (纸本)9798350301298

Controllable layout generation aims at synthesizing plausible arrangement of element bounding boxes with optional constraints, such as type or position of a specific element. In this work, we try to solve a broad range of layout generation tasks in a single model that is based on discrete state-space diffusion models. Our model, named LayoutDM, naturally handles the structured layout data in the discrete representation and learns to progressively infer a noiseless layout from the initial input, where we model the layout corruption process by modality-wise discrete diffusion. For conditional generation, we propose to inject layout constraints in the form of masking or logit adjustment during inference. We show in the experiments that our LayoutDM successfully generates high-quality layouts and outperforms both task-specific and task-agnostic baselines on several layout tasks.(1)

关键词： vision + graphics

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The multi-modal universe of fast-fashion: the Visuelle 2.0 benchmark

The multi-modal universe of fast-fashion: the Visuelle 2.0 b...

引用

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

作者： Skenderi, Geri Joppi, Christian Denitto, Matteo Scarpa, Berniero Cristani, Marco Univ Verona Verona Italy Humatics Srl Verona Italy Nuna Lie Srl Rome Italy

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

We present Visuelle 2.0, the first dataset useful for facing diverse prediction problems that a fast-fashion company has to manage routinely. Furthermore, we demonstrate how the use of computer vision is substantial in this scenario. Visuelle 2.0 contains data for 6 seasons / 5355 clothing products of Nuna Lie(1), a famous Italian company with hundreds of shops located in different areas within the country. In particular, we focus on a specific prediction problem, namely short-observation new product sale forecasting (SO-fore). SO-fore assumes that the season has started and a set of new products is on the shelves of the different stores. The goal is to forecast the sales for a particular horizon, given a short, available past (few weeks), since no earlier statistics are available. To be successful, SO-fore approaches should capture this short past and exploit other modalities or exogenous data. To these aims, Visuelle 2.0 is equipped with disaggregated data at the item-shop level and multi-modal information for each clothing item, allowing computer vision approaches to come into play. The main message that we deliver is that the use of image data with deep networks boosts performances obtained when using the time series in long-term forecasting scenarios, ameliorating the WAPE by 8.2% and the MAE by 7.7%. The dataset is available at : https://***/forecasting/visuelle.

关键词： computer vision conferences Clothing Time series analysis Companies Benchmark testing pattern recognition

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Visual Domain Bridge: A source-free domain adaptation for cross-domain few-shot learning

Visual Domain Bridge: A source-free domain adaptation for cr...

引用

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

作者： Yazdanpanah, Moslem Moradi, Parham Univ Kurdistan Erbil Iraq

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Due to the covariate shift, deep neural networks performance always degrades when applied to novel domains. In order to mitigate this problem, domain adaptation techniques require samples from target data during the feature extraction training, which is not always applicable in real-world scenarios. Batch Normalization is a known component of computer vision models, aiming at reducing the training-time covariate shift. However, facing distribution shift results in an internal state mismatch inside the Batch-Norm layers during the inference time. In favor of alleviating the induced mismatch, this paper proposes a source-free, lightweight and straightforward approach by introducing the "Visual Domain Bridge" concept reducing the BatchNorm's internal mismatch in the cross-domain settings. Compared to the other BatchNorm-based source-free domain adaptation techniques such as AdaBN and Prediction-BN, our method formed a new state-of-the-art cross-domain few-shot fine-tuning method neglecting extra augmentations;while improving the performance in near-domain settings too. The proposed method can integrate with other domain adaptation methods and enhance their performance requiring just a few lines of modification in the BatchNorm's implementation. Implementations are available in https://***/MosyMosy/VDB

关键词： Bridges Training Deep learning Visualization computer vision conferences Neural networks

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Optimising rPPG Signal Extraction by Exploiting Facial Surface Orientation

Optimising rPPG Signal Extraction by Exploiting Facial Surfa...

引用

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

作者： Wong, Kwan Long Chin, Jing Wei Chan, Tsz Tai Odinaev, Ismoil Suhartono, Kristian Kang Tianqu So, Richard H. Y. Hong Kong Univ Sci & Technol Hong Kong Peoples R China PanopticAI Ltd Hong Kong Peoples R China

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Remote photoplethysmography (rPPG) is a contactless method to measure human vital signs by detecting subtle skin color changes through a camera. Although many studies have used region of interest (ROI) selection tools to improve rPPG signal extraction, no study has investigated the influence of the ROI's surface orientation. We propose a novel 'angle map' representation of the face to study the effects of the surface orientation on the extracted rPPG signal. The angle map is generated by mapping each facial pixel to an angle of reflection (angle between the skin surface and the camera) calculated from the surface normal of the facial landmarks and the camera axis. Our results show that surface orientation significantly affects the correlation between the extracted rPPG signal and ground truth blood volume pulse (BVP). Regions with small angles of reflection contained stronger signals, which explains why areas near the cheeks and forehead are often chosen for rPPG signal extraction. Moreover, we applied a thresholding method to the angle map and demonstrated its potential for dynamic ROI selection, thereby optimising the rPPG signal extraction process.

关键词： computer vision Forehead Correlation Face recognition conferences Cameras Photoplethysmography

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Fourier Image Transformer

Fourier Image Transformer

引用

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

作者： Buchholz, Tim-Oliver Jug, Florian FMI Biomed Res Basel Switzerland Fdn Human Technopole Milan Italy

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Transformer architectures show spectacular performance on NLP tasks and have recently also been used for tasks such as image completion or image classification. Here we propose to use a sequential image representation, where each prefix of the complete sequence describes the whole image at reduced resolution. Using such Fourier Domain Encodings (FDEs), an auto-regressive image completion task is equivalent to predicting a higher resolution output given a low-resolution input. Additionally, we show that an encoder-decoder setup can be used to query arbitrary Fourier coefficients given a set of Fourier domain observations. We demonstrate the practicality of this approach in the context of computed tomography (CT) image reconstruction. In summary, we show that Fourier Image Transformer (FIT) can be used to solve relevant image analysis tasks in Fourier space, a domain inherently inaccessible to convolutional architectures.

关键词： Image resolution Image coding Computed tomography computer architecture Image representation Transformers pattern recognition

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Classification of Facial Expression In-the-Wild based on Ensemble of Multi-head Cross Attention Networks

Classification of Facial Expression In-the-Wild based on Ens...

引用

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

作者： Jeong, Jae Yeop Hong, Yeong-Gi Kim, Daun Jeong, Jin-Woo Jung, Yuchul Kim, Sang-Ho Seoul Natl Univ Sci & Technol Dept Data Sci Gongreung Ro 232 Seoul South Korea Kumoh Natl Inst Technol Daehak Ro 61 Gumi South Korea

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

How to build a system for robust classification and recognition of facial expressions has been one of the most important research issues for successful interactive computing applications. However, previous datasets and studies mainly focused on facial expression recognition in a controlled/lab setting, therefore, could hardly be generalized in a more practical and real-life environment. The Affective Behavior Analysis in-the-wild (ABAW) 2022 competition released a dataset consisting of various video clips of facial expressions in-the-wild. In this paper, we propose a method based on the ensemble of multi-head cross attention networks to address the facial expression classification task introduced in the ABAW 2022 competition. We built a uni-task approach for this task, achieving the average F1-score of 34.60 on the validation set and 33.77 on the test set, ranking second place on the final leaderboard.

关键词： Gold computer vision Face recognition conferences Estimation Multitasking Behavioral sciences

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