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检索条件"任意字段=2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2024"

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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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Multiclass Confidence and Localization Calibration for Object Detection

Multiclass Confidence and Localization Calibration for Objec...

引用

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

作者： Pathiraja, Bimsara Gunawardhana, Malitha Khan, Muhammad Haris Mohamed Bin Zayed Univ Artificial Intelligence Abu Dhabi U Arab Emirates

ISBN: (纸本)9798350301298

Albeit achieving high predictive accuracy across many challenging computer vision problems, recent studies suggest that deep neural networks (DNNs) tend to make overconfident predictions, rendering them poorly calibrated. Most of the existing attempts for improving DNN calibration are limited to classification tasks and restricted to calibrating in-domain predictions. Surprisingly, very little to no attempts have been made in studying the calibration of object detection methods, which occupy a pivotal space in vision-based security-sensitive, and safety-critical applications. In this paper, we propose a new train-time technique for calibrating modern object detection methods. It is capable of jointly calibrating multiclass confidence and box localization by leveraging their predictive uncertainties. We perform extensive experiments on several in-domain and out-of-domain detection benchmarks. Results demonstrate that our proposed train-time calibration method consistently outperforms several baselines in reducing calibration error for both in-domain and out-of-domain predictions. Our code and models are available at https: //***/bimsarapathiraja/MCCL

关键词： vision applications and systems

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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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Scaling Language-Image Pre-training via Masking

Scaling Language-Image Pre-training via Masking

引用

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

作者： Li, Yanghao Fan, Haoqi Hu, Ronghang Feichtenhofert, Christoph He, Kaiming Meta AI FAIR New York NY 10023 USA

ISBN: (纸本)9798350301298

We present Fast Language-Image Pre-training (FLIP), a simple and more efficient method for training CLIP [52]. Our method randomly masks out and removes a large portion of image patches during training. Masking allows us to learn from more image-text pairs given the same wall-clock time and contrast more samples per iteration with similar memory footprint. It leads to a favorable trade-off between accuracy and training time. In our experiments on 400 million image-text pairs, FLIP improves both accuracy and speed over the no-masking baseline. On a large diversity of downstream tasks, FLIP dominantly outperforms the CLIP counterparts trained on the same data. Facilitated by the speedup, we explore the scaling behavior of increasing the model size, data size, or training length, and report encouraging results and comparisons. We hope that our work will foster future research on scaling vision-language learning.

关键词： and reasoning language vision

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Second Edition FRCSyn Challenge at cvpr 2024: Face recognition Challenge in the Era of Synthetic Data

Second Edition FRCSyn Challenge at CVPR 2024: Face Recogniti...

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

作者： DeAndres-Tame, Ivan Tolosana, Ruben Melzi, Pietro Vera-Rodriguez, Ruben Kim, Minchul Rathgeb, Christian Liu, Xiaoming Morales, Aythami Fierrez, Julian Ortega-Garcia, Javier Zhong, Zhizhou Huang, Yuge Mi, Yuxi Ding, Shouhong Zhou, Shuigeng He, Shuai Fu, Lingzhi Cong, Heng Zhang, Rongyu Xiao, Zhihong Smirnov, Evgeny Pimenov, Anton Grigorev, Aleksei Timoshenko, Denis Asfaw, Kaleb Mesfin Low, Cheng Yaw Liu, Hao Wang, Chuyi Zuo, Qing He, Zhixiang Shahreza, Hatef Otroshi George, Anjith Unnervik, Alexander Rahimi, Parsa Marcel, Ebastien Neto, Pedro C. Huber, Marco Kolf, Jan Niklas Damer, Naser Boutros, Fadi Cardoso, Jaime S. Sequeira, Ana F. Atzori, Andrea Fenu, Gianni Marras, Mirko Struc, Vitomir Yu, Jiang Li, Zhangjie Li, Jichun Zhao, Weisong Lei, Zhen Zhu, Xiangyu Zhang, Xiao-Yu Biesseck, Bernardo Vidal, Pedro Coelho, Luiz Granada, Roger Menotti, David Univ Autonoma Madrid Madrid Spain Michigan State Univ E Lansing MI 48824 USA Hsch Darmstadt Darmstadt Germany Fudan Univ Shanghai Peoples R China Tencent Youtu Lab Shanghai Peoples R China Netease Inc Interact Entertainment Grp Guangzhou Peoples R China ID R&D Inc New York NY USA Korea Adv Inst Sci & Technol Daejeon South Korea Inst for Basic Sci Korea Daejeon South Korea China Telecom AI Beijing Peoples R China Idiap Res Inst Martigny Switzerland Ecole Polytech Fed Lausanne Lausanne Switzerland Univ Lausanne Lausanne Switzerland INESC TEC Porto Portugal Univ Porto Porto Portugal Fraunhofer IGD Darmstadt Germany Univ Cagliari Cagliari Italy Univ Ljubljana Ljubljana Slovenia Samsung Elect China R&D Ctr Shenzhen Peoples R China Univ Sci & Technol Hefei Peoples R China Chinese Acad Sci IIE Beijing Peoples R China CASIA MAIS Shanghai Peoples R China Univ Fed Parana Curitiba Parana Brazil Fed Inst Mato Grosso Cuiaba Brazil Unico IdTech Sao Paulo Brazil

ISBN: (纸本)9798350365474

Synthetic data is gaining increasing relevance for training machine learning models. This is mainly motivated due to several factors such as the lack of real data and intra-class variability, time and errors produced in manual labeling, and in some cases privacy concerns, among others. This paper presents an overview of the 2(nd) edition of the Face recognition Challenge in the Era of Synthetic Data (FRCSyn) organized at cvpr 2024. FRCSyn aims to investigate the use of synthetic data in face recognition to address current technological limitations, including data privacy concerns, demographic biases, generalization to novel scenarios, and performance constraints in challenging situations such as aging, pose variations, and occlusions. Unlike the 1(st) edition, in which synthetic data from DCFace and GANDiffFace methods was only allowed to train face recognition systems, in this 2(nd) edition we propose new subtasks that allow participants to explore novel face generative methods. The outcomes of the 2(nd) FRCSyn Challenge, along with the proposed experimental protocol and benchmarking contribute significantly to the application of synthetic data to face recognition.

关键词： Benchmarking Biometrics recognition Demographic Bias Face recognition FRCSyn Generative AI Privacy Synthetic Data

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YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors

YOLOv7: Trainable bag-of-freebies sets new state-of-the-art ...

引用

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

作者： Wang, Chien-Yao Bochkovskiy, Alexey Liao, Hong-Yuan Mark Acad Sinica Inst Informat Sci Taipei Taiwan

ISBN: (纸本)9798350301298

Real-time object detection is one of the most important research topics in computer vision. As new approaches regarding architecture optimization and training optimization are continually being developed, we have found two research topics that have spawned when dealing with these latest state-of-the-art methods. To address the topics, we propose a trainable bag-of-freebies oriented solution. We combine the flexible and efficient training tools with the proposed architecture and the compound scaling method. YOLOv7 surpasses all known object detectors in both speed and accuracy in the range from 5 FPS to 120 FPS and has the highest accuracy 56.8% AP among all known real-time object detectors with 30 FPS or higher on GPU V100. Source code is released in https://***/WongKinYiu/yolov7.

关键词： detection recognition: Categorization retrieval

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How you feelin'? Learning Emotions and Mental States in Movie Scenes

How you feelin'? Learning Emotions and Mental States in Movi...

引用

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

作者： Srivastava, Dhruv Singh, Aditya Kumar Tapaswi, Makarand IIIT Hyderabad CVIT Hyderabad Telangana India

ISBN: (纸本)9798350301298

Movie story analysis requires understanding characters' emotions and mental states. Towards this goal, we formulate emotion understanding as predicting a diverse and multi-label set of emotions at the level of a movie scene and for each character. We propose EmoTx, a multimodal Transformer-based architecture that ingests videos, multiple characters, and dialog utterances to make joint predictions. By leveraging annotations from the MovieGraphs dataset [72], we aim to predict classic emotions (e.g. happy, angry) and other mental states (e.g. honest, helpful). We conduct experiments on the most frequently occurring 10 and 25 labels, and a mapping that clusters 181 labels to 26. Ablation studies and comparison against adapted state-of-the-art emotion recognition approaches shows the effectiveness of EmoTx. Analyzing EmoTx's self-attention scores reveals that expressive emotions often look at character tokens while other mental states rely on video and dialog cues.

关键词： language reasoning vision

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Adversarial Counterfactual Visual Explanations

Adversarial Counterfactual Visual Explanations

引用

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

作者： Jeanneret, Guillaume Simon, Loic Jurie, Frederic Univ Caen Normandie ENSICAEN CNRS Caen France

ISBN: (纸本)9798350301298

Counterfactual explanations and adversarial attacks have a related goal: flipping output labels with minimal perturbations regardless of their characteristics. Yet, adversarial attacks cannot be used directly in a counterfactual explanation perspective, as such perturbations are perceived as noise and not as actionable and understandable image modifications. Building on the robust learning literature, this paper proposes an elegant method to turn adversarial attacks into semantically meaningful perturbations, without modifying the classifiers to explain. The proposed approach hypothesizes that Denoising Diffusion Probabilistic Models are excellent regularizers for avoiding high-frequency and out-of-distribution perturbations when generating adversarial attacks. The paper's key idea is to build attacks through a diffusion model to polish them. This allows studying the target model regardless of its robustification level. Extensive experimentation shows the advantages of our counterfactual explanation approach over current State-of-the-Art in multiple testbeds.

关键词： Explainable computer vision

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DNeRV: Modeling Inherent Dynamics via Difference Neural Representation for Videos

DNeRV: Modeling Inherent Dynamics via Difference Neural Repr...

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

作者： Zhao, Qi Asif, M. Salman Ma, Zhan Nanjing Univ Nanjing Peoples R China Univ Calif Riverside CA USA

ISBN: (纸本)9798350301298

Existing implicit neural representation (INR) methods do not fully exploit spatiotemporal redundancies in videos. Index-based INRs ignore the content-specific spatial features and hybrid INRs ignore the contextual dependency on adjacent frames, leading to poor modeling capability for scenes with large motion or dynamics. We analyze this limitation from the perspective of function fitting and reveal the importance of frame difference. To use explicit motion information, we propose Difference Neural Representation for Videos (DNeRV), which consists of two streams for content and frame difference. We also introduce a collaborative content unit for effective feature fusion. We test DNeRV for video compression, inpainting, and interpolation. DNeRV achieves competitive results against the state-of-the-art neural compression approaches and outperforms existing implicit methods on downstream inpainting and interpolation for 960 x 1920 videos.

关键词： Low-level 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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