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Situational Awareness Matters in 3D vision Language Reasoning

Situational Awareness Matters in 3D Vision Language Reasonin...

引用

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

作者： Man, Yunze Gui, Liang-Yan Wang, Yu-Xiong Univ Illinois Urbana IL 61801 USA

ISBN: (纸本)9798350353006

Being able to carry out complicated vision language reasoning tasks in 3D space represents a significant milestone in developing household robots and human-centered embodied AI. In this work, we demonstrate that a critical and distinct challenge in 3D vision language reasoning is the situational awareness, which incorporates two key components: (1) The autonomous agent grounds its self-location based on a language prompt. (2) The agent answers open-ended questions from the perspective of its calculated position. To address this challenge, we introduce SIG3D, an end-to-end Situation-Grounded model for 3D vision language reasoning. We tokenize the 3D scene into sparse voxel representation, and propose a language-grounded situation estimator, followed by a situated question answering module. Experiments on the SQA3D and ScanQA datasets show that SIG3D outperforms state-of-the-art models in situational estimation and question answering by a large margin (e.g., an enhancement of over 30% on situation accuracy). Subsequent analysis corroborates our architectural design choices, explores the distinct functions of visual and textual tokens, and highlights the importance of situational awareness in the domain of 3D question-answering. Project page is available at https://***/situation3d.

关键词： vision-Language Multi-modal 3D Reasoning

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Guiding Attention using Partial-Order Relationships for Image Captioning

Guiding Attention using Partial-Order Relationships for Imag...

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

作者： Popattia, Murad Rafi, Muhammad Qureshi, Rizwan Nawaz, Shah Natl Univ Comp & Emerging Sci Karachi Pakistan Hamad Bin Khalifa Univ Doha Qatar Ist Italiano Tecnol IIT Pattern Anal & Comp Vis PAVIS Genoa Italy DESY Hamburg Germany

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

The use of attention models for automated image captioning has enabled many systems to produce accurate and meaningful descriptions for images. Over the years, many novel approaches have been proposed to enhance the attention process using different feature representations. In this paper, we extend this approach by creating a guided attention network mechanism, that exploits the relationship between the visual scene and text-descriptions using spatial features from the image, high-level information from the topics, and temporal context from caption generation, which are embedded together in an ordered embedding space. A pairwise ranking objective is used for training this embedding space which allows similar images, topics and captions in the shared semantic space to maintain a partial order in the visual-semantic hierarchy and hence, helps the model to produce more visually accurate captions. The experimental results based on MSCOCO dataset shows the competitiveness of our approach, with many state-of-the-art models on various evaluation metrics.

关键词： Training Measurement Visualization computer vision conferences Semantics computer architecture

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The Effect of Improving Annotation Quality on Object Detection Datasets: A Preliminary Study

The Effect of Improving Annotation Quality on Object Detecti...

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

作者： Ma, Jiaxin Ushiku, Yoshitaka Sagara, Miori OMRON SINIC X Corp Tokyo Japan Baobab Inc Tokyo Japan

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

In this study, we partially reannotate conventional benchmark datasets for object detection and check whether there is performance improvement/drop compared with the original annotations. Recent studies on the annotation qualities of ImageNet for image classification revealed some issues of how to associate only a single label to each image accurately. Object detection, on the other hand, should have other nontrivial issues because there are multiple objects in a single image, and realizing consistency among bounding boxes is challenging. A team of professional annotators was formed for MS COCO and Google Open Images datasets. To realize highly-consistent annotations, we prepared carefully designed guidelines for each category and selected quality inspectors who checked the annotation quality of each annotator. Finally, we applied conventional object detection methods for reannotated parts of each dataset. We found mixed results: whether the performance dropped or improved depended on each category and dataset.

关键词： Annotations conferences Object detection Machine learning Benchmark testing Internet pattern recognition

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ScanpathNet: A Recurrent Mixture Density Network for Scanpath Prediction

ScanpathNet: A Recurrent Mixture Density Network for Scanpat...

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

作者： de Belen, Ryan Anthony Jalova Bednarz, Tomasz Sowmya, Arcot Univ New South Wales Sydney NSW Australia

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Understanding the mechanisms underlying human visual attention is an important research problem in cognitive neuroscience and computer vision. While existing models predict salient regions (i.e., saliency maps) and temporal sequences of eye fixations (i.e., scanpaths) in images, their designs often partially follow theoretical frameworks. Here, we introduce ScanpathNet, a deep learning model inspired by the latest theoretical model in neuroscience. It is 'guided' by a dynamic priority map influenced by semantic content and fixation history. The model leverages convolutional neural networks to extract rich semantic features, convolutional long short-term memory networks to model the inhibition of return mechanism and sequential dependencies of fixations, and mixture density networks to predict probability distributions of fixations for each pixel. Simulated human scanpaths can then be generated by sequentially sampling the output of the proposed model. Despite its simplicity, ScanpathNet showed promising qualitative and quantitative scanpath prediction performance in extensive experiments on numerous eye-tracking benchmark datasets.

关键词： Deep learning Visualization computer vision Computational modeling Semantics Predictive models Visual systems

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Federated Learning-based Driver Activity recognition for Edge Devices

Federated Learning-based Driver Activity Recognition for Edg...

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

作者： Doshi, Keval Yilmaz, Yasin Univ S Florida 4202 E Fowler Ave Tampa FL 33620 USA

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Video action recognition has been an active area of research for the past several years. However, the majority of research is concentrated on recognizing a diverse range of activities in distinct environments. On the other hand, Driver Activity recognition (DAR) is significantly more difficult since there is a much finer distinction between various actions. Moreover, training robust DAR models requires diverse training data from multiple sources, which might not be feasible for a centralized setup due to privacy and security concerns. Furthermore, it is critical to develop efficient models due to limited computational resources available on vehicular edge devices. Federated Learning (FL), which allows data parties to collaborate on machine learning models while preserving data privacy and reducing communication requirements, can be used to overcome these challenges. Despite significant progress on various computer vision tasks, FL for DAR has been largely unexplored. In this work, we propose an FL-based DAR model and extensively benchmark the model performance on two datasets under various practical setups. Our results indicate that the proposed approach performs competitively under the centralized (non-FL) and decentralized (FL) settings.

关键词： Performance evaluation Data privacy computer vision Computational modeling Activity recognition Collaborative work Data models

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Self-Supervised Video Similarity Learning

Self-Supervised Video Similarity Learning

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

作者： Kordopatis-Zilos, Giorgos Tolias, Giorgos Tzelepis, Christos Kompatsiaris, Ioannis Patras, Ioannis Papadopoulos, Symeon Czech Technical University VRG FEE Prague Czech Republic Queen Mary University London United Kingdom Information Technologies Institute CERTH Greece

ISBN: (纸本)9798350302493

We introduce S2VS, a video similarity learning approach with self-supervision. Self-Supervised Learning (SSL) is typically used to train deep models on a proxy task so as to have strong transferability on target tasks after fine-tuning. Here, in contrast to prior work, SSL is used to perform video similarity learning and address multiple retrieval and detection tasks at once with no use of labeled data. This is achieved by learning via instance-discrimination with task-tailored augmentations and the widely used InfoNCE loss together with an additional loss operating jointly on self-similarity and hard-negative similarity. We benchmark our method on tasks where video relevance is defined with varying granularity, ranging from video copies to videos depicting the same incident or event. We learn a single universal model that achieves state-of-the-art performance on all tasks, surpassing previously proposed methods that use labeled data. The code and pretrained models are publicly available at: https://***/gkordo/s2vs © 2023 ieee.

关键词： computer vision

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CORE: Consistent Representation Learning for Face Forgery Detection

CORE: Consistent Representation Learning for Face Forgery De...

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

作者： Ni, Yunsheng Meng, Depu Yu, Changqian Quan, Chengbin Ren, Dongchun Zhao, Youjian Tsinghua Univ Beijing Peoples R China Univ Sci & Technol China Hefei Anhui Peoples R China Meituan Beijing Peoples R China

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Face manipulation techniques develop rapidly and arouse widespread public concerns. Despite that vanilla convolutional neural networks achieve acceptable performance, they suffer from the overfitting issue. To relieve this issue, there is a trend to introduce some erasing-based augmentations. We find that these methods indeed attempt to implicitly induce more consistent representations for different augmentations via assigning the same label for different augmented images. However, due to the lack of explicit regularization, the consistency between different representations is less satisfactory. Therefore, we constrain the consistency of different representations explicitly and propose a simple yet effective framework, COnsistent REpresentation Learning (CORE). Specifically, we first capture the different representations with different augmentations, then regularize the cosine distance of the representations to enhance the consistency. Extensive experiments (in-dataset and cross-dataset) demonstrate that CORE performs favorably against state-of-the-art face forgery detection methods. Our code is available at https://***/niyunsheng/CORE.

关键词： Representation learning computer vision Codes Face recognition conferences Market research Forgery

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Implications of Solution patterns on Adversarial Robustness

Implications of Solution Patterns on Adversarial Robustness

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

作者： Liang, Hengyue Liang, Buyun Sun, Ju Cui, Ying Mitchell, Tim University of Minnesota Department of Electrical & Computer Engineering United States University of Minnesota Department of Computer Science & Engineering United States University of Minnesota Department of Industrial & Systems Engineering United States City University of New York Queens College Department of Computer Science United States

ISBN: (纸本)9798350302493

Empirical robustness evaluation (RE) of deep learning models against adversarial perturbations involves solving non-trivial constrained optimization problems. Recent work has shown that these RE problems can be reliably solved by a general-purpose constrained-optimization solver, PyGRANSO with Constraint-Folding (PWCF). In this paper, we take advantage of PWCF and other existing numerical RE algorithms to explore distinct solution patterns in solving RE problems with various combinations of losses, perturbation models, and optimization algorithms. We then provide extensive discussions on the implications of these patterns on current robustness evaluation and adversarial training. A comprehensive version of this work can be found in [19]. © 2023 ieee.

关键词： Constrained optimization

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EKILA: Synthetic Media Provenance and Attribution for Generative Art

EKILA: Synthetic Media Provenance and Attribution for Genera...

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

作者： Balan, Kar Agarwal, Shruti Jenni, Simon Parsons, Andy Gilbert, Andrew Collomosse, John University of Surrey United Kingdom Adobe Inc.

ISBN: (纸本)9798350302493

We present EKILA;a decentralized framework that enables creatives to receive recognition and reward for their contributions to generative AI (GenAI). EKILA proposes a robust visual attribution technique and combines this with an emerging content provenance standard (C2PA) to address the problem of synthetic image provenance - determining the generative model and training data responsible for an AI-generated image. Furthermore, EKILA extends the non-fungible token (NFT) ecosystem to introduce a tokenized representation for rights, enabling a triangular relationship between the asset's Ownership, Rights, and Attribution (ORA). Leveraging the ORA relationship enables creators to express agency over training consent and, through our attribution model, to receive apportioned credit, including royalty payments for the use of their assets in GenAI. © 2023 ieee.

关键词： computer vision

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Semi-Supervised Training to Improve Player and Ball Detection in Soccer

Semi-Supervised Training to Improve Player and Ball Detectio...

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

作者： Vandeghen, Renaud Cioppa, Anthony Van Droogenbroeck, Marc Univ Liege Liege Belgium

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Accurate player and ball detection has become increasingly important in recent years for sport analytics. As most state-of-the-art methods rely on training deep learning networks in a supervised fashion, they require huge amounts of annotated data, which are rarely available. In this paper, we present a novel generic semi-supervised method to train a network based on a labeled image dataset by leveraging a large unlabeled dataset of soccer broadcast videos. More precisely, we design a teacher-student approach in which the teacher produces surrogate annotations on the unlabeled data to be used later for training a student which has the same architecture as the teacher. Furthermore, we introduce three training loss parametrizations that allow the student to doubt the predictions of the teacher during training depending on the proposal confidence score. We show that including unlabeled data in the training process allows to substantially improve the performances of the detection network trained only on the labeled data. Finally, we provide a thorough performance study including different proportions of labeled and unlabeled data, and establish the first benchmark on the new SoccerNet-v3 detection task, with an mAP of 52.3%.

关键词： Training Adaptation models Annotations Benchmark testing Data models pattern recognition Proposals

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