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

共 11897 条记录，以下是1681-1690 订阅

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Quantifying Societal Bias Amplification in Image Captioning

Quantifying Societal Bias Amplification in Image Captioning

引用

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

作者： Hirota, Yusuke Nakashima, Yuta Garcia, Noa Osaka Univ Suita Osaka Japan

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

We study societal bias amplification in image captioning. Image captioning models have been shown to perpetuate gender and racial biases, however, metrics to measure, quantify, and evaluate the societal bias in captions are not yet standardized. We provide a comprehensive study on the strengths and limitations of each metric, and propose LIC, a metric to study captioning bias amplification. We argue that, for image captioning, it is not enough to focus on the correct prediction of the protected attribute, and the whole context should be taken into account. We conduct extensive evaluation on traditional and state-of-the-art image captioning models, and surprisingly find that, by only focusing on the protected attribute prediction, bias mitigation models are unexpectedly amplifying bias.

关键词： Measurement computer vision Equalizers Computational modeling Focusing Predictive models Skin

来源：评论

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A Low-cost & Real-time Motion Capture System

A Low-cost & Real-time Motion Capture System

引用

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

作者： Chatzitofis, Anargyros Albanis, Georgios Zioulis, Nikolaos Thermos, Spyridon Codewheel Larnaka Larnaca Cyprus Univ Thessaly Dept Informat & Telecommun Volos Greece

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Traditional marker-based motion capture requires excessive and specialized equipment, hindering accessibility and wider adoption. In this work, we demonstrate such a system but rely on a very sparse set of low-cost consumer-grade sensors. Our system exploits a data-driven backend to infer the captured subject's joint positions from noisy marker estimates in real-time. In addition to reduced costs and portability, its inherent denoising nature allows for quicker captures by alleviating the need for precise marker placement and post-processing, making it suitable for interactive virtual reality applications.

关键词： computer vision Costs Noise reduction Virtual reality Motion capture Sensor systems Real-time systems

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Deep Depth from Focus with Differential Focus Volume

Deep Depth from Focus with Differential Focus Volume

引用

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

作者： Yang, Fengting Huang, Xiaolei Zhou, Zihan Penn State Univ University Pk PA 16802 USA

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Depth-from-focus (DFF) is a technique that infers depth using the focus change of a camera. In this work, we propose a convolutional neural network (CNN) to find the best-focused pixels in a focal stack and infer depth from the focus estimation. The key innovation of the network is the novel deep differential focus volume (DFV). By computing the first-order derivative with the stacked features over different focal distances, DFV is able to capture both the focus and context information for focus analysis. Besides, we also introduce a probability regression mechanism for focus estimation to handle sparsely sampled focal stacks and provide uncertainty estimation to the final prediction. Comprehensive experiments demonstrate that the proposed model achieves state-of-the-art performance on multiple datasets with good generalizability and fast speed.

关键词： Technological innovation computer vision Uncertainty Computational modeling Estimation computer architecture pattern recognition

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Sequential Transformer for End-to-End Video Text Detection

Sequential Transformer for End-to-End Video Text Detection

引用

ieee/cvf Winter conference on Applications of computer vision (WACV)

作者： Zhang, Jun-Bo Zhao, Meng-Biao Yin, Fei Liu, Cheng-Lin Chinese Acad Sci Inst Automat SKL MAIS Beijing Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing Peoples R China

ISBN: (纸本)9798350318920;9798350318937

In existing methods of video text detection, the detection and tracking branches are usually independent of each other, and although they jointly optimize the backbone network, the tracking-by-detection paradigm still needs to be used during the inference stage. To address this issue, we propose a novel video text detection framework based on sequential transformer, which decodes detection and tracking tasks in parallel, without explicitly setting up a tracking branch. To achieve this, we first introduce the concept of instance query, which learns long-term context information in the video sequence. Then, based on the instance query, the transformer decoder is used to predict the entire box and mask sequence of the text instance in one pass. As a result, the tracking task is realized naturally. In addition, the proposed method can be applied to the scene text detection task seamlessly, without modifying any modules. To the best of our knowledge, this is the first framework to unify the tasks of scene text detection and video text detection. Our model achieves state-of-the-art performance on four video text datasets (YVT, RT-1K, BOVText, and BiRViT-1K), and competitive results on three scene text datasets (CTW1500, MSRA-TD500, and Total-Text). The code is available at https://***/zjb-1/SeqVideoText.

关键词： Algorithms Algorithms Image recognition and understanding Video recognition and understanding

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Self-Supervised Representation Learning with Cross-Context Learning between Global and Hypercolumn Features

Self-Supervised Representation Learning with Cross-Context L...

引用

ieee/cvf Winter conference on Applications of computer vision (WACV)

作者： Gao, Zheng Feng, Chen Patras, Ioannis Queen Mary Univ London Mile End Rd London E1 4NS England

ISBN: (纸本)9798350318920;9798350318937

Whilst contrastive learning yields powerful representations by matching different augmented views of the same instance, it lacks the ability to capture the similarities between different instances. One popular way to address this limitation is by learning global features (after the global pooling) to capture inter-instance relationships based on knowledge distillation, where the global features of the teacher are used to guide the learning of the global features of the student. Inspired by cross-modality learning, we extend this existing framework that only learns from global features by encouraging the global features and intermediate layer features to learn from each other. This leads to our novel self-supervised framework: cross-context learning between global and hypercolumn features (CGH), that enforces the consistency of instance relations between low- and high-level semantics. Specifically, we stack the intermediate feature maps to construct a "hypercolumn" representation so that we can measure instance relations using two contexts (hypercolumn and global feature) separately, and then use the relations of one context to guide the learning of the other. This cross-context learning allows the model to learn from the differences between the two contexts. The experimental results on linear classification and downstream tasks show that our method outperforms the state-of-the-art methods.

关键词： Algorithms Algorithms and algorithms formulations Image recognition and understanding Machine learning architectures

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Augment the Pairs: Semantics-Preserving Image-Caption Pair Augmentation for Grounding-Based vision and Language Models

Augment the Pairs: Semantics-Preserving Image-Caption Pair A...

引用

ieee/cvf Winter conference on Applications of computer vision (WACV)

作者： Yi, Jingru Uzkent, Burak Ignat, Oana Li, Zili Garg, Amanmeet Yu, Xiang Liu, Linda Amazon Prime Video Seattle WA 98109 USA

ISBN: (纸本)9798350318920;9798350318937

Grounding-based vision and language models have been successfully applied to low-level vision tasks, aiming to precisely locate objects referred in captions. The effectiveness of grounding representation learning heavily relies on the scale of the training dataset. Despite being a useful data enrichment strategy, data augmentation has received minimal attention in existing vision and language tasks as augmentation for image-caption pairs is non-trivial. In this study, we propose a robust phrase grounding model trained with text-conditioned and text-unconditioned data augmentations. Specifically, we apply text-conditioned color jittering and horizontal flipping to ensure semantic consistency between images and captions. To guarantee image-caption correspondence in the training samples, we modify the captions according to pre-defined keywords when applying horizontal flipping. Additionally, inspired by recent masked signal reconstruction, we propose to use pixel-level masking as a novel form of data augmentation. While we demonstrate our data augmentation method with MDETR framework, the proposed approach is applicable to common grounding-based vision and language tasks with other frameworks. Finally, we show that image encoder pretrained on large-scale image and language datasets (such as CLIP) can further improve the results. Through extensive experiments on three commonly applied datasets: Flickr30k, referring expressions and GQA, our method demonstrates advanced performance over the state-of-the-arts with various metrics. Code can be found in https://***/amzn/augment-the-pairs-wacv2024.

关键词： Algorithms Algorithms Applications Image recognition and understanding Robotics vision + language and/or other modalities

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Guided Distillation for Semi-Supervised Instance Segmentation

Guided Distillation for Semi-Supervised Instance Segmentatio...

引用

ieee/cvf Winter conference on Applications of computer vision (WACV)

作者： Berrada, Tariq Couprie, Camille Alahari, Karteek Verbeek, Jakob Meta FAIR Menlo Pk CA 94025 USA Univ Grenoble Alpes CNRS Inria Grenoble INPLJK Grenoble France

ISBN: (纸本)9798350318920;9798350318937

Although instance segmentation methods have improved considerably, the dominant paradigm is to rely on fully-annotated training images, which are tedious to obtain. To alleviate this reliance, and boost results, semi-supervised approaches leverage unlabeled data as an additional training signal that limits overfitting to the labeled samples. In this context, we present novel design choices to significantly improve teacher-student distillation models. In particular, we (i) improve the distillation approach by introducing a novel "guided burn-in" stage, and (ii) evaluate different instance segmentation architectures, as well as backbone networks and pre-training strategies. Contrary to previous work which uses only supervised data for the burn-in period of the student model, we also use guidance of the teacher model to exploit unlabeled data in the burn-in period. Our improved distillation approach leads to substantial improvements over previous state-of-the-art results. For example, on the Cityscapes dataset we improve mask-AP from 23.7 to 33.9 when using labels for 10% of images, and on the COCO dataset we improve mask-AP from 18.3 to 34.1 when using labels for only 1% of the training data.

关键词： Algorithms Algorithms and algorithms formulations Image recognition and understanding Machine learning architectures

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M³3D: Learning 3D priors using Multi-Modal Masked Autoencoders for 2D image and video understanding

M<SUP>3</SUP>3D: Learning 3D priors using Multi-Modal Masked...

引用

ieee/cvf Winter conference on Applications of computer vision (WACV)

作者： Jamal, Muhammad Abdullah Mohareri, Omid Intuit Surg Inc Sunnyvale CA 94086 USA

ISBN: (纸本)9798350318920;9798350318937

We present a new pre-training strategy called M(3)3D (Multi-Modal Masked 3D) built based on Multi-modal masked autoencoders that can leverage 3D priors and learned cross-modal representations in RGB-D data. We integrate two major self-supervised learning frameworks;Masked Image Modeling (MIM) and contrastive learning;aiming to effectively embed masked 3D priors and modality complementary features to enhance the correspondence between modalities. In contrast to recent approaches which are either focusing on specific downstream tasks or require multi-view correspondence, we show that our pretraining strategy is ubiquitous, enabling improved representation learning that can transfer into improved performance on various downstream tasks such as video action recognition, video action detection, 2D semantic segmentation and depth estimation. Experiments show that M(3)3D outperforms the existing state-of-the-art approaches on ScanNet, NYUv2, UCF-101 and OR-AR, particularly with an improvement of +1.3% mIoU against Mask3D on ScanNet semantic segmentation. We further evaluate our method on low-data regime and demonstrate its superior data efficiency compared to current state-of-the-art approaches.

关键词： Algorithms Algorithms and algorithms Applications Biomedical / healthcare / medicine formulations Machine learning architectures Video recognition and understanding

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On the Importance of Large Objects in CNN Based Object Detection Algorithms

On the Importance of Large Objects in CNN Based Object Detec...

引用

ieee/cvf Winter conference on Applications of computer vision (WACV)

作者： Ben Saad, Ahmed Facciolo, Gabriele Davy, Axel Univ Paris Saclay CNRS ENS ParisSaclay Ctr Borelli F-91190 Gif Sur Yvette France Schlumberger AI Lab Houston TX 77056 USA

ISBN: (纸本)9798350318920;9798350318937

Object detection models, a prominent class of machine learning algorithms, aim to identify and precisely locate objects in images or videos. However, this task might yield uneven performances sometimes caused by the objects sizes and the quality of the images and labels used for training. In this paper, we highlight the importance of large objects in learning features that are critical for all sizes. Given these findings, we propose to introduce a weighting term into the training loss. This term is a function of the object area size. We show that giving more weight to large objects leads to improved detection scores across all object sizes and so an overall improvement in Object Detectors performances (+2 p.p. of mAP on small objects, +2 p.p. on medium and +4 p.p. on large on COCO val 2017 with InternImage-T). Additional experiments and ablation studies with different models and on a different dataset further confirm the robustness of our findings.

关键词： Algorithms Algorithms Datasets and evaluations Image recognition and understanding

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Handformer2T: A Lightweight Regression-based Model for Interacting Hands Pose Estimation from A Single RGB Image

Handformer2T: A Lightweight Regression-based Model for Inter...

引用

ieee/cvf Winter conference on Applications of computer vision (WACV)

作者： Zhang, Pengfei Kong, Deying Univ Calif Irvine Irvine CA 92617 USA Google Mountain View CA USA

ISBN: (纸本)9798350318920;9798350318937

Despite its extensive range of potential applications in virtual reality and augmented reality, 3D interacting hand pose estimation from RGB image remains a very challenging problem, due to appearance confusions between keypoints of the two hands, and severe hand-hand occlusion. Due to their ability to capture long range relationships between keypoints, transformer-based methods have gained popularity in the research community. However, the existing methods usually deploy tokens at keypoint level, which inevitably results in high computational and memory complexity. In this paper, we propose a simple yet novel mechanism, i.e., hand-level tokenization, in our transformer based model, where we deploy only one token for each hand. With this novel design, we also propose a pose query enhancer module, which can refine the pose prediction iteratively, by focusing on features guided by previous coarse pose predictions. As a result, our proposed model, Handformer2T, can achieve high performance while maintaining lightweight. Extensive experiments on public benchmarks demonstrate that our model can achieve state-of-the-art performance on interacting-hand pose estimation with higher throughput, less memory and faster speed.

关键词： 3D computer vision Algorithms Algorithms Algorithms Biometrics body pose face gesture Image recognition and understanding

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