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Metadata Normalization

Metadata Normalization

引用

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

作者： Lu, Mandy Zhao, Qingyu Zhang, Jiequan Pohl, Kilian M. Li Fei-Fei Niebles, Juan Carlos Adeli, Ehsan Stanford Univ Stanford CA 94305 USA

ISBN: (纸本)9781665445092

Batch Normalization (BN) and its variants have delivered tremendous success in combating the covariate shift induced by the training step of deep learning methods. While these techniques normalize the feature distribution by standardizing with batch statistics, they do not correct the influence on features from extraneous variables or multiple distributions. Such extra variables, referred to as meta-data here, may create bias or confounding effects (e.g., race when classifying gender from face images). We introduce the Metadata Normalization (MDN) layer, a new batch-level operation which can be used end-to-end within the training framework, to correct the influence of meta-data on the feature distribution. MDN adopts a regression analysis technique traditionally used for preprocessing to remove (regress out) the metadata effects on model features during training. We utilize a metric based on distance correlation to quantify the distribution bias from the meta-data and demonstrate that our method successfully removes metadata effects on four diverse settings: one synthetic, one 2D image, one video, and one 3D medical image dataset.

关键词： Deep learning Training Measurement Three-dimensional displays Computational modeling Face recognition computer architecture

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3PSDF: Three-Pole Signed Distance Function for Learning Surfaces with Arbitrary Topologies

3PSDF: Three-Pole Signed Distance Function for Learning Surf...

引用

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

作者： Chen, Weikai Lin, Cheng Li, Weiyang Yang, Bo Tencent Games Digital Content Technol Ctr Shenzhen Peoples R China

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Recent advances in learning 3D shapes using neural implicit functions have achieved impressive results by breaking the previous barrier of resolution and diversity for varying topologies. However, most of such approaches are limited to closed surfaces as they require the space to be divided into inside and outside. More recent works based on unsigned distance function have been proposed to handle complex geometry containing both the open and closed surfaces. Nonetheless, as their direct outputs are point clouds, robustly obtaining high -quality meshing results from discrete points remains an open question. We present a novel learnable implicit representation, called three pole signed distance function (3PSDF), that can represent non-watertight 3D shapes with arbitrary topologies while supporting easy field-to-mesh conversion using the classic Marching Cubes algorithm. The key to our method is the introduction of a new sign, the NULL sign, in addition to the conventional in and out labels. The existence of the null sign could stop the formation of a closed isosurface derived from the bisector of the in/out regions. Further, we propose a dedicated learning framework to effectively learn 3PSDF without worrying about the vanishing gradient due to the null labels. Experimental results show that our approach outperforms the previous state-of-the-art methods in a wide range of benchmarks both quantitatively and qualitatively.

关键词： Point cloud compression Geometry computer vision Three-dimensional displays Shape Benchmark testing Topology

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Neural Reprojection Error: Merging Feature Learning and Camera Pose Estimation

Neural Reprojection Error: Merging Feature Learning and Came...

引用

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

作者： Germain, Hugo Lepetit, Vincent Bourmaud, Guillaume Univ Gustave Eiffel Ecole Ponts LIGM CNRS Marne La Vallee France Univ Bordeaux IMS Bordeaux INP CNRS Bordeaux France

ISBN: (纸本)9781665445092

Absolute camera pose estimation is usually addressed by sequentially solving two distinct subproblems: First a feature matching problem that seeks to establish putative 2D-3D correspondences, and then a Perspective-n-Point problem that minimizes, w.r.t. the camera pose, the sum of so-called Reprojection Errors (RE). We argue that generating putative 2D-3D correspondences 1) leads to an important loss of information that needs to be compensated as far as possible, within RE, through the choice of a robust loss and the tuning of its hyperparameters and 2) may lead to an RE that conveys erroneous data to the pose estimator. In this paper, we introduce the Neural Reprojection Error (NRE) as a substitute for RE. NRE allows to rethink the camera pose estimation problem by merging it with the feature learning problem, hence leveraging richer information than 2D-3D correspondences and eliminating the need for choosing a robust loss and its hyperparameters. Thus NRE can be used as training loss to learn image descriptors tailored for pose estimation. We also propose a coarse-to-fine optimization method able to very efficiently minimize a sum of NRE terms w.r.t. the camera pose. We experimentally demonstrate that NRE is a good substitute for RE as it significantly improves both the robustness and the accuracy of the camera pose estimate while being computationally and memory highly efficient. From a broader point of view, we believe this new way of merging deep learning and 3D geometry may be useful in other computer vision applications.

关键词： Geometry Deep learning computer vision Three-dimensional displays Pose estimation Merging Memory management

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Dense Contrastive Learning for Self-Supervised Visual Pre-Training

Dense Contrastive Learning for Self-Supervised Visual Pre-Tr...

引用

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

作者： Wang, Xinlong Zhang, Rufeng Shen, Chunhua Kong, Tao Li, Lei Univ Adelaide Adelaide SA Australia Tongji Univ Shanghai Peoples R China ByteDance AI Lab Beijing Peoples R China

ISBN: (纸本)9781665445092

To date, most existing self-supervised learning methods are designed and optimized for image classification. These pre-trained models can be sub-optimal for dense prediction tasks due to the discrepancy between image-level prediction and pixel-level prediction. To fill this gap, we aim to design an effective, dense self-supervised learning method that directly works at the level of pixels (or local features) by taking into account the correspondence between local features. We present dense contrastive learning (DenseCL), which implements self-supervised learning by optimizing a pairwise contrastive (dis)similarity loss at the pixel level between two views of input images. Compared to the baseline method MoCo-v2, our method introduces negligible computation overhead (only <1% slower), but demonstrates consistently superior performance when transferring to downstream dense prediction tasks including object detection, semantic segmentation and instance segmentation;and outperforms the state-of-the-art methods by a large margin. Specifically, over the strong MoCo-v2 baseline, our method achieves significant improvements of 2.0% AP on PASCAL VOC object detection, 1.1% AP on COCO object detection, 0.9% AP on COCO instance segmentation, 3.0% mIoU on PASCAL VOC semantic segmentation and 1.8% mIoU on Cityscapes semantic segmentation.

关键词： Learning systems Image segmentation computer vision Visualization Computational modeling Semantics Object detection

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Efficient Deep Embedded Subspace Clustering

Efficient Deep Embedded Subspace Clustering

引用

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

作者： Cai, Jinyu Fan, Jicong Guo, Wenzhong Wang, Shiping Zhang, Yunhe Zhang, Zhao Fuzhou Univ Coll Comp & Data Sci Fuzhou Peoples R China Chinese Univ Hong Kong Shenzhen Sch Data Sci Hong Kong Peoples R China Shenzhen Res Inst Big Data Shenzhen Peoples R China Hefei Univ Technol Hefei Peoples R China

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Recently deep learning methods have shown significant progress in data clustering tasks. Deep clustering methods (including distance-based methods and subspace-based methods) integrate clustering and feature learning into a unified framework, where there is a mutual promotion between clustering and representation. However, deep subspace clustering methods are usually in the framework of self-expressive model and hence have quadratic time and space complexities, which prevents their applications in large-scale clustering and real-time clustering. In this paper, we propose a new mechanism for deep clustering. We aim to learn the subspace bases from deep representation in an iterative refining manner while the refined subspace bases help learning the representation of the deep neural networks in return. The proposed method is out of the selfexpressive framework, scales to the sample size linearly, and is applicable to arbitrarily large datasets and online clustering scenarios. More importantly, the clustering accuracy of the proposed method is much higher than its competitors. Extensive comparison studies with state-of-the-art clustering approaches on benchmark datasets demonstrate the superiority of the proposed method.

关键词： Deep learning Representation learning computer vision Clustering methods Refining Neural networks Real-time systems

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Fine-Grained Object Classification via Self-Supervised Pose Alignment

Fine-Grained Object Classification via Self-Supervised Pose ...

引用

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

作者： Yang, Xuhui Wang, Yaowei Chen, Ke Xu, Yong Tian, Yonghong Peng Cheng Lab Shenzhen Peoples R China South China Univ Technol Guangzhou Peoples R China China Commun & Comp Network Lab Guangdong Guangzhou Peoples R China

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Semantic patterns of fine-grained objects are determined by subtle appearance difference of local parts, which thus inspires a number of part-based methods. However, due to uncontrollable object poses in images, distinctive details carried by local regions can be spatially distributed or even self-occluded, leading to a large variation on object representation. For discounting pose variations, this paper proposes to learn a novel graph based object representation to reveal a global configuration of local parts for self-supervised pose alignment across classes, which is employed as an auxiliary feature regularization on a deep representation learning network. Moreover, a coarse-to-fine supervision together with the proposed pose-insensitive constraint on shallow-to-deep sub-networks encourages discriminative features in a curriculum learning manner. We evaluate our method on three popular fine-grained object classification benchmarks, consistently achieving the state-of-the-art performance. Source codes are available at https://***/yangxh11/P2P-Net.

关键词： Representation learning computer vision Codes Semantics Benchmark testing Image representation Robustness

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Language-driven Grasp Detection

Language-driven Grasp Detection

引用

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

作者： An Dinh Vuong Minh Nhat Vu Baoru Huang Nghia Nguyen Hieu Le Thieu Vo Anh Nguyen FPT Software AI Ctr Hanoi Vietnam TU Wien Automat Control Inst Vienna Austria Imperial Coll London London England Ton Duc Thang Univ Ho Chi Minh City Vietnam Univ Liverpool Liverpool Merseyside England

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353006

Grasp detection is a persistent and intricate challenge with various industrial applications. Recently, many methods and datasets have been proposed to tackle the grasp detection problem. However, most of them do not consider using natural language as a condition to detect the grasp poses. In this paper, we introduce Grasp-Anything++, a new language- driven grasp detection dataset featuring 1M samples, over 3M objects, and upwards of 10M grasping instructions. We utilize foundation models to create a large-scale scene corpus with corresponding images and grasp prompts. We approach the language-driven grasp detection task as a conditional generation problem. Drawing on the success of diffusion models in generative tasks and given that language plays a vital role in this task, we propose a new language-driven grasp detection method based on diffusion models. Our key contribution is the contrastive training objective, which explicitly contributes to the denoising process to detect the grasp pose given the language instructions. We illustrate that our approach is theoretically supportive. The intensive experiments show that our method outperforms state-of-the-art approaches and allows real-world robotic grasping. Finally, we demonstrate our large-scale dataset enables zero-short grasp detection and is a challenging benchmark for future work.

关键词： Training computer vision Noise reduction Natural languages Grasping Benchmark testing Diffusion models

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Regressive Domain Adaptation for Unsupervised Keypoint Detection

Regressive Domain Adaptation for Unsupervised Keypoint Detec...

引用

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

作者： Jiang, Junguang Ji, Yifei Wang, Ximei Liu, Yufeng Wang, Jianmin Long, Mingsheng Tsinghua Univ Sch Software BNRist Beijing Peoples R China Kuaishou Technol Y Tech Beijing Peoples R China

ISBN: (纸本)9781665445092

Domain adaptation (DA) aims at transferring knowledge from a labeled source domain to an unlabeled target domain. Though many DA theories and algorithms have been proposed, most of them are tailored into classification settings and may fail in regression tasks, especially in the practical keypoint detection task. To tackle this difficult but significant task, we present a method of regressive domain adaptation (RegDA) for unsupervised keypoint detection. Inspired by the latest theoretical work, we first utilize an adversarial regressor to maximize the disparity on the target domain and train a feature generator to minimize this disparity. However, due to the high dimension of the output space, this regressor fails to detect samples that deviate from the support of the source. To overcome this problem, we propose two important ideas. First, based on our observation that the probability density of the output space is sparse, we introduce a spatial probability distribution to describe this sparsity and then use it to guide the learning of the adversarial regressor. Second, to alleviate the optimization difficulty in the high-dimensional space, we innovatively convert the minimax game in the adversarial training to the minimization of two opposite goals. Extensive experiments show that our method brings large improvement by 8% to 11% in terms of PCK on different datasets.

关键词： Training computer vision Games Superluminescent diodes Minimization Probability distribution Generators

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VideoGrounding-DINO: Towards Open-Vocabulary Spatio-Temporal Video Grounding

VideoGrounding-DINO: Towards Open-Vocabulary Spatio-Temporal...

引用

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

作者： Wasim, Syed Talal Naseer, Muzammal Khan, Salman Yang, Ming-Hsuan Khan, Fahad Shahbaz Mohamed Bin Zayed Univ AI Abu Dhabi U Arab Emirates Australian Natl Univ Canberra Australia Univ Calif Merced Merced CA USA Google Res Mountain View CA USA Linkoping Univ Linkoping Sweden

ISBN: (纸本)9798350353006

Video grounding aims to localize a spatio-temporal section in a video corresponding to an input text query. This paper addresses a critical limitation in current video grounding methodologies by introducing an Open-Vocabulary Spatio-Temporal Video Grounding task. Unlike prevalent closed-set approaches that struggle with open-vocabulary scenarios due to limited training data and pre-defined vocabularies, our model leverages pre-trained representations from foundational spatial grounding models. This empowers it to effectively bridge the semantic gap between natural language and diverse visual content, achieving strong performance in closed-set and open-vocabulary settings. Our contributions include a novel spatio-temporal video grounding model, surpassing state-of-the-art results in closed-set evaluations on multiple datasets and demonstrating superior performance in open-vocabulary scenarios. Notably, the proposed model outperforms state-of-the-art methods in closed-set settings on VidSTG (Declarative and Interrogative) and HC-STVG (V1 and V2) datasets. Furthermore, in open-vocabulary evaluations on HC-STVG V1 and YouCook-Interactions, our model surpasses the recent best-performing models by 4.88 m vIoU and 1.83% accuracy, demonstrating its efficacy in handling diverse linguistic and visual concepts for improved video understanding. Our codes will be publicly released.

关键词： MultiModal Open Vocabulary Video Grounding vision Language

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ActiveZero: Mixed Domain Learning for Active Stereovision with Zero Annotation

ActiveZero: Mixed Domain Learning for Active Stereovision wi...

引用

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

作者： Liu, Isabella Yang, Edward Tao, Jianyu Chen, Rui Zhang, Xiaoshuai Ran, Qing Liu, Zhu Su, Hao Univ Calif San Diego San Diego CA 92103 USA Tsinghua Univ Beijing Peoples R China Alibaba DAMO Acad Hangzhou Peoples R China

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Traditional depth sensors generate accurate real world depth estimates that surpass even the most advanced learning approaches trained only on simulation domains. Since ground truth depth is readily available in the simulation domain but quite difficult to obtain in the real domain, we propose a method that leverages the best of both worlds. In this paper we present a new framework, ActiveZero, which is a mixed domain learning solution for active stereovision systems that requires no real world depth annotation. First, we demonstrate the transferability of our method to out-of-distribution real data by using a mixed domain learning strategy. In the simulation domain, we use a combination of supervised disparity loss and self-supervised losses on a shape primitives dataset. By contrast, in the real domain, we only use self-supervised losses on a dataset that is out-of-distribution from either training simulation data or test real data. Second, our method introduces a novel self-supervised loss called temporal IR reprojection to increase the robustness and accuracy of our reprojections in hard-to-perceive regions. Finally, we show how the method can be trained end-to-end and that each module is important for attaining the end result. Extensive qualitative and quantitative evaluations on real data demonstrate state of the art results that can even beat a commercial depth sensor.

关键词： Training computer vision Codes Annotations Shape Robustness Data models

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