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检索条件"任意字段=Conference on Computer Vision and Pattern Recognition"

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Weakly-Supervised Physically Unconstrained Gaze Estimation

Weakly-Supervised Physically Unconstrained Gaze Estimation

引用

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

作者： Kothari, Rakshit De Mello, Shalini Iqbal, Umar Byeon, Wonmin Park, Seonwook Kautz, Jan NVIDIA Santa Clara CA 95051 USA Rochester Inst Technol Rochester NY 14623 USA Lunit Inc Seoul South Korea

ISBN: (纸本)9781665445092

A major challenge for physically unconstrained gaze estimation is acquiring training data with 3D gaze annotations for in-the-wild and outdoor scenarios. In contrast, videos of human interactions in unconstrained environments are abundantly available and can be much more easily annotated with frame-level activity labels. In this work, we tackle the previously unexplored problem of weakly-supervised gaze estimation from videos of human interactions. We leverage the insight that strong gaze-related geometric constraints exist when people perform the activity of "looking at each other" (LAEO). To acquire viable 3D gaze supervision from LAEO labels, we propose a training algorithm along with several novel loss functions especially designed for the task. With weak supervision from two large scale CMU-Panoptic and AVA-LAEO activity datasets, we show significant improvements in (a) the accuracy of semisupervised gaze estimation and (b) cross-domain generalization on the state-of-the-art physically unconstrained in-the-wild Gaze360 gaze estimation benchmark.

关键词： Training Geometry Three-dimensional displays Annotations Estimation Training data pattern recognition

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Rich Context Aggregation with Reflection Prior for Glass Surface Detection

Rich Context Aggregation with Reflection Prior for Glass Sur...

引用

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

作者： Lin, Jiaying He, Zebang Lau, Rynson W. H. City Univ Hong Kong Hong Kong Peoples R China

ISBN: (纸本)9781665445092

Glass surfaces appear everywhere. Their existence can however pose a serious problem to computer vision tasks. Recently, a method is proposed to detect glass surfaces by learning multi-scale contextual information. However, as it is only based on a general context integration operation and does not consider any specific glass surface properties, it gets confused when the images contain objects that are similar to glass surfaces and degenerates in challenging scenes with insufficient contexts. We observe that humans often rely on identifying reflections in order to sense the existence of glass and on locating the boundary in order to determine the extent of the glass. Hence, we propose a model for glass surface detection, which consists of two novel modules: (1) a rich context aggregation module (RCAM) to extract multi-scale boundary features from rich context features for locating glass surface boundaries of different sizes and shapes, and (2) a reflection-based refinement module (RRM) to detect reflection and then incorporate it so as to differentiate glass regions from non-glass regions. In addition, we also propose a challenging dataset consisting of 4,012 glass images with annotations for glass surface detection. Our experiments demonstrate that the proposed model outperforms state-of-the-art methods from relevant fields.

关键词： computer vision Shape Annotations Computational modeling Glass Feature extraction Reflection

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DeVLBert: Out-of-distribution Visio-Linguistic Pretraining with Causality

DeVLBert: Out-of-distribution Visio-Linguistic Pretraining w...

引用

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

作者： Zhang, Shengyu Jiang, Tan Wang, Tan Kuang, Kun Zhao, Zhou Zhu, Jianke Yu, Jin Yang, Hongxia Wu, Fei Zhejiang Univ Hangzhou Peoples R China Univ Elect Sci & Technol China Chengdu Peoples R China Alibaba Grp Hangzhou Peoples R China

ISBN: (纸本)9781665448994

In this paper, we propose to investigate out-of-domain visio-linguistic pretraining, where the pretraining data distribution differs from that of downstream data on which the pretrained model will be fine-tuned. Existing methods for this problem are purely likelihood-based, leading to the spurious correlations and hurt the generalization ability when transferred to out-of-domain downstream tasks. By spurious correlation, we mean that the conditional probability of one token (object or word) given another one can be high (due to the dataset biases) without robust (causal) relationships between them. To mitigate such dataset biases, we propose a Deconfounded Visio-Linguistic Bert framework, abbreviated as DeVLBert(1), to perform intervention based-learning. We borrow the idea of the backdoor adjustment from the research field of causality and propose several neural-network based architectures for Bert-style out-of-domain pretraining. The quantitative results on three downstream tasks, Image Retrieval (IR), Zero-shot IR, and Visual Question Answering, show the effectiveness of DeVLBert by boosting generalization ability(2).

关键词： Visualization computer vision Correlation conferences Image retrieval computer architecture Knowledge discovery

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Cluster-wise Hierarchical Generative Model for Deep Amortized Clustering

Cluster-wise Hierarchical Generative Model for Deep Amortize...

引用

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

作者： Liu, Huafeng Wang, Jiaqi Jing, Liping Beijing Jiaotong Univ Sch Comp & Informat Technol Beijing Key Lab Traff Data Anal & Min Beijing Peoples R China

ISBN: (纸本)9781665445092

In this paper, we propose Cluster-wise Hierarchical Generative Model for deep amortized clustering (CHiGac). It provides an efficient neural clustering architecture by grouping data points in a cluster-wise view rather than point-wise view. CHiGac simultaneously learns what makes a cluster, how to group data points into clusters, and how to adaptively control the number of clusters. The dedicated cluster generative process is able to sufficiently exploit pairwise or higher-order interactions between data points in both inter- and intra-cluster, which is useful to sufficiently mine the hidden structure among data. To efficiently minimize the generalized lower bound of CHiGac, we design an Ergodic Amortized Inference (EAI) strategy by considering the average behavior over sequence on an inner variational parameter trajectory, which is theoretically proven to reduce the amortization gap. A series of experiments have been conducted on both synthetic and real-world data. The experimental results demonstrated that CHiGac can efficiently and accurately cluster datasets in terms of both internal and external evaluation metrics (DBI and ACC).

关键词： Measurement computer vision Adaptation models Computational modeling computer architecture Trajectory pattern recognition

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Rethinking and Improving the Robustness of Image Style Transfer

Rethinking and Improving the Robustness of Image Style Trans...

引用

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

作者： Wang, Pei Li, Yijun Vasconcelos, Nuno UC La Jolla CA 92093 USA Adobe Res Los Altos CA USA

ISBN: (纸本)9781665445092

Extensive research in neural style transfer methods has shown that the correlation between features extracted by a pre-trained VGG network has a remarkable ability to capture the visual style of an image. Surprisingly, however, this stylization quality is not robust and often degrades significantly when applied to features from more advanced and lightweight networks, such as those in the ResNet family. By performing extensive experiments with different network architectures, we find that residual connections, which represent the main architectural difference between VGG and ResNet, produce feature maps of small entropy, which are not suitable for style transfer. To improve the robustness of the ResNet architecture, we then propose a simple yet effective solution based on a softmax transformation of the feature activations that enhances their entropy. Experimental results demonstrate that this small magic can greatly improve the quality of stylization results, even for networks with random weights. This suggests that the architecture used for feature extraction is more important than the use of learned weights for the task of style transfer.

关键词： Visualization Smoothing methods Correlation computer architecture Network architecture Feature extraction Robustness

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Determining Dendrometry Using Drone Scouting, Convolutional Neural Networks and Point Clouds

Determining Dendrometry Using Drone Scouting, Convolutional ...

引用

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

作者： Jensen, Kim Krogh, Oskar Kondrup Jorgensen, Marius Willemoes Lehotsky, Daniel Andersen, Anton Bock Porqueras, Ernest Sondergaard, Jens Aksel S. Gade, Rikke Aalborg Univ Dept Elect Syst Aalborg Denmark TeeJet Technol Aabybro Denmark Aalborg Univ Sect Media Technol Aalborg Denmark

ISBN: (纸本)9781665448994

This paper presents a solution for mapping the location of trees in an orchard and estimating the dendrometric data of the trees. The combined solution consists of a mapping and navigation algorithm, which allows for autonomous data collection at an orchard with a regular rectangular layout, and data processing for tree detection and dendrometric data estimation. The data collection is done using an Intel RealSense D435i camera, which can obtain both RGB and depth data. The paper presents a comparison between the performance of point cloud processing (PCP) and convolutional neural networks (CNNs) on RGB data for tree detection and dendrometric data estimation. The YOLOv3 CNN achieved a mAP50 of 63.53% with 65.5 FPS and a mean error of 20.6 cm in height estimation. Point cloud processing achieved a precision of 76.72% with 2.1 FPS and a mean error of 20.4 cm in height estimation. In conclusion, this work shows that point cloud processing shows comparable results to convolutional neural networks for height estimation, but trades off processing time for better precision in detection.

关键词： Three-dimensional displays Navigation Shape Estimation Vegetation Data collection pattern recognition

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Bi-Causal: Group Activity recognition via Bidirectional Causality

Bi-Causal: Group Activity Recognition via Bidirectional Caus...

引用

conference on computer vision and pattern recognition (CVPR)

作者： Youliang Zhang Wenxuan Liu Danni Xu Zhuo Zhou Zheng Wang National Engineering Research Center for Multimedia Software Institute of Artificial Intelligence School of Computer Science Wuhan University Hubei Key Laboratory of Multimedia and Network Communication Engineering Wuhan University of Technology National University of Singapore

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

Current approaches in Group Activity recognition (GAR) predominantly emphasize Human Relations (HRs) while often neglecting the impact of Human-Object Inter-actions (HOIs). This study prioritizes the consideration of both HRs and HOIs, emphasizing their interdependence. Notably, employing Granger Causality Tests reveals the presence of bidirectional causality between HRs and HOIs. Leveraging this insight, we propose a Bidirectional-Causal GAR network. This network establishes a causality commu-nication channel while modeling relations and interactions, enabling reciprocal enhancement between human-object interactions and human relations, ensuring their mutual consistency. Additionally, an Interaction Module is devised to effectively capture the dynamic nature of human-object interactions. Comprehensive experiments conducted on two publicly available datasets showcase the superiority of our proposed method over state-of-the-art approaches. Our project page: https://***/***/

关键词： computer vision Computational modeling Cause effect analysis Communication channels Activity recognition

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A Bop and Beyond: A Second Order Optimizer for Binarized Neural Networks

A Bop and Beyond: A Second Order Optimizer for Binarized Neu...

引用

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

作者： Daniel Suarez-Ramirez, Cuauhtemoc Gonzalez-Mendoza, Miguel Chang, Leonardo Ochoa-Ruiz, Gilberto Alberto Duran-Vega, Mario Tecnol Monterrey Sch Engn & Sci Dept Comp Sci Monterrey NL Mexico

ISBN: (纸本)9781665448994

The optimization of Binary Neural Networks (BNNs) relies on approximating the real-valued weights with their binarized representations. Current techniques for weight-updating use the same approaches as traditional Neural Networks (NNs) with the extra requirement of using an approximation to the derivative of the sign function - as it is the Dirac-Delta function - for back-propagation;thus, efforts are focused adapting full-precision techniques to work on BNNs. In the literature, only one previous effort has tackled the problem of directly training the BNNs with bit-flips by using the first raw moment estimate of the gradients and comparing it against a threshold for deciding when to flip a weight (Bop). In this paper, we take an approach parallel to Adam which also uses the second raw moment estimate to normalize the first raw moment before doing the comparison with the threshold, we call this method Bop2ndOrder. We present two versions of the proposed optimizer: a biased one and a bias-corrected one, each with its own applications. Also, we present a complete ablation study of the hyperparameters space, as well as the effect of using schedulers on each of them. For these studies, we tested the optimizer in CIFAR10 using the BinaryNet architecture. Also, we tested it in ImageNet 2012 with the XnorNet and BiRealNet architectures for accuracy. In both datasets our approach proved to converge faster, was robust to changes of the hyperparameters, and achieved better accuracy values.

关键词： Training computer vision conferences computer architecture Artificial neural networks pattern recognition Optimization

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What is Point Supervision Worth in Video Instance Segmentation?

What is Point Supervision Worth in Video Instance Segmentati...

引用

IEEE computer Society conference on computer vision and pattern recognition Workshops (CVPRW)

作者： Shuaiyi Huang De-An Huang Zhiding Yu Shiyi Lan Subhashree Radhakrishnan Jose M. Alvarez Abhinav Shrivastava Anima Anandkumar University of Maryland College Park NVIDIA Caltech

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Video instance segmentation (VIS) is a challenging vision task that aims to detect, segment, and track objects in videos. Conventional VIS methods rely on densely-annotated object masks which are expensive. We reduce the human annotations to only one point for each object in a video frame during training, and obtain high-quality mask predictions close to fully supervised models. Our proposed training method consists of a class-agnostic proposal generation module to provide rich negative samples and a spatio-temporal point-based matcher to match the object queries with the provided point annotations. Comprehensive experiments on three VIS benchmarks demonstrate competitive performance of the proposed framework, nearly matching fully supervised methods.

关键词： Instance segmentation Training computer vision Annotations Shape Object segmentation Object detection

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Taskology: Utilizing Task Relations at Scale

Taskology: Utilizing Task Relations at Scale

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

作者： Lu, Yao Pirk, Soren Dlabal, Jan Brohan, Anthony Pasad, Ankita Chen, Zhao Casser, Vincent Angelova, Anelia Gordon, Ariel Google Robot Mountain View CA 94043 USA Google Res Mountain View CA 94043 USA Toyota Technol Inst Chicago Chicago IL 60637 USA Waymo LLC Mountain View CA USA

ISBN: (纸本)9781665445092

Many computer vision tasks address the problem of scene understanding and are naturally interrelated e.g. object classification, detection, scene segmentation, depth estimation, etc. We show that we can leverage the inherent relationships among collections of tasks, as they are trained jointly, supervising each other through their known relationships via consistency losses. Furthermore, explicitly utilizing the relationships between tasks allows improving their performance while dramatically reducing the need for labeled data, and allows training with additional unsupervised or simulated data. We demonstrate a distributed joint training algorithm with task-level parallelism, which affords a high degree of asynchronicity and robustness. This allows learning across multiple tasks, or with large amounts of input data, at scale. We demonstrate our framework on subsets of the following collection of tasks: depth and normal prediction, semantic segmentation, 3D motion and egomotion estimation, and object tracking and 3D detection in point clouds. We observe improved performance across these tasks, especially in the low-label regime.

关键词： Training computer vision Three-dimensional displays Computational modeling Semantics Estimation Prediction algorithms

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