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Sat2Cap: Mapping Fine-Grained Textual Descriptions from Satellite Images

Sat2Cap: Mapping Fine-Grained Textual Descriptions from Sate...

引用

ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Aayush Dhakal Adeel Ahmad Subash Khanal Srikumar Sastry Hannah Kerner Nathan Jacobs Washington University in St. Louis Taylor Geospatial Institute Arizona State University

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

We propose a weakly supervised approach for creating maps using free-form textual descriptions. We refer to this work of creating textual maps as zero-shot mapping. Prior works have approached mapping tasks by developing models that predict a fixed set of attributes using overhead imagery. However, these models are very restrictive as they can only solve highly specific tasks for which they were trained. Mapping text, on the other hand, allows us to solve a large variety of mapping problems with minimal restrictions. To achieve this, we train a contrastive learning framework called Sat2Cap on a new large-scale dataset with 6.1M pairs of overhead and ground-level images. For a given location and overhead image, our model predicts the expected CLIP embeddings of the ground-level scenery. The predicted CLIP embeddings are then used to learn about the textual space associated with that location. Sat2Cap is also conditioned on date-time information, allowing it to model temporally varying concepts over a location. Our experimental results demonstrate that our models successfully capture ground-level concepts and allow large-scale mapping of fine-grained textual queries. Our approach does not require any text-labeled data, making the training easily scalable. The code, dataset, and models will be made publicly available.

关键词： Training computer vision Codes Geology conferences Contrastive learning Predictive models

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Towards Weakly-Supervised Domain Adaptation for Lane Detection

Towards Weakly-Supervised Domain Adaptation for Lane Detecti...

引用

ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Jingxing Zhou Chongzhe Zhang Jürgen Beyerer Porsche Engineering Group GmbH Vision and Fusion Laboratory (IES) Karlsruhe Institute of Technology University of Stuttgart Fraunhofer IOSB

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Lane detection plays an indispensable role in automated driving functions and advanced driver assistance systems by providing fundamental spatial orientation, which is imperative for trajectory planning with traffic regulation compliance. The variability of lane structures across the world poses challenges for data-driven lane detection models. However, acquiring vast amounts of labeled data encompassing a wide variety of real-world scenarios for supervised learning is often cost-prohibitive. In this work, we propose a Weakly Supervised Domain Adaptation framework for Lane Detection (WSDAL), which requires easily-provided labels exclusively for the number of lanes in the target domain to aid the adaptation process. WSDAL consists of a teacher-student network, an additional segmentation head as an auxiliary task during training, and a novel loss function that incorporates the number of lanes prediction. As a versatile framework, WSDAL can be applied to any anchor-based lane detector. Between three frequently-used lane detection datasets (TuSimple, CULane and CurveLanes) for domain adaptation, WSDAL framework demonstrates its effectiveness and efficiency over common unsupervised domain adaptation methods and fully supervised training. In addition, we discuss the quality requisites from the labels for the weakly-supervised domain adaptation, indicating that label errors at realistic scales still provide satisfactory results on the considered tasks.

关键词： Training Image segmentation Adaptation models Lane detection Trajectory planning Supervised learning Detectors

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The Affective Growth of computer vision

The Affective Growth of Computer Vision

引用

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

作者： Su, Norman Makoto Crandall, David J. Indiana Univ Luddy Sch Informat Comp & Engn Bloomington IN 47405 USA

ISBN: (纸本)9781665445092

The success of deep learning has led to intense growth and interest in computer vision, along with concerns about its potential impact on society. Yet we know little about how these changes have affected the people that research and practice computer vision: we as a community spend so much effort trying to replicate the abilities of humans, but so little time considering the impact of this work on ourselves. In this paper, we report on a study in which we asked computer vision researchers and practitioners to write stories about emotionally-salient events that happened to them. Our analysis of over 50 responses found tremendous affective (emotional) strain in the computer vision community. While many describe excitement and success, we found strikingly frequent feelings of isolation, cynicism, apathy, and exasperation over the state of the field. This is especially true among people who do not share the unbridled enthusiasm for normative standards for computer vision research and who do not see themselves as part of the "incrowd." Our findings suggest that these feelings are closely tied to the kinds of research and professional practices now expected in computer vision. We argue that as a community with significant stature, we need to work towards an inclusive culture that makes transparent and addresses the real emotional toil of its members.

关键词： Deep learning computer vision pattern recognition Standards Strain

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Dedicated Inference Engine and Binary-Weight Neural Networks for Lightweight Instance Segmentation

Dedicated Inference Engine and Binary-Weight Neural Networks...

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ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Tse-Wei Chen Wei Tao Dongyue Zhao Kazuhiro Mima Tadayuki Ito Kinya Osa Masami Kato Device Technology Development Headquarters Canon Inc. Tokyo Japan Canon Innovative Solution (Beijing) Co. Ltd. Beijing China

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Binary-weight Neural Networks (BNNs), in which weights are binarized and activations are quantized, are employed to reduce computational costs of various kinds of applications. In this paper, a design methodology of hardware architecture for inference engines is proposed to handle modern BNNs with two operation modes. Multiply-Accumulate (MAC) operations can be simplified by replacing multiply operations with bitwise operations. The proposed method can effectively reduce the gate count of inference engines by removing a part of computational costs from the hardware system. The architecture of MAC operations can calculate the inference results of BNNs efficiently with only 52% of hardware costs compared with the related works. To show that the inference engine can handle practical applications, two lightweight networks which combine the backbones of SegNeXt and the decoder of SparseInst for instance segmentation are also proposed. The output results of the lightweight networks are computed using only bitwise operations and add operations. The proposed inference engine has lower hardware costs than related works. The experimental results show that the proposed inference engine can handle the proposed instance-segmentation networks and achieves higher accuracy than YOLACT on the "Person" category although the model size is 77.7× smaller compared with YOLACT.

关键词： Instance segmentation Costs Accuracy Design methodology Neural networks computer architecture Logic gates

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Deep Learning-Based Identification of Arctic Ocean Boundaries and Near-Surface Phenomena in Underwater Echograms

Deep Learning-Based Identification of Arctic Ocean Boundarie...

引用

ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Femina Senjaliya Melissa Cote Amanda Dash Alexandra Branzan Albu Andrea Niemi Stéphane Gauthier Julek Chawarski Steve Pearce Kaan Ersahin Keath Borg Electrical and Computer Engineering University of Victoria Victoria Canada ASL Environmental Sciences Victoria Canada Fisheries and Oceans Canada

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Monitoring marine environments is a crucial part of understanding the impact of oceans on global climate and their importance for biodiversity and ecological systems, particularly in the Arctic region. Underwater active acoustic surveys with moored multi-frequency echosounders allow for the continuous collection of valuable data reflecting the complex dynamics of these environments. This paper addresses the automatic identification of sea surface boundaries and near-surface phenomena in echograms using deep learning methods to support researchers such as biologists in their work, who typically rely on time-consuming manual analyses. We propose a two-step process that first characterizes echograms according to the surface conditions using an image classification paradigm and then identifies the sea surface boundary and near-surface bubbles and their extent in the water column using a semantic segmentation paradigm. Segmentation is carried out using surface type-specific models, which perform better than a single global segmentation model. We also propose learning strategies, such as a custom boundary loss function, that further improve performance. Experiments with various image classification and semantic segmentation architectures allow us to select the most efficient models for Arctic echogram analysis that, when used in conjunction within our proposed pipeline and our learning strategies, offer excellent results.

关键词： Surveys Sea surface Analytical models Biological system modeling Semantic segmentation Arctic Surface treatment

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Uncovering Hidden Emotions with Adaptive Multi-Attention Graph Networks

Uncovering Hidden Emotions with Adaptive Multi-Attention Gra...

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ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Ankith Jain Rakesh Kumar Bir Bhanu Department of Electrical and Computer Engineering University of California Riverside

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Micro-expressions (MEs) are subtle expressions lasting a fraction of a second, offering valuable cues for understanding human emotions and intentions. However, effectively classifying these subtle expressions from video data poses several challenges due to their short duration and low intensity. This paper addresses these issues and presents a novel 2-stream Adaptive Multi-Attention ((Self-Attention and Gaussian Attention) Graph Network (2S-AMAGN) based approach for ME classification in videos. The Self-Attention mechanism captures the global and local dependencies between nodes in a graph. The Gaussian attention mechanism computes weights based on the Gaussian distribution, considering the mean and variance of features across each edge, offering a nuanced understanding of spatial and temporal relationships within MEs. It meticulously analyzes node pair features and edge features, capturing the significance of facial regions. An adaptive learnable weight is introduced to learn the contributions of each attention mechanism, facilitating adaptive attention fusion. The network utilizes a three-frame graph structure to extract spatio-temporal information. The approach incorporates a dynamic frame selection mechanism, which utilizes a sliding window optical flow method to filter out low-intensity emotion frames, thereby refining the extraction of spatio-temporal information from the video data. The results are presented and compared with state-of-the-art methods for SMIC and SAMM databases. Additionally, cross-dataset experiments are conducted, and the results are reported.

关键词： Optical filters Attention mechanisms Adaptive systems Databases Refining Gaussian distribution Information filters

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Unsupervised Multi-Person 3D Human Pose Estimation From 2D Poses Alone

Unsupervised Multi-Person 3D Human Pose Estimation From 2D P...

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ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Peter Hardy Hansung Kim School of Electronics and Computer Science University of Southampton UK

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Current unsupervised 2D-3D human pose estimation (HPE) methods do not work in multi-person scenarios due to perspective ambiguity in monocular images. Therefore, we present one of the first studies investigating the feasibility of unsupervised multi-person 2D-3D HPE from just 2D poses alone, focusing on reconstructing human interactions. To address the issue of perspective ambiguity, we expand upon prior work by predicting the cameras’ elevation angle relative to the subjects’ pelvis. This allows us to rotate the predicted poses to be level with the ground plane, while obtaining an estimate for the vertical offset in 3D between individuals. Our method involves independently lifting each subject’s 2D pose to 3D, before combining them in a shared 3D coordinate system. The poses are then rotated and offset by the predicted elevation angle before being scaled. This by itself enables us to retrieve an accurate 3D reconstruction of their poses. We present our results on the CHI3D dataset, introducing its use for unsupervised 2D-3D pose estimation with three new quantitative metrics, and establishing a benchmark for future research.

关键词： Measurement Three-dimensional displays Accuracy Pose estimation Focusing Benchmark testing Real-time systems

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2T-UNET: A Two-Tower UNet with Depth Clues for Robust Stereo Depth Estimation

2T-UNET: A Two-Tower UNet with Depth Clues for Robust Stereo...

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ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Mansi Sharma Rohit Choudhary Rithvik Anil Thapar Institute of Engineering & Technology Patiala Punjab India Indian Institute of Technology Madras India

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Stereo correspondence matching is an essential part of the multi-step stereo depth estimation process. This paper revisits the depth estimation problem, avoiding the explicit stereo-matching step using a simple two-tower convolutional neural network. The proposed algorithm is entitled as 2T-UNet. The idea behind 2T-UNet is to replace cost volume construction with twin convolution towers. These towers have an allowance for different weights between them. Additionally, the input for twin encoders in 2T-UNet are different compared to the existing stereo methods. Generally, a stereo network takes a right and left image pair as input to determine the scene geometry. However, in the 2T-UNet model, the right stereo image is taken as one input and the left stereo image along with its monocular depth clue information, is taken as the other input. Depth clues provide complementary suggestions that help enhance the quality of predicted scene geometry. The 2T-UNet surpasses state-of-the-art monocular and stereo depth estimation methods on the challenging Scene flow dataset, both quantitatively and qualitatively. The architecture performs incredibly well on complex natural scenes, highlighting its usefulness for various real-time applications. Pretrained weights and code will be made readily available.

关键词： Geometry Solid modeling Costs Three-dimensional displays Poles and towers Estimation computer architecture

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Learning Video Representations of Human Motion from Synthetic Data

Learning Video Representations of Human Motion from Syntheti...

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2022 ieee/CVF conference on computer vision and pattern recognition, CVPR 2022

作者： Guo, Xi Wu, Wei Wang, Dongliang Su, Jing Su, Haisheng Gan, Weihao Huang, Jian Yang, Qin Beihang University China SenseTime Research

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

In this paper, we take an early step towards video representation learning of human actions with the help of large-scale synthetic videos, particularly for human motion representation enhancement. Specifically, we first introduce an automatic action-related video synthesis pipeline based on a photorealistic video game. A large-scale human action dataset named GATA (GTA Animation Transformed Actions) is then built by the proposed pipeline, which includes 8.1 million action clips spanning over 28K action classes. Based on the presented dataset, we design a contrastive learning framework for human motion representation learning, which shows significant performance improvements on several typical video datasets for action recognition, e.g., Charades, HAA 500 and NTU-RGB. Besides, we further explore a domain adaptation method based on cross-domain positive pairs mining to alleviate the domain gap between synthetic and realistic data. Extensive properties analyses of learned representation are conducted to demonstrate the effectiveness of the proposed dataset for enhancing human motion representation learning. © 2022 ieee.

关键词： Representation learning computer vision Pipelines Games Animation Data mining

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AR-CP: Uncertainty-Aware Perception in Adverse Conditions with Conformal Prediction and Augmented Reality For Assisted Driving

AR-CP: Uncertainty-Aware Perception in Adverse Conditions wi...

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ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Achref Doula Max Mühlhäuser Alejandro Sanchez Guinea Technical University of Darmstadt Germany

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Deep learning models are pivotal in enhancing driver assistance systems and improving environmental perception. However, the tendency of neural networks towards overconfident predictions poses a risk of inaccurate predictions, potentially compromising driver safety in adverse conditions. To mitigate this issue, we introduce AR-CP, an uncertainty-aware framework designed to augment driver perception in scenarios characterized by adverse weather and insufficient lighting, through the integration of conformal prediction and augmented reality (AR). Our framework initiates with a conformal prediction step that produces an uncertainty-aware prediction set including potential object classes at a predefined probability level. Subsequently, AR is used to provide a simplified and informative visualization of the closest common parent class of the classes in the prediction set, thereby reducing the likelihood of misinformation. We provide a principled formulation and theoretical analysis of our framework. We evaluate AR-CP on the ROAD dataset, a large dataset containing different difficult situations that induce high uncertainty during prediction time. The results show that our framework outperforms state-of-the-art approaches in providing smaller prediction sets while holding the theoretical guarantees, ensuring an uncertainty-aware prediction, and reducing user confusion. We conduct an immersive user study with 15 participants to investigate the effects of our concept on the quality of perception, situation awareness, and mental load of participants. The results show that our concept facilitates a safer driving experience while holding the mental load low and the situation awareness high.

关键词： Visualization Uncertainty Roads Lighting Reliability theory Safety pattern recognition

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