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MVFuseNet: Improving End-to-End Object Detection and Motion Forecasting through Multi-View Fusion of LiDAR Data

MVFuseNet: Improving End-to-End Object Detection and Motion ...

引用

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

作者： Laddha, Ankit Gautam, Shivam Palombo, Stefan Pandey, Shreyash Vallespi-Gonzalez, Carlos Aurora Innovat Mountain View CA 94043 USA

ISBN: (纸本)9781665448994

In this work, we propose MVFuseNet, a novel end-to-end method for joint object detection and motion forecasting from a temporal sequence of LiDAR data. Most existing methods operate in a single view by projecting data in either range view (RV) or bird's eye view (BEV). In contrast, we propose a method that effectively utilizes both RV and BEV for spatio-temporal feature learning as part of a temporal fusion network as well as for multi-scale feature learning in the backbone network. Further, we propose a novel sequential fusion approach that effectively utilizes multiple views in the temporal fusion network. We show the benefits of our multi-view approach for the tasks of detection and motion forecasting on two large-scale self-driving data sets, achieving state-of-the-art results. Furthermore, we show that MVFusenet scales well to large operating ranges while maintaining real-time performance.

关键词： computer vision Laser radar conferences Object detection Real-time systems pattern recognition Forecasting

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A vision-based System for Traffic Anomaly Detection using Deep Learning and Decision Trees

A Vision-based System for Traffic Anomaly Detection using De...

引用

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

作者： Aboah, Armstrong Shoman, Maged Mandal, Vishal Davami, Sayedomidreza Adu-Gyamfi, Yaw Sharma, Anuj Univ Missouri Columbia Dept Civil & Environm Engn Columbia MO 65211 USA Iowa State Univ Dept Civil & Environm Engn Ames IA USA

ISBN: (纸本)9781665448994

Any intelligent traffic monitoring system must be able to detect anomalies such as traffic accidents in real time. In this paper, we propose a Decision-Tree enabled approach powered by deep learning for extracting anomalies from traffic cameras while accurately estimating the start and end times of the anomalous event. Our approach included creating a detection model, followed by anomaly detection and analysis. YOLOv5 served as the foundation for our detection model. The anomaly detection and analysis step entail traffic scene background estimation, road mask extraction, and adaptive thresholding. Candidate anomalies were passed through a decision tree to detect and analyze final anomalies. The proposed approach yielded an F1 score of 0.8571, and an S4 score of 0.5686, per the experimental validation.

关键词： Deep learning Adaptation models Roads conferences Estimation Real-time systems pattern recognition

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Coordinate Attention for Efficient Mobile Network Design

Coordinate Attention for Efficient Mobile Network Design

引用

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

作者： Hou, Qibin Zhou, Daquan Feng, Jiashi Natl Univ Singapore Singapore Singapore SEA AI Lab Singapore Singapore

ISBN: (纸本)9781665445092

Recent studies on mobile network design have demonstrated the remarkable effectiveness of channel attention (e.g., the Squeeze-and-Excitation attention) for lifting model performance, but they generally neglect the positional information, which is important for generating spatially selective attention maps. In this paper, we propose a novel attention mechanism for mobile networks by embedding positional information into channel attention, which we call "coordinate attention". Unlike channel attention that transforms a feature tensor to a single feature vector via 2D global pooling, the coordinate attention factorizes channel attention into two 1D feature encoding processes that aggregate features along the two spatial directions, respectively. In this way, long-range dependencies can be captured along one spatial direction and meanwhile precise positional information can be preserved along the other spatial direction. The resulting feature maps are then encoded separately into a pair of direction-aware and position-sensitive attention maps that can be complementarily applied to the input feature map to augment the representations of the objects of interest. Our coordinate attention is simple and can be flexibly plugged into classic mobile networks, such as MobileNetV2, MobileNeXt, and EfficientNet with nearly no computational overhead. Extensive experiments demonstrate that our coordinate attention is not only beneficial to ImageNet classification but more interestingly, behaves better in down-stream tasks, such as object detection and semantic segmentation. Code is available at https://***/Andrew-Qibin/CoordAttention.

关键词： Image segmentation computer vision Tensors Computational modeling Semantics Object detection Transforms

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Calibrated RGB-D Salient Object Detection

Calibrated RGB-D Salient Object Detection

引用

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

作者： Ji, Wei Li, Jingjing Yu, Shuang Zhang, Miao Piao, Yongri Yao, Shunyu Bi, Qi Ma, Kai Zheng, Yefeng Lu, Huchuan Cheng, Li Univ Alberta Edmonton AB Canada Tencent Jarvis Lab Shenzhen Peoples R China Dalian Univ Technol Dalian Peoples R China Wuhan Univ Wuhan Hubei Peoples R China

ISBN: (纸本)9781665445092

Complex backgrounds and similar appearances between objects and their surroundings are generally recognized as challenging scenarios in Salient Object Detection (SOD). This naturally leads to the incorporation of depth information in addition to the conventional RGB image as input, known as RGB-D SOD or depth-aware SOD. Meanwhile, this emerging line of research has been considerably hindered by the noise and ambiguity that prevail in raw depth images. To address the aforementioned issues, we propose a Depth Calibration and Fusion (DCF) framework that contains two novel components: 1) a learning strategy to calibrate the latent bias in the original depth maps towards boosting the SOD performance;2) a simple yet effective cross reference module to fuse features from both RGB and depth modalities. Extensive empirical experiments demonstrate that the proposed approach achieves superior performance against 27 state-of-the-art methods. Moreover, our depth calibration strategy alone can work as a preprocessing step;empirically it results in noticeable improvements when being applied to existing cutting-edge RGB-D SOD models.

关键词： computer vision Codes Fuses Computational modeling Object detection Boosting Calibration

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BASAR:Black-box Attack on Skeletal Action recognition

BASAR:Black-box Attack on Skeletal Action Recognition

引用

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

作者： Diao, Yunfeng Shao, Tianjia Yang, Yong-Liang Zhou, Kun Wang, He Univ Leeds Leeds W Yorkshire England Southwest Jiaotong Univ Chengdu Peoples R China Zhejiang Univ State Key Lab CAD & CG Hangzhou Peoples R China Univ Bath Bath Avon England

ISBN: (纸本)9781665445092

Skeletal motion plays a vital role in human activity recognition as either an independent data source or a complement [33]. The robustness of skeleton-based activity recognizers has been questioned recently [29,50], which shows that they are vulnerable to adversarial attacks when the full-knowledge of the recognizer is accessible to the attacker. However, this white-box requirement is overly restrictive in most scenarios and the attack is not truly threatening. In this paper, we show that such threats do exist under black-box settings too. To this end, we propose the first black-box adversarial attack method BASAR. Through BASAR, we show that adversarial attack is not only truly a threat but also can be extremely deceitful, because on-manifold adversarial samples are rather common in skeletal motions, in contrast to the common belief that adversarial samples only exist off-manifold [18]. Through exhaustive evaluation and comparison, we show that BASAR can deliver successful attacks across models, data, and attack modes. Through harsh perceptual studies, we show that it achieves effective yet imperceptible attacks. By analyzing the attack on different activity recognizers, BASAR helps identify the potential causes of their vulnerability and provides insights on what classifiers are likely to be more robust against attack.

关键词： computer vision Activity recognition Robustness Data models

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Self-attention based Text Knowledge Mining for Text Detection

Self-attention based Text Knowledge Mining for Text Detectio...

引用

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

作者： Wan, Qi Ji, Haoqin Shen, Linlin Shenzhen Univ Comp Vis Inst Sch Comp Sci & Software Engn Nanhai Ave 3688 Shenzhen Peoples R China Shenzhen Univ Shenzhen Inst Artificial Intelligence & Robot Soc Nanhai Ave 3688 Shenzhen Peoples R China Shenzhen Univ Guangdong Key Lab Intelligent Informat Proc Nanhai Ave 3688 Shenzhen Peoples R China

ISBN: (纸本)9781665445092

Pre-trained models play an important role in deep learning based text detectors. However, most methods ignore the gap between natural images and scene text images and directly apply ImageNet for pre-training. To address such a problem, some of them firstly pre-train the model using a large amount of synthetic data and then fine-tune it on target datasets, which is task-specific and has limited generalization capability. In this paper, we focus on providing general pre-trained models for text detectors. Considering the importance of exploring text contents for text detection, we propose STKM (Self-attention based Text Knowledge Mining), which consists of a CNN Encoder and a Self-attention Decoder, to learn general prior knowledge for text detection from SynthText. Given only image level text labels, Self-attention Decoder directly decodes features extracted from CNN Encoder to texts without requirement of detection, which guides the CNN backbone to explicitly learn discriminative semantic representations ignored by previous approaches. After that, the text knowledge learned by the backbone can be transferred to various text detectors to significantly improve their detection performance (e.g., 5.89% higher F-measure for EAST on ICDAR15 dataset) without bells and whistles. Pre-trained model is available at: http.s/***/CVI -SZU/STKM

关键词： Knowledge engineering Deep learning computer vision Semantics Detectors Feature extraction Data models

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Generalizable Novel-View Synthesis Using a Stereo Camera

Generalizable Novel-View Synthesis Using a Stereo Camera

引用

conference on computer vision and pattern recognition (cvpr)

作者： Haechan Lee Wonjoon Jin Seung-Hwan Baek Sunghyun Cho GSAI POSTECH CSE POSTECH

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

In this paper, we propose the first generalizable view synthesis approach that specifically targets multi-view stereocamera images. Since recent stereo matching has demonstrated accurate geometry prediction, we introduce stereo matching into novel-view synthesis for high-quality geometry reconstruction. To this end, this paper proposes a novel framework, dubbed StereoNeRF, which integrates stereo matching into a NeRF-based generalizable view synthesis approach. StereoNeRF is equipped with three key components to effectively exploit stereo matching in novel-view synthesis: a stereo feature extractor, a depth-guided plane-sweeping, and a stereo depth loss. Moreover, we propose the StereoNVS dataset, the first multi-view dataset of stereocamera images, encompassing a wide variety of both real and synthetic scenes. Our experimental results demonstrate that StereoNeRF surpasses previous approaches in generalizable view synthesis.

关键词： Geometry Training computer vision Accuracy Estimation Feature extraction Cameras

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ArtAdapter: Text-to-Image Style Transfer using Multi-Level Style Encoder and Explicit Adaptation

ArtAdapter: Text-to-Image Style Transfer using Multi-Level S...

引用

conference on computer vision and pattern recognition (cvpr)

作者： Dar-Yen Chen Hamish Tennent Ching-Wen Hsu PicCollage Taiwan

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

This work introduces ArtAdapter, a transformative text-to-image (T2I) style transfer framework that transcends tra-ditional limitations of color, brushstrokes, and object shape, capturing high-level style elements such as composition and distinctive artistic expression. The integration of a multi-level style encoder with our proposed explicit adaptation mechanism enables ArtAdapter to achieve unprecedented fidelity in style transfer, ensuring close alignment with tex-tual descriptions. Additionally, the incorporation of an Aux-iliary Content Adapter (ACA) effectively separates content from style, alleviating the borrowing of content from style references. Moreover, our novel fast finetuning approach could further enhance zero-shot style representation while mitigating the risk of overfitting. Comprehensive evaluations confirm that ArtAdapter surpasses current state-of-the-art methods.

关键词： computer vision Adaptation models Shape Image color analysis Text to image Interference Benchmark testing

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Efficient Hierarchical Entropy Model for Learned Point Cloud Compression

Efficient Hierarchical Entropy Model for Learned Point Cloud...

引用

conference on computer vision and pattern recognition (cvpr)

作者： Rui Song Chunyang Fu Shan Liu Ge Li School of Electronic and Computer Engineering Shenzhen Graduate Scool Peking University Tencent America

Learning an accurate entropy model is a fundamental way to remove the redundancy in point cloud compression. Recently, the octree-based auto-regressive entropy model which adopts the self-attention mechanism to explore dependencies in a large-scale context is proved to be promising. However, heavy global attention computations and auto-regressive contexts are inefficient for practical applications. To improve the efficiency of the attention model, we propose a hierarchical attention structure that has a linear complexity to the context scale and maintains the global receptive field. Furthermore, we present a grouped context structure to address the serial decoding issue caused by the auto-regression while preserving the compression performance. Experiments demonstrate that the proposed entropy model achieves superior rate-distortion performance and significant decoding latency reduction compared with the state-of-the-art large-scale auto-regressive entropy model.

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Monocular Reconstruction of Neural Face Reflectance Fields

Monocular Reconstruction of Neural Face Reflectance Fields

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

作者： Mallikarjun, B. R. Tewari, Ayush Oh, Tae-Hyun Weyrich, Tim Bickel, Bernd Seidel, Hans-Peter Pfister, Hanspeter Matusik, Wojciech Elgharib, Mohamed Theobalt, Christian Max Planck Inst Informat Saarland Informat Campus Saarbrucken Germany POSTECH Pohang South Korea UCL London England IST Austria Klosterneuburg Austria Harvard Univ Cambridge MA 02138 USA MIT CSAIL Cambridge MA USA

ISBN: (纸本)9781665445092

The reflectance field of a face describes the reflectance properties responsible for complex lighting effects including diffuse, specular, inter-reflection and self shadowing. Most existing methods for estimating the face reflectance from a monocular image assume faces to be diffuse with very few approaches adding a specular component. This still leaves out important perceptual aspects of reflectance such as higher-order global illumination effects and self-shadowing. We present a new neural representation for face reflectance where we can estimate all components of the reflectance responsible for the final appearance from a monocular image. Instead of modeling each component of the reflectance separately using parametric models, our neural representation allows us to generate a basis set of faces in a geometric deformation-invariant space, parameterized by the input light direction, viewpoint and face geometry. We learn to reconstruct this reflectance field of a face just from a monocular image, which can be used to render the face from any viewpoint in any light condition. Our method is trained on a light-stage dataset, which captures 300 people illuminated with 150 light conditions from 8 viewpoints. We show that our method outperforms existing monocular reflectance reconstruction methods due to better capturing of physical effects, such as sub-surface scattering, specularities, self-shadows and other higher-order effects.

关键词： Reflectivity Geometry Deformable models Face recognition Lighting Scattering Reconstruction algorithms

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