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UAV-Human: A Large Benchmark for Human Behavior Understanding with Unmanned Aerial Vehicles

UAV-Human: A Large Benchmark for Human Behavior Understandin...

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

作者： Li, Tianjiao Liu, Jun Zhang, Wei Ni, Yun Wang, Wenqian Li, Zhiheng Singapore Univ Technol & Design Informat Syst Technol & Design Singapore Singapore Shandong Univ Sch Control Sci & Engn Jinan Shandong Peoples R China

ISBN: (纸本)9781665445092

Human behavior understanding with unmanned aerial vehicles (UAVs) is of great significance for a wide range of applications, which simultaneously brings an urgent demand of large, challenging, and comprehensive benchmarks for the development and evaluation of UAV-based models. However, existing benchmarks have limitations in terms of the amount of captured data, types of data modalities, categories of provided tasks, and diversities of subjects and environments. Here we propose a new benchmark - UAV-Human - for human behavior understanding with UAVs, which contains 67,428 multi-modal video sequences and 119 subjects for action recognition, 22,476 frames for pose estimation, 41,290 frames and 1,144 identities for person re-identification, and 22,263 frames for attribute recognition. Our dataset was collected by a flying UAV in multiple urban and rural districts in both daytime and night-time over three months, hence covering extensive diversities w.r.t subjects, backgrounds, illuminations, weathers, occlusions, camera motions, and UAV flying attitudes. Such a comprehensive and challenging benchmark shall be able to promote the research of UAV-based human behavior understanding, including action recognition, pose estimation, re-identification, and attribute recognition. Furthermore, we propose a fisheye-based action recognition method that mitigates the distortions in fisheye videos via learning unbounded transformations guided by flat RGB videos. Experiments show the efficacy of our method on the UAV-Human dataset.

关键词： computer vision Pose estimation Video sequences Lighting Benchmark testing Distortion Unmanned aerial vehicles

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Dual-stream Multiple Instance Learning Network for Whole Slide Image Classification with Self-supervised Contrastive Learning

Dual-stream Multiple Instance Learning Network for Whole Sli...

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

作者： Li, Bin Li, Yin Eliceiri, Kevin W. Univ Wisconsin Dept Biomed Engn Madison WI 53706 USA Morgridge Inst Res Madison WI 53715 USA Univ Wisconsin Dept Biostat & Med Informat Madison WI 53706 USA Univ Wisconsin Dept Comp Sci Madison WI 53706 USA Univ Wisconsin Dept Med Phys Madison WI 53706 USA

ISBN: (纸本)9781665445092

We address the challenging problem of whole slide image (WSI) classification. WSIs have very high resolutions and usually lack localized annotations. WSI classification can be cast as a multiple instance learning (MIL) problem when only slide-level labels are available. We propose a MIL-based method for WSI classification and tumor detection that does not require localized annotations. Our method has three major components. First, we introduce a novel MIL aggregator that models the relations of the instances in a dual-stream architecture with trainable distance measurement. Second, since WSIs can produce large or unbalanced bags that hinder the training of MIL models, we propose to use self-supervised contrastive learning to extract good representations for MIL and alleviate the issue of prohibitive memory cost for large bags. Third, we adopt a pyramidal fusion mechanism for multiscale WSI features, and further improve the accuracy of classification and localization. Our model is evaluated on two representative WSI datasets. The classification accuracy of our model compares favorably to fully-supervised methods, with less than 2% accuracy gap across datasets. Our results also outperform all previous MIL-based methods. Additional benchmark results on standard MIL datasets further demonstrate the superior performance of our MIL aggregator on general MIL problems.

关键词： Training Location awareness Image resolution Annotations Feature extraction Distance measurement pattern recognition

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D-NeRF: Neural Radiance Fields for Dynamic Scenes

D-NeRF: Neural Radiance Fields for Dynamic Scenes

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

作者： Pumarola, Albert Corona, Enric Pons-Moll, Gerard Moreno-Noguer, Francesc UPC Inst Robbt & Informat Ind CSIC Barcelona Spain Univ Tubingen Tubingen Germany Max Planck Inst Informat Saarbrucken Germany

ISBN: (纸本)9781665445092

Neural rendering techniques combining machine learning with geometric reasoning have arisen as one of the most promising approaches for synthesizing novel views of a scene from a sparse set of images. Among these, stands out the Neural radiance fields (NeRF) [31], which trains a deep network to map 5D input coordinates (representing spatial location and viewing direction) into a volume density and view-dependent emitted radiance. However, despite achieving an unprecedented level of photorealism on the generated images, NeRF is only applicable to static scenes, where the same spatial location can be queried from different images. In this paper we introduce D-NeRF, a method that extends neural radiance fields to a dynamic domain, allowing to reconstruct and render novel images of objects under rigid and non-rigid motions from a single camera moving around the scene. For this purpose we consider time as an additional input to the system, and split the learning process in two main stages: one that encodes the scene into a canonical space and another that maps this canonical representation into the deformed scene at a particular time. Both mappings are simultaneously learned using fully-connected networks. Once the networks are trained, D-NeRF can render novel images, controlling both the camera view and the time variable, and thus, the object movement. We demonstrate the effectiveness of our approach on scenes with objects under rigid, articulated and non-rigid motions. Code, model weights and the dynamic scenes dataset will be available at [1].

关键词： Photorealism computer vision Three-dimensional displays Dynamics Machine learning Cameras Rendering (computer graphics)

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Selective, Interpretable and Motion Consistent Privacy Attribute Obfuscation for Action recognition

Selective, Interpretable and Motion Consistent Privacy Attri...

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conference on computer vision and pattern recognition (cvpr)

作者： Filip Ilic He Zhao Thomas Pock Richard P. Wildes TU Graz York University

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

Concerns for the privacy of individuals captured in public imagery have led to privacy-preserving action recognition. Existing approaches often suffer from issues arising through obfuscation being applied globally and a lack of interpretability. Global obfuscation hides privacy sensitive regions, but also contextual regions important for action recognition. Lack of interpretability erodes trust in these new technologies. We highlight the limitations of current paradigms and propose a solution: Human selected privacy templates that yield interpretability by design, an ob-fuscation scheme that selectively hides attributes and also induces temporal consistency, which is important in action recognition. Our approach is architecture agnostic and directly modifies input imagery, while existing approaches generally require architecture training. Our approach offers more flexibility, as no training is required, and outperforms alternatives on three widely used datasets.

关键词： Training Privacy computer vision Image recognition Art Semantics Noise

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Combinatorial Learning of Graph Edit Distance via Dynamic Embedding

Combinatorial Learning of Graph Edit Distance via Dynamic Em...

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

作者： Wang, Runzhong Zhang, Tianqi Yu, Tianshu Yan, Junchi Yang, Xiaokang Shanghai Jiao Tong Univ Dept Comp Sci & Engn Shanghai Peoples R China Shanghai Jiao Tong Univ MoE Key Lab Artificial Intelligence Shanghai Peoples R China Arizona State Univ Tempe AZ 85287 USA

ISBN: (纸本)9781665445092

Graph Edit Distance (GED) is a popular similarity measurement for pairwise graphs and it also refers to the recovery of the edit path from the source graph to the target graph. Traditional A* algorithm suffers scalability issues due to its exhaustive nature, whose search heuristics heavily rely on human prior knowledge. This paper presents a hybrid approach by combing the interpretability of traditional search-based techniques for producing the edit path, as well as the efficiency and adaptivity of deep embedding models to achieve a cost-effective GED solver. Inspired by dynamic programming, node-level embedding is designated in a dynamic reuse fashion and suboptimal branches are encouraged to be pruned. To this end, our method can be readily integrated into A* procedure in a dynamic fashion, as well as significantly reduce the computational burden with a learned heuristic. Experimental results on different graph datasets show that our approach can remarkably ease the search process of A* without sacrificing much accuracy. To our best knowledge, this work is also the first deep learning-based GED method for recovering the edit path.

关键词： computer vision Adaptation models Costs Scalability Heuristic algorithms Computational modeling Search problems

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Weakly Supervised Video Emotion Detection and Prediction via Cross-Modal Temporal Erasing Network

Weakly Supervised Video Emotion Detection and Prediction via...

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conference on computer vision and pattern recognition (cvpr)

作者： Zhicheng Zhang Lijuan Wang Jufeng Yang TMCC College of Computer Science Nankai University China

Automatically predicting the emotions of user-generated videos (UGVs) receives increasing interest recently. However, existing methods mainly focus on a few key visual frames, which may limit their capacity to encode the context that depicts the intended emotions. To tackle that, in this paper, we propose a cross-modal temporal erasing network that locates not only keyframes but also context and audio-related information in a weakly-supervised manner. In specific, we first leverage the intra- and inter-modal relationship among different segments to accurately select keyframes. Then, we iteratively erase keyframes to encourage the model to concentrate on the contexts that include complementary information. Extensive experiments on three challenging video emotion benchmarks demonstrate that our method performs favorably against state-of-the-art approaches. The code is released on https://***/nku-zhichengzhang/WECL.

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Dual Pixel Exploration: Simultaneous Depth Estimation and Image Restoration

Dual Pixel Exploration: Simultaneous Depth Estimation and Im...

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

作者： Pan, Liyuan Chowdhury, Shah Hartley, Richard Liu, Miaomiao Zhang, Hongguang Li, Hongdong Australian Natl Univ Canberra ACT Australia CSIRO A&F Canberra ACT Australia CSIRO Data61 Canberra ACT Australia SE Inst AMS Amsterdam Netherlands

ISBN: (纸本)9781665445092

The dual-pixel (DP) hardware works by splitting each pixel in half and creating an image pair in a single snapshot. Several works estimate depth/inverse depth by treating the DP pair as a stereo pair. However, dual-pixel disparity only occurs in image regions with the defocus blur. The heavy defocus blur in DP pairs affects the performance of matching-based depth estimation approaches. Instead of removing the blur effect blindly, we study the formation of the DP pair which links the blur and the depth information. In this paper, we propose a mathematical DP model which can benefit depth estimation by the blur. These explorations motivate us to propose an end-to-end DDDNet (DP-based Depth and Deblur Network) to jointly estimate the depth and restore the image. Moreover, we define a reblur loss, which reflects the relationship of the DP image formation process with depth information, to regularise our depth estimate in training. To meet the requirement of a large amount of data for learning, we propose the first DP image simulator which allows us to create datasets with DP pairs from any existing RGBD dataset. As a side contribution, we collect a real dataset for further research. Extensive experimental evaluation on both synthetic and real datasets shows that our approach achieves competitive performance compared to state-of-the-art approaches.

关键词： Training computer vision Computational modeling Buildings Estimation Cameras Mathematical models

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Learning to Fuse Asymmetric Feature Maps in Siamese Trackers

Learning to Fuse Asymmetric Feature Maps in Siamese Trackers

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

作者： Han, Wencheng Dong, Xingping Khan, Fahad Shahbaz Shao, Ling Shen, Jianbing Beijing Inst Technol Beijing Peoples R China Incept Inst Artificial Intelligence Abu Dhabi U Arab Emirates Mohamed Bin Zayed Univ Artificial Intelligence Abu Dhabi U Arab Emirates

ISBN: (纸本)9781665445092

Recently, Siamese-based trackers have achieved promising performance in visual tracking. Most recent Siamese-based trackers typically employ a depth-wise cross-correlation (DW-XCorr) to obtain multi-channel correlation information from the two feature maps (target and search region). However, DW-XCorr has several limitations within Siamese-based tracking: it can easily be fooled by distractors, has fewer activated channels and provides weak discrimination of object boundaries. Further, DW-XCorr is a handcrafted parameter-free module and cannot fully benefit from offline learning on large-scale data. We propose a learnable module, called the asymmetric convolution (ACM), which learns to better capture the semantic correlation information in offline training on largescale data. Different from DW-XCorr and its predecessor (XCorr), which regard a single feature map as the convolution kernel, our ACM decomposes the convolution operation on a concatenated feature map into two mathematically equivalent operations, thereby avoiding the need for the feature maps to be of the same size (width and height) during concatenation. Our ACM can incorporate useful prior information, such as bounding-box size, with standard visual features. Furthermore, ACM can easily be integrated into existing Siamese trackers based on DW-XCorr or XCorr. To demonstrate its generalization ability, we integrate ACM into three representative trackers: SiamFC, SiamRPN++ and SiamBAN. Our experiments reveal the benefits of the proposed ACM, which outperforms existing methods on six tracking benchmarks. On the LaSOT test set, our ACM-based tracker obtains a significant improvement of 5.8% in terms of success (AUC), over the baseline.

关键词： Training Visualization Target tracking Correlation Convolution Fuses pattern recognition

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Towards Efficient Tensor Decomposition-Based DNN Model Compression with Optimization Framework

Towards Efficient Tensor Decomposition-Based DNN Model Compr...

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

作者： Yin, Miao Sui, Yang Liao, Siyu Yuan, Bo Rutgers State Univ Dept ECE New Brunswick NJ 08901 USA Amazon Seattle WA USA

ISBN: (纸本)9781665445092

Advanced tensor decomposition, such as tensor train (TT) and tensor ring (TR), has been widely studied for deep neural network (DNN) model compression, especially for recurrent neural networks (RNNs). However, compressing convolutional neural networks (CNNs) using TT/TR always suffers significant accuracy loss. In this paper, we propose a systematic framework for tensor decomposition-based model compression using Alternating Direction Method of Multipliers (ADMM). By formulating TT decompositionbased model compression to an optimization problem with constraints on tensor ranks, we leverage ADMM technique to systemically solve this optimization problem in an iterative way. During this procedure, the entire DNN model is trained in the original structure instead of TT format, but gradually enjoys the desired low tensor rank characteristics. We then decompose this uncompressed model to TT format, and fine-tune it to finally obtain a high-accuracy TTformat DNN model. Our framework is very general, and it works for both CNNs and RNNs, and can be easily modified to fit other tensor decomposition approaches. We evaluate our proposed framework on different DNN models for image classification and video recognition tasks. Experimental results show that our ADMM-based TT-format models demonstrate very high compression performance with high accuracy. Notably, on CIFAR-100, with 2.3x and 2.4xcompression ratios, our models have 1.96% and 2.21% higher top-1 accuracy than the original ResNet-20 and ResNet-32, respectively. For compressing ResNet-18 on ImageNet, our model achieves 2.47x FLOPs reduction without accuracy loss.

关键词： Tensors Image coding Systematics Recurrent neural networks Image recognition Computational modeling Convex functions

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DeFLOCNet: Deep Image Editing via Flexible Low-level Controls

DeFLOCNet: Deep Image Editing via Flexible Low-level Control...

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

作者： Liu, Hongyu Wan, Ziyu Huang, Wei Song, Yibing Han, Xintong Liao, Jing Jiang, Bin Liu, Wei Huya Inc Guangzhou Peoples R China City Univ Hong Kong Hong Kong Peoples R China Hunan Univ Changsha Peoples R China Tencent AI Lab Shenzhen Peoples R China Tencent Data Platform Shenzhen Peoples R China

ISBN: (纸本)9781665445092

User-intended visual content fills the hole regions of an input image in the image editing scenario. The coarse low-level inputs, which typically consist of sparse sketch lines and color dots, convey user intentions for content creation (i.e., free-form editing). While existing methods combine an input image and these low-level controls for CNN inputs, the corresponding feature representations are not sufficient to convey user intentions, leading to unfaithfully generated content. In this paper, we propose DeFLOCNet which relies on a deep encoder-decoder CNN to retain the guidance of these controls in the deep feature representations. In each skip-connection layer, we design a structure generation block. Instead of attaching low-level controls to an input image, we inject these controls directly into each structure generation block for sketch line refinement and color propagation in the CNN feature space. We then concatenate the modulated features with the original decoder features for structure generation. Meanwhile, DeFLOCNet involves another decoder branch for texture generation and detail enhancement. Both structures and textures are rendered in the decoder, leading to user-intended editing results. Experiments on benchmarks demonstrate that DeFLOCNet effectively transforms different user intentions to create visually pleasing content.

关键词： Visualization computer vision Image color analysis Transforms Benchmark testing Aerospace electronics Decoding

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