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RPSRNet: End-to-End Trainable Rigid Point Set Registration Network using Barnes-Hut 2^D-Tree Representation

RPSRNet: End-to-End Trainable Rigid Point Set Registration N...

引用

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

作者： Ali, Sk Aziz Kahraman, Kerem Reis, Gerd Stricker, Didier TU Kaiserslautern Kaiserslautern Germany German Res Ctr Artificial Intelligence DFKI GmbH Kaiserslautern Germany

ISBN: (纸本)9781665445092

We propose RPSRNet - a novel end-to-end trainable deep neural network for rigid point set registration. For this task, we use a novel 2D-tree representation for the input point sets and a hierarchical deep feature embedding in the neural network. An iterative transformation refinement module of our network boosts the feature matching accuracy in the intermediate stages. We achieve an inference speed of similar to 12-15 ms to register a pair of input point clouds as large as similar to 250K. Extensive evaluations on (i) KITTI LiDAR-odometry and (ii) ModelNet-40 datasets show that our method outperforms prior state-of-the-art methods - e.g., on the KITTI dataset, DCP-v2 by 1.3 and 1.5 times, and PointNetLK by 1.8 and 1.9 times better rotational and translational accuracy respectively. Evaluation on ModelNet40 shows that RPSRNet is more robust than other benchmark methods when the samples contain a significant amount of noise and disturbance. RPSRNet accurately registers point clouds with non-uniform sampling densities, e.g., LiDAR data, which cannot be processed by many existing deep-learning-based registration methods.

关键词： Deep learning computer vision Laser radar Neural networks Benchmark testing Registers pattern recognition

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Dynamic Neural Radiance Fields for Monocular 4D Facial Avatar Reconstruction

Dynamic Neural Radiance Fields for Monocular 4D Facial Avata...

引用

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

作者： Gafni, Guy Thies, Justus Zollhoefer, Michael Niessner, Matthias Tech Univ Munich Munich Germany Facebook Real Labs Res Pittsburgh PA USA

ISBN: (纸本)9781665445092

We present dynamic neural radiance fields for modeling the appearance and dynamics of a human face(1). Digitally modeling and reconstructing a talking human is a key building-block for a variety of applications. Especially, for telepresence applications in AR or VR, a faithful reproduction of the appearance including novel viewpoint or headposes is required. In contrast to state-of-the-art approaches that model the geometry and material properties explicitly, or are purely image-based, we introduce an implicit representation of the head based on scene representation networks. To handle the dynamics of the face, we combine our scene representation network with a low-dimensional morphable model which provides explicit control over pose and expressions. We use volumetric rendering to generate images from this hybrid representation and demonstrate that such a dynamic neural scene representation can be learned from monocular input data only, without the need of a specialized capture setup. In our experiments, we show that this learned volumetric representation allows for photorealistic image generation that surpasses the quality of state-of-the-art video-based reenactment methods.

关键词： Geometry Telepresence Webcams Image synthesis Avatars Rendering (computer graphics) Hybrid power systems

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Reciprocal Transformations for Unsupervised Video Object Segmentation

Reciprocal Transformations for Unsupervised Video Object Seg...

引用

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

作者： Ren, Sucheng Liu, Wenxi Liu, Yongtuo Chen, Haoxin Han, Guoqiang He, Shengfeng South China Univ Technol Sch Comp Sci & Engn Guangzhou Peoples R China Fuzhou Univ Coll Math & Comp Sci Fuzhou Peoples R China

ISBN: (纸本)9781665445092

Unsupervised video object segmentation (UVOS) aims at segmenting the primary objects in videos without any human intervention. Due to the lack of prior knowledge about the primary objects, identifying them from videos is the major challenge of UVOS. Previous methods often regard the moving objects as primary ones and rely on optical flow to capture the motion cues in videos, but the flow information alone is insufficient to distinguish the primary objects from the background objects that move together. This is because, when the noisy motion features are combined with the appearance features, the localization of the primary objects is misguided. To address this problem, we propose a novel reciprocal transformation network to discover primary objects by correlating three key factors: the intra-frame contrast, the motion cues, and temporal coherence of recurring objects. Each corresponds to a representative type of primary object, and our reciprocal mechanism enables an organic coordination of them to effectively remove ambiguous distractions from videos. Additionally, to exclude the information of the moving background objects from motion features, our transformation module enables to reciprocally transform the appearance features to enhance the motion features, so as to focus on the moving objects with salient appearance while removing the co-moving outliers. Experiments on the public benchmarks demonstrate that our model significantly outperforms the state-of-the-art methods.

关键词： Optical filters computer vision Transforms Object segmentation Coherence Search problems pattern recognition

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Exploring and Utilizing pattern Imbalance

Exploring and Utilizing Pattern Imbalance

引用

conference on computer vision and pattern recognition (CVPR)

作者： Shibin Mei Chenglong Zhao Shengchao Yuan Bingbing Ni Shanghai Jiao Tong University Shanghai China

In this paper, we identify pattern imbalance from several aspects, and further develop a new training scheme to avert pattern preference as well as spurious correlation. In contrast to prior methods which are mostly concerned with category or domain granularity, ignoring the potential finer structure that existed in datasets, we give a new definition of seed category as an appropriate optimization unit to distinguish different patterns in the same category or domain. Extensive experiments on domain generalization datasets of diverse scales demonstrate the effectiveness of the proposed method.

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Multi-Label Learning from Single Positive Labels

Multi-Label Learning from Single Positive Labels

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

作者： Cole, Elijah Mac Aodha, Oisin Lorieul, Titouan Perona, Pietro Morris, Dan Jojic, Nebojsa CALTECH Pasadena CA 91125 USA Univ Edinburgh Edinburgh Midlothian Scotland INRIA Rocquencourt France Microsoft AI Earth Washington DC USA Microsoft Res Redmond WA USA

ISBN: (纸本)9781665445092

Predicting all applicable labels for a given image is known as multi-label classification. Compared to the standard multi-class case (where each image has only one label), it is considerably more challenging to annotate training data for multi-label classification. When the number of potential labels is large, human annotators find it difficult to mention all applicable labels for each training image. Furthermore, in some settings detection is intrinsically difficult e.g. finding small object instances in high resolution images. As a result, multi-label training data is often plagued by false negatives. We consider the hardest version of this problem, where annotators provide only one relevant label for each image. As a result, training sets will have only one positive label per image and no confirmed negatives. We explore this special case of learning from missing labels across four different multi-label image classification datasets for both linear classifiers and end-to-end fine-tuned deep networks. We extend existing multi-label losses to this setting and propose novel variants that constrain the number of expected positive labels during training. Surprisingly, we show that in some cases it is possible to approach the performance of fully labeled classifiers despite training with significantly fewer confirmed labels.

关键词： Training computer vision Image resolution Annotations Training data pattern recognition Standards

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Sequential Graph Convolutional Network for Active Learning

Sequential Graph Convolutional Network for Active Learning

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

作者： Caramalau, Razvan Bhattarai, Binod Kim, Tae-Kyun Imperial Coll London London England Korea Adv Inst Sci & Technol Daejeon South Korea

ISBN: (纸本)9781665445092

We propose a novel pool-based Active Learning framework constructed on a sequential Graph Convolution Network (GCN). Each images feature from a pool of data represents a node in the graph and the edges encode their similarities. With a small number of randomly sampled images as seed labelled examples, we learn the parameters of the graph to distinguish labelled vs unlabelled nodes by minimising the binary cross-entropy loss. GCN performs message-passing operations between the nodes, and hence, induces similar representations of the strongly associated nodes. We exploit these characteristics of GCN to select the unlabelled examples which are sufficiently different from labelled ones. To this end, we utilise the graph node embeddings and their confidence scores and adapt sampling techniques such as CoreSet and uncertainty-based methods to query the nodes. We flip the label of newly queried nodes from unlabelled to labelled, re-train the learner to optimise the downstream task and the graph to minimise its modified objective. We continue this process within a fixed budget. We evaluate our method on 6 different benchmarks: 4 real image classification, 1 depth-based hand pose estimation and 1 synthetic RGB image classification datasets. Our method outperforms several competitive baselines such as VAAL, Learning Loss, CoreSet and attains the new state-of-the-art performance on multiple applications.

关键词： computer vision Convolution Image edge detection Pose estimation Benchmark testing pattern recognition Task analysis

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Informative and Consistent Correspondence Mining for Cross-Domain Weakly Supervised Object Detection

Informative and Consistent Correspondence Mining for Cross-D...

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

作者： Hou, Luwei Zhang, Yu Fu, Kui Li, Jia Beihang Univ Sch Comp Sci & Engn State Key Lab Virtual Real Technol & Syst Beijing Peoples R China Peng Cheng Lab Shenzhen Peoples R China SenseTime Res Beijing Peoples R China

ISBN: (纸本)9781665445092

Cross-domain weakly supervised object detection aims to adapt object-level knowledge from a fully labeled source domain dataset (i.e., with object bounding boxes) to train object detectors for target domains that are weakly labeled (i.e., with image-level tags). Instead of domain-level distribution matching, as popularly adopted in the literature, we propose to learn pixel-wise cross-domain correspondences for more precise knowledge transfer. It is realized through a novel cross-domain co-attention scheme trained as region competition. In this scheme, the cross-domain correspondence module seeks for informative features on the target domain image, which if warped to the source domain image, could best explain its annotations. Meanwhile, a collaborative mask generator competes to mask out the relevant target image region to make the remaining features uninformative. Such competitive learning strives to correlate the full foreground in cross-domain image pairs, revealing the accurate object extent in target domain. To alleviate the ambiguity of inter-domain correspondence learning, a domain-cycle consistency regularizer is further proposed to leverage the more reliable intra-domain correspondence. The proposed approach achieves consistent improvements over existing approaches by a considerable margin, demonstrated by the experiments on various datasets.

关键词： computer vision Annotations Collaboration Object detection Detectors Generators pattern recognition

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VarifocalNet: An IoU-aware Dense Object Detector

VarifocalNet: An IoU-aware Dense Object Detector

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

作者： Zhang, Haoyang Wang, Ying Dayoub, Feras Sunderhauf, Niko Queensland Univ Technol Australian Ctr Robot Vis Brisbane Qld Australia Univ Queensland Brisbane Qld Australia

ISBN: (纸本)9781665445092

Accurately ranking the vast number of candidate detections is crucial for dense object detectors to achieve high performance. Prior work uses the classification score or a combination of classification and predicted localization scores to rank candidates. However, neither option results in a reliable ranking, thus degrading detection performance. In this paper, we propose to learn an Iou-Aware Classification Score (IACS) as a joint representation of object presence confidence and localization accuracy. We show that dense object detectors can achieve a more accurate ranking of candidate detections based on the IACS. We design a new loss function, named Varifocal Loss, to train a dense object detector to predict the IACS, and propose a new star-shaped bounding box feature representation for IACS prediction and bounding box refinement. Combining these two new components and a bounding box refinement branch, we build an IoU-aware dense object detector based on the FCOS+ATSS architecture, that we call VarifocalNet or VFNet for short. Extensive experiments on MS COCO show that our VFNet consistently surpasses the strong baseline by similar to 2.0 AP with different backbones. Our best model VFNet-X-1200 with Res2Net-101-DCN achieves a single-model single-scale AP of 55.1 on COCO test-dev, which is state-of-the-art among various object detectors.

关键词： Location awareness Training computer vision Codes Detectors computer architecture Benchmark testing

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Deep Compositional Metric Learning

Deep Compositional Metric Learning

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

作者： Zheng, Wenzhao Wang, Chengkun Lu, Jiwen Zhou, Jie Tsinghua Univ Dept Automat Beijing Peoples R China Beijing Natl Res Ctr Informat Sci & Technol Beijing Peoples R China

ISBN: (纸本)9781665445092

In this paper, we propose a deep compositional metric learning (DCML) framework for effective and generalizable similarity measurement between images. Conventional deep metric learning methods minimize a discriminative loss to enlarge interclass distances while suppressing intraclass variations, which might lead to inferior generalization performance since samples even from the same class may present diverse characteristics. This motivates the adoption of the ensemble technique to learn a number of sub-embeddings using different and diverse subtasks. However, most subtasks impose weaker or contradictory constraints, which essentially sacrifices the discrimination ability of each sub-embedding to improve the generalization ability of their combination. To achieve a better generalization ability without compromising, we propose to separate the sub-embeddings from direct supervisions from the subtasks and apply the losses on different composites of the sub-embeddings. We employ a set of learnable compositors to combine the sub-embeddings and use a self-reinforced loss to train the compositors, which serve as relays to distribute the diverse training signals to avoid destroying the discrimination ability. Experimental results on the CUB-200-2011, Cars196, and Stanford Online Products datasets demonstrate the superior performance of our framework.(1)

关键词： Measurement Training Learning systems computer vision pattern recognition Relays

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ACTION-Net: Multipath Excitation for Action recognition

ACTION-Net: Multipath Excitation for Action Recognition

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

作者： Wang, Zhengwei She, Qi Smolic, Aljosa Trinity Coll Dublin V SENSE Dublin Ireland ByteDance AI Lab Beijing Peoples R China

ISBN: (纸本)9781665445092

Spatial-temporal, channel-wise, and motion patterns are three complementary and crucial types of information for video action recognition. Conventional 2D CNNs are computationally cheap but cannot catch temporal relationships;3D CNNs can achieve good performance but are computationally intensive. In this work, we tackle this dilemma by designing a generic and effective module that can be embedded into 2D CNNs. To this end, we propose a spAtio-temporal, Channel and moTion excitatION (ACTION) module consisting of three paths: Spatio-Temporal Excitation (STE) path, Channel Excitation (CE) path, and Motion Excitation (ME) path. The STE path employs one channel 3D convolution to characterize spatio-temporal representation. The CE path adaptively recalibrates channelwise feature responses by explicitly modeling interdependencies between channels in terms of the temporal aspect. The ME path calculates feature-level temporal differences, which is then utilized to excite motion-sensitive channels. We equip 2D CNNs with the proposed ACTION module to form a simple yet effective ACTION-Net with very limited extra computational cost. ACTION-Net is demonstrated by consistently outperforming 2D CNN counterparts on three backbones (i.e., ResNet-50, MobileNet V2 and BNInception) employing three datasets (i.e., Something-Something V2, Jester, and EgoGesture). Code is provided at https://***/V-Sense/ACTION-Net.

关键词： computer vision Adaptation models Three-dimensional displays Codes Target recognition Convolution Computational modeling

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