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Compatibility-aware Heterogeneous Visual Search

Compatibility-aware Heterogeneous Visual Search

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

作者： Duggal, Rahul Zhou, Hao Yang, Shuo Xiong, Yuanjun Xia, Wei Tu, Zhuowen Soatto, Stefano Amazon AI AWS Seattle WA 98121 USA Georgia Inst Technol Atlanta GA 30332 USA Amazon AI Seattle WA USA

ISBN: (纸本)9781665445092

We tackle the problem of visual search under resource constraints. Existing systems use the same embedding model to compute representations (embeddings) for the query and gallery images. Such systems inherently face a hard accuracy-efficiency trade-off: the embedding model needs to be large enough to ensure high accuracy, yet small enough to enable query-embedding computation on resource-constrained platforms. This trade-off could be mitigated if gallery embeddings are generated from a large model and query embeddings are extracted using a compact model. The key to building such a system is to ensure representation compatibility between the query and gallery models. In this paper, we address two forms of compatibility: One enforced by modifying the parameters of each model that computes the embeddings. The other by modifying the architectures that compute the embeddings, leading to compatibility-aware neural architecture search (CMP-NAS). We test CMP-NAS on challenging retrieval tasks for fashion images (DeepFashion2), and face images (IJB-C). Compared to ordinary (homogeneous) visual search using the largest embedding model (paragon), CMP-NAS achieves 80-fold and 23-fold cost reduction while maintaining accuracy within 0:3% and 1:6% of the paragon on DeepFashion2 and IJB-C respectively.

关键词： Visualization computer vision Costs Computational modeling Face recognition Architecture Buildings

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Multi-Label Learning from Single Positive Labels

Multi-Label Learning from Single Positive Labels

引用

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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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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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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STaR: Self-supervised Tracking and Reconstruction of Rigid Objects in Motion with Neural Rendering

STaR: Self-supervised Tracking and Reconstruction of Rigid O...

引用

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

作者： Yuan, Wentao Lv, Zhaoyang Schmidt, Tanner Lovegrove, Steven Univ Washington Seattle WA 98195 USA Facebook Real Labs Res Menlo Pk CA USA

ISBN: (纸本)9781665445092

We present STaR, a novel method that performs Self-supervised Tracking and Reconstruction of dynamic scenes with rigid motion from multi-view RGB videos without any manual annotation. Recent work has shown that neural networks are surprisingly effective at the task of compressing many views of a scene into a learned function which maps from a viewing ray to an observed radiance value via volume rendering. Unfortunately, these methods lose all their predictive power once any object in the scene has moved. In this work, we explicitly model rigid motion of objects in the context of neural representations of radiance fields. We show that without any additional human specified supervision, we can reconstruct a dynamic scene with a single rigid object in motion by simultaneously decomposing it into its two constituent parts and encoding each with its own neural representation. We achieve this by jointly optimizing the parameters of two neural radiance fields and a set of rigid poses which align the two fields at each frame. On both synthetic and real world datasets, we demonstrate that our method can render photorealistic novel views, where novelty is measured on both spatial and temporal axes. Our factored representation furthermore enables animation of unseen object motion.

关键词： Tracking Dynamics Neural networks Lighting Transforms Manuals Rendering (computer graphics)

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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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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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Domain Adaptation with Auxiliary Target Domain-Oriented Classifier

Domain Adaptation with Auxiliary Target Domain-Oriented Clas...

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

作者： Liang, Jian Hu, Dapeng Feng, Jiashi Natl Univ Singapore NUS Singapore Singapore Sea AI Lab SAIL Singapore Singapore

ISBN: (纸本)9781665445092

Domain adaptation (DA) aims to transfer knowledge from a label-rich but heterogeneous domain to a label-scare domain, which alleviates the labeling efforts and attracts considerable attention. Different from previous methods focusing on learning domain-invariant feature representations, some recent methods present generic semi-supervised learning (SSL) techniques and directly apply them to DA tasks, even achieving competitive performance. One of the most popular SSL techniques is pseudo-labeling that assigns pseudo labels for each unlabeled data via the classifier trained by labeled data. However, it ignores the distribution shift in DA problems and is inevitably biased to source data. To address this issue, we propose a new pseudo-labeling framework called Auxiliary Target Domain-Oriented Classifier (ATDOC). ATDOC alleviates the classifier bias by introducing an auxiliary classifier for target data only, to improve the quality of pseudo labels. Specifically, we employ the memory mechanism and develop two types of non-parametric classifiers, i.e. the nearest centroid classifier and neighborhood aggregation, without introducing any additional network parameters. Despite its simplicity in a pseudo classification objective, ATDOC with neighborhood aggregation significantly outperforms domain alignment techniques and prior SSL techniques on a large variety of DA benchmarks and even scare-labeled SSL tasks.

关键词： computer vision Handheld computers Semantics Focusing Semisupervised learning Benchmark testing pattern recognition

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Nearest Neighbor Matching for Deep Clustering

Nearest Neighbor Matching for Deep Clustering

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

作者： Dang, Zhiyuan Deng, Cheng Yang, Xu Wei, Kun Huang, Heng Xidian Univ Sch Elect Engn Xian 710071 Peoples R China JD Tech Beijing 100176 Peoples R China Univ Pittsburgh Dept Elect & Comp Engn Pittsburgh PA 15260 USA JD Finance Amer Corp Mountain View CA 94043 USA

ISBN: (纸本)9781665445092

Deep clustering gradually becomes an important branch in unsupervised learning methods. However, current approaches hardly take into consideration the semantic sample relationships that existed in both local and global features. In addition, since the deep features are updated on-the-fly, relying on these sample relationships may construct more semantically confident sample pairs, leading to inferior performance. To tackle this issue, we propose a method called Nearest Neighbor Matching (NNM) to match samples with their nearest neighbors from both local (batch) and global (overall) levels. Specifically, for the local level, we match the nearest neighbors based on batch embedded features, as for the global one, we match neighbors from overall embedded features. To keep the clustering assignment consistent in both neighbors and classes, we frame consistent loss and class contrastive loss for both local and global levels. Experimental results on three benchmark datasets demonstrate the superiority of our new model against state-of-the-art methods. Particularly on the STL-10 dataset, our method can achieve supervised performance. As for the CIFAR-100 dataset, our NNM leads 3.7% against the latest comparison method. Our code will be available at https://***/ZhiyuanDang/NNM.

关键词： computer vision Codes Semantics Artificial neural networks Benchmark testing pattern recognition Unsupervised learning

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