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Inverse Cooking: Recipe Generation from Food Images 32

Inverse Cooking: Recipe Generation from Food Images

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32nd IEEE/CVF Conference on Computer Vision and Pattern recognition (CVPR)

作者： Salvador, Amaia Drozdzal, Michal Giro-i-Nieto, Xavier Romero, Adriana Univ Politecn Cataluna Barcelona Spain Facebook AI Res Menlo Pk CA USA

ISBN: (纸本)9781728132938

People enjoy food photography because they appreciate food. Behind each meal there is a story described in a complex recipe and, unfortunately, by simply looking at a food image we do not have access to its preparation process. Therefore, in this paper we introduce an inverse cooking system that recreates cooking recipes given food images. Our system predicts ingredients as sets by means of a novel architecture, modeling their dependencies without imposing any order, and then generates cooking instructions by attending to both image and its inferred ingredients simultaneously. We extensively evaluate the whole system on the large-scale Recipe1M dataset and show that (1) we improve performance w.r.t. previous baselines for ingredient prediction;(2) we are able to obtain high quality recipes by leveraging both image and ingredients;(3) our system is able to produce more compelling recipes than retrieval-based approaches according to human judgment. We make code and models publicly available(1).

关键词： Categorization Deep Learning recognition: detection Retrieval Vision + Language Vision Applications and Systems

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AOGNets: Compositional Grammatical Architectures for Deep Learning 32

AOGNets: Compositional Grammatical Architectures for Deep Le...

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32nd IEEE/CVF Conference on Computer Vision and Pattern recognition (CVPR)

作者： Li, Xilai Song, Xi Wu, Tianfu North Carolina State Univ Dept ECE Raleigh NC 27695 USA North Carolina State Univ Visual Narrat Initiat Raleigh NC 27695 USA

ISBN: (纸本)9781728132938

Neural architectures are the foundation for improving performance of deep neural networks (DNNs). This paper presents deep compositional grammatical architectures which harness the best of two worlds: grammar models and DNNs. The proposed architectures integrate compositionality and reconfigurability of the former and the capability of learning rich features of the latter in a principled way. We utilize AND-OR Grammar (AOG) [53, 71, 70] as network generator in this paper and call the resulting networks AOGNets. An AOGNet consists of a number of stages each of which is composed of a number of AOG building blocks. An AOG building block splits its input feature map into N groups along feature channels and then treat it as a sentence of N words. It then jointly realizes a phrase structure grammar and a dependency grammar in bottom-up parsing the "sentence" for better feature exploration and reuse. It provides a unified framework for the best practices developed in state-of-the-art DNNs. In experiments, AOGNet is tested in the ImageNet-1K classification benchmark and the MS-COCO object detection and segmentation benchmark. In ImageNet-1K, AOGNet obtains better performance than ResNet [21] and most of its variants, ResNeXt [63] and its attention based variants such as SENet [24], DenseNe[26] and DualPathNet [6]. AOGNet also obtains the best model interpretability score using network dissection [3]. AOGNet further shows better potential in adversarial defense. In MS-COCO, AOGNet obtains better performance than the ResNet and ResNeXt backbones in Mask R-CNN [20].

关键词： Categorization Deep Learning recognition: detection Representation Learning Retrieval

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Part-regularized Near-duplicate Vehicle Re-identification 32

Part-regularized Near-duplicate Vehicle Re-identification

引用

32nd IEEE/CVF Conference on Computer Vision and Pattern recognition (CVPR)

作者： He, Bing Li, Jia Zhao, Yifan Tian, Yonghong Beihang Univ SCSE State Key Lab Virtual Real Technol & Syst Beijing Peoples R China Peking Univ Sch EE&CS Natl Engn Lab Video Technol Beijing Peoples R China Peng Cheng Lab Shenzhen Peoples R China Beijing Adv Innovat Ctr Big Data & Brain Comp Beijing Peoples R China

ISBN: (纸本)9781728132938

Vehicle re-identification (Re-ID) has been attracting more interests in computer vision owing to its great contributions in urban surveillance and intelligent transportation. With the development of deep learning approaches, vehicle Re-ID still faces a near-duplicate challenge, which is to distinguish different instances with nearly identical appearances. Previous methods simply rely on the global visual features to handle this problem. In this paper, we proposed a simple but efficient part-regularized discriminative feature preserving method which enhances the perceptive ability of subtle discrepancies. We further develop a novel framework to integrate part constrains with the global Re-ID modules by introducing an detection branch. Our framework is trained end-to-end with combined local and global constrains. Specially, without the part-regularized local constrains in inference step, our Re-ID network outperforms the state-of-the-art method by a large margin on large benchmark datasets VehicleID and VeRi-776.

关键词： Categorization Deep Learning recognition: detection Representation Learning Retrieval

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R²GAN: Cross-modal Recipe Retrieval with Generative Adversarial Network 32

R<SUP>2</SUP>GAN: Cross-modal Recipe Retrieval with Generati...

引用

32nd IEEE/CVF Conference on Computer Vision and Pattern recognition (CVPR)

作者： Zhu, Bin Ngo, Chong-Wah Chen, Jingjing Hao, Yanbin City Univ Hong Kong Hong Kong Peoples R China Natl Univ Singapore Singapore Singapore

ISBN: (纸本)9781728132938

Representing procedure text such as recipe for cross modal retrieval is inherently a difficult problem, not mentioning to generate image from recipe for visualization. This paper studies a new version of GAN, named Recipe Retrieval Generative Adversarial Network (R(2)GAN), to explore the feasibility of generating image from procedure text for retrieval problem. The motivation of using GAN is twofold: learning compatible cross-modal features in an adversarial way, and explanation of search results by showing the images generated from recipes. The novelty of R(2)GAN comes from architecture design, specifically a GAN with one generator and dual discriminators is used, which makes the generation of image from recipe a feasible idea. Furthermore, empowered by the generated images, a two-level ranking loss in both embedding and image spaces are considered. These add-ons not only result in excellent retrieval performance, but also generate close-to-realistic food images useful for explaining ranking of recipes. On recipe 1M dataset, R(2)GAN demonstrates high scalability to data size, outperforms all the existing approaches, and generates images intuitive for human to interpret the search results.

关键词： Categorization Image and Video Synthesis recognition: detection Representation Learning Retrieval Vision + Language

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PartNet: A Large-scale Benchmark for Fine-grained and Hierarchical Part-level 3D Object Understanding 32

PartNet: A Large-scale Benchmark for Fine-grained and Hierar...

引用

32nd IEEE/CVF Conference on Computer Vision and Pattern recognition (CVPR)

作者： Mo, Kaichun Zhu, Shilin Chang, Angel X. Yi, Li Tripathi, Subarna Guibas, Leonidas J. Su, Hao Stanford Univ Stanford CA 94305 USA Univ Calif San Diego La Jolla CA USA Simon Fraser Univ Burnaby BC Canada Intel AI Lab San Diego CA USA Facebook AI Res Menlo Pk CA USA

ISBN: (纸本)9781728132938

We present PartNet: a consistent, large-scale dataset of 3D objects annotated with fine-grained, instance-level, and hierarchical 3D part information. Our dataset consists of 573,585 part instances over 26,671 3D models covering 24 object categories. This dataset enables and serves as a catalyst for many tasks such as shape analysis, dynamic 3D scene modeling and simulation, affordance analysis, and others. Using our dataset, we establish three benchmarking tasks for evaluating 3D part recognition: fine-grained semantic segmentation, hierarchical semantic segmentation, and instance segmentation. We benchmark four state-of-the-art 3D deep learning algorithms for fine-grained semantic segmentation and three baseline methods for hierarchical semantic segmentation. We also propose a baseline method for part instance segmentation and demonstrate its superior performance over existing methods.

关键词： Big Data Categorization Datasets and Evaluation Deep Learning Large Scale Methods recognition: detection Retrieval S

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Grid R-CNN 32

Grid R-CNN

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32nd IEEE/CVF Conference on Computer Vision and Pattern recognition (CVPR)

作者： Lu, Xin Li, Buyu Yue, Yuxin Li, Quanquan Yan, Junjie SenseTime Res Hong Kong Peoples R China Chinese Univ Hong Kong Hong Kong Peoples R China Beihang Univ Beijing Peoples R China

ISBN: (纸本)9781728132938

This paper proposes a novel object detection framework named Grid R-CNN, which adopts a grid guided localization mechanism for accurate object detection. Different from the traditional regression based methods, the Grid R-CNN captures the spatial information explicitly and enjoys the position sensitive property of fully convolutional architecture. Instead of using only two independent points, we design a multi-point supervision formulation to encode more clues in order to reduce the impact of inaccurate prediction of specific points. To take the full advantage of the correlation of points in a grid, we propose a two-stage information fusion strategy to fuse feature maps of neighbor grid points. The grid guided localization approach is easy to be extended to different state-of-the-art detection frameworks. Grid R-CNN leads to high quality object localization, and experiments demonstrate that it achieves a 4.1% AP gain at IoU=0.8 and a 10.0% AP gain at IoU=0.9 on COCO benchmark compared to Faster R-CNN with Res50 backbone and FPN architecture.

关键词： Categorization Deep Learning recognition: detection Retrieval

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Action recognition from Single Timestamp Supervision in Untrimmed Videos 32

Action Recognition from Single Timestamp Supervision in Untr...

引用

IEEE/CVF Conference on Computer Vision and Pattern recognition (CVPR)

作者： Moltisanti, Davide Fidler, Sanja Damen, Dima Univ Bristol Visual Informat Lab Bristol Avon England Univ Toronto NVIDIA Vector Inst Toronto ON Canada

ISBN: (纸本)9781728132938

Recognising actions in videos relies on labelled supervision during training, typically the start and end times of each action instance. This supervision is not only subjective, but also expensive to acquire. Weak video-level supervision has been successfully exploited for recognition in untrimmed videos, however it is challenged when the number of different actions in training videos increases. We propose a method that is supervised by single timestamps located around each action instance, in untrimmed videos. We replace expensive action bounds with sampling distributions initialised from these timestamps. We then use the classifier's response to iteratively update the sampling distributions. We demonstrate that these distributions converge to the location and extent of discriminative action segments. We evaluate our method on three datasets for fine-grained recognition, with increasing number of different actions per video, and show that single timestamps offer a reasonable compromise between recognition performance and labelling effort, performing comparably to full temporal supervision. Our update method improves top-I test accuracy by up to 5.4%. across the evaluated datasets.

关键词： Action recognition Categorization recognition: detection Retrieval Video Analytics

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SSN: Learning Sparse Switchable Normalization via SparsestMax 32

SSN: Learning Sparse Switchable Normalization via SparsestMa...

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32nd IEEE/CVF Conference on Computer Vision and Pattern recognition (CVPR)

作者： Shao, Wenqi Meng, Tianjian Li, Jingyu Zhang, Ruimao Li, Yudian Wang, Xiaogang Luo, Ping Chinese Univ Hong Kong CHUK SenseTime Joint Lab Hong Kong Peoples R China SenseTime Res Hong Kong Peoples R China Univ Pittsburgh Pittsburgh PA 15260 USA

ISBN: (纸本)9781728132938

Normalization methods improve both optimization and generalization of ConvNets. To further boost performance, the recently-proposed switchable normalization (SN) provides a new perspective for deep learning: it learns to select different normalizers for different convolution layers of a ConvNet. However, SN uses softmax function to learn importance ratios to combine normalizers, leading to redundant computations compared to a single normalizer. This work addresses this issue by presenting Sparse Switchable Normalization (SSN) where the importance ratios are constrained to be sparse. Unlike l(1) and l(0) constraints that impose difficulties in optimization, we turn this constrained optimization problem into feed-forward computation by proposing SparsestMax, which is a sparse version of softmax. SSN has several appealing properties. (1) It inherits all benefits from SN such as applicability in various tasks and robustness to a wide range of batch sizes. (2) It is guaranteed to select only one normalizer for each normalization layer, avoiding redundant computations. (3) SSN can be transferred to various tasks in an end-to-end manner. Extensive experiments show that SSN outperforms its counterparts on various challenging benchmarks such as ImageNet, Cityscapes, ADE20K, and Kinetics.

关键词： Action recognition Categorization Deep Learning Optimization Methods recognition: detection Retrieval Vision

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VRSTC: Occlusion-Free Video Person Re-Identification 32

VRSTC: Occlusion-Free Video Person Re-Identification

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32nd IEEE/CVF Conference on Computer Vision and Pattern recognition (CVPR)

作者： Hou, Ruibing Ma, Bingpeng Chang, Hong Gu, Xinqian Shan, Shiguang Chen, Xilin Chinese Acad Sci Key Lab Intelligent Informat Proc Inst Comp Technol CAS Beijing 100190 Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China CAS Ctr Excellence Brain Sci & Intelligence Techn Shanghai 200031 Peoples R China

ISBN: (纸本)9781728132938

Video person re-identification (re-ID) plays an important role in surveillance video analysis. However, the performance of video re-ID degenerates severely under partial occlusion. In this paper, we propose a novel network, called Spatio-Temporal Completion network (STCnet), to explicitly handle partial occlusion problem. Different from most previous works that discard the occluded frames, STCnet can recover the appearance of the occluded parts. For one thing, the spatial structure of a pedestrian frame can be used to predict the occluded body parts from the unoccluded body parts of this frame. For another, the temporal patterns of pedestrian sequence provide important clues to generate the contents of occluded parts. With the spatio-temporal information, STCnet can recover the appearance for the occluded parts, which could be leveraged with those unoccluded parts for more accurate video re-ID. By combining a re-ID network with STCnet, a video re-ID framework robust to partial occlusion (VRSTC) is proposed. Experiments on three challenging video re-ID databases demonstrate that the proposed approach outperforms the state-of-the-arts.

关键词： Categorization recognition: detection Retrieval

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Learning Spatial Common Sense with Geometry-Aware Recurrent Networks 32

Learning Spatial Common Sense with Geometry-Aware Recurrent ...

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32nd IEEE/CVF Conference on Computer Vision and Pattern recognition (CVPR)

作者： Tung, Hsiao-Yu Fish Cheng, Ricson Fragkiadaki, Katerina Carnegie Mellon Univ Pittsburgh PA 15213 USA Uber Adv Technol Grp Pittsburgh PA USA

ISBN: (纸本)9781728132938

We integrate two powerful ideas, geometry and deep visual representation learning, into recurrent network architectures for mobile visual scene understanding. The proposed networks learn to "lift" 2D visual features and integrate them over time into latent 3D feature maps of the scene. They are equipped with differentiable geometric operations, such as projection, unprojection, egomotion estimation and stabilization, in order to compute a geometrically-consistent mapping between the world scene and their 3D latent feature space. We train the proposed architectures to predict novel image views given short frame sequences as input. Their predictions strongly generalize to scenes with a novel number of objects, appearances and configurations, and greatly outperform predictions of previous works that do not consider egomotion stabilization or a space-aware latent feature space. We train the proposed architectures to detect and segment objects in 3D, using the latent 3D feature map as input-as opposed to 2D feature maps computed from video frames. The resulting detections are permanent: they continue to exist even when an object gets occluded or leaves the field of view. Our experiments suggest the proposed space-aware latent feature arrangement and egomotion-stabilized convolutions are essential architectural choices for spatial common sense to emerge in artificial embodied visual agents.

关键词： 3D from Single Image Categorization recognition: detection Representation Learning Retrieval Scene Analysis and Understa

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