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Modeling Relationships in Referential Expressions with Compositional Modular Networks 30

Modeling Relationships in Referential Expressions with Compo...

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30th ieee/cvf conference on computer vision and pattern recognition (cvpr)

作者： Hu, Ronghang Rohrbach, Marcus Andreas, Jacob Darrell, Trevor Saenko, Kate Univ Calif Berkeley Berkeley CA 94720 USA Boston Univ Boston MA 02215 USA

ISBN: (纸本)9781538604571

People often refer to entities in an image in terms of their relationships with other entities. For example, the black cat sitting under the table refers to both a black cat entity and its relationship with another table entity. Understanding these relationships is essential for interpreting and grounding such natural language expressions. Most prior work focuses on either grounding entire referential expressions holistically to one region, or localizing relationships based on a fixed set of categories. In this paper we instead present a modular deep architecture capable of analyzing referential expressions into their component parts, identifying entities and relationships mentioned in the input expression and grounding them all in the scene. We call this approach Compositional Modular Networks (CMNs): a novel architecture that learns linguistic analysis and visual inference end-to-end. Our approach is built around two types of neural modules that inspect local regions and pairwise interactions between regions. We evaluate CMNs on multiple referential expression datasets, outperforming state-of-the-art approaches on all tasks.

关键词： Visualization Grounding Feature extraction Natural languages computer vision Cats

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Rethinking Class Relations: Absolute-relative Supervised and Unsupervised Few-shot Learning

Rethinking Class Relations: Absolute-relative Supervised and...

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

作者： Zhang, Hongguang Koniusz, Piotr Jian, Songlei Li, Hongdong Torr, Philip H. S. AMS Syst Engn Inst Beijing Peoples R China Australian Natl Univ Canberra ACT Australia Data61 CSIRO Sydney NSW Australia Univ Oxford Oxford England Natl Univ Def Technol Changsha Hunan Peoples R China

ISBN: (纸本)9781665445092

The majority of existing few-shot learning methods describe image relations with binary labels. However, such binary relations are insufficient to teach the network complicated real-world relations, due to the lack of decision smoothness. Furthermore, current few-shot learning models capture only the similarity via relation labels, but they are not exposed to class concepts associated with objects, which is likely detrimental to the classification performance due to underutilization of the available class labels. For instance, children learn the concept of tiger from a few of actual examples as well as from comparisons of tiger to other animals. Thus, we hypothesize that both similarity and class concept learning must be occurring simultaneously. With these observations at hand, we study the fundamental problem of simplistic class modeling in current few-shot learning methods. We rethink the relations between class concepts, and propose a novel Absolute-relative Learning paradigm to fully take advantage of label information to refine the image an relation representations in both supervised and unsupervised scenarios. Our proposed paradigm improves the performance of several state-of-the-art models on publicly available datasets.

关键词： Training Learning systems computer vision Protocols Annotations Animals Semantics

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φ-SfT: Shape-from-Template with a Physics-Based Deformation Model

φ-SfT: Shape-from-Template with a Physics-Based Deformation...

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

作者： Kairanda, Navami Tretschk, Edith Elgharib, Mohamed Theobalt, Christian Golyanik, Vladislav Max Planck Inst Informat SIC Saarbrucken Germany Saarland Univ SIC Saarbrucken Germany

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Shape-from-Template (SIT) methods estimate 3D surface deformations from a single monocular RGB camera while assuming a 3D state known in advance (a template). This is an important yet challenging problem due to the under-constrained nature of the monocular setting. Existing SfT techniques predominantly use geometric and simplified deformation models, which often limits their reconstruction abilities. In contrast to previous works, this paper proposes a new SfT approach explaining 2D observations through physical simulations accounting for forces and material properties. Our differentiable physics simulator regularises the surface evolution and optimises the material elastic properties such as bending coefficients, stretching stiffness and density. We use a differentiable renderer to minimise the dense reprojection error between the estimated 3D states and the input images and recover the deformation parameters using an adaptive gradient-based optimisation. For the evaluation, we record with an RGB-D camera challenging real surfaces exposed to physical forces with various material properties and textures. Our approach significantly reduces the 3D reconstruction error compared to multiple competing methods.

关键词： Deformable models Surface reconstruction Three-dimensional displays Cameras Rendering (computer graphics) Data models Surface texture

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Two-stage Network For Single Image Super-Resolution

Two-stage Network For Single Image Super-Resolution

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

作者： Han, Yuzhuo Du, Xiaobiao Yang, Zhi Dalian Univ Technol Dalian Peoples R China Jilin Univ Zhuhai Coll Zhuhai Peoples R China Dibaocheng Shanghai Med Imaging Technol Co Ltd Shanghai Peoples R China

ISBN: (纸本)9781665448994

The task of single-image super-resolution (SISR) is a highly inverse problem because it is very challenging to reconstruct rich details from blurred images. Most previous super-resolution (SR) methods based on the convolutional neural networks (CNN) tend to design more complex network structures to directly learn the mapping between low-resolution images and high-resolution images. However, this is not the best choice to blindly increase the network depth, because the performance improvement may not increase, but it will increase the computational cost. To solve this problem, we propose an effective method that learns high frequency information in high-resolution images to enhance the image reconstruction. In this work, we propose a two-stage network (TSN) to recover clear SR images. The proposed TSN firstly learns the high frequency information in high-resolution images, then learns how to transform to high-resolution images. A large number of experiments show that our TSN achieves satisfactory performance.

关键词： computer vision Inverse problems conferences Computational modeling Superresolution Transforms pattern recognition

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Towards Semantic Segmentation of Urban-Scale 3D Point Clouds: A Dataset, Benchmarks and Challenges

Towards Semantic Segmentation of Urban-Scale 3D Point Clouds...

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

作者： Hu, Qingyong Yang, Bo Khalid, Sheikh Xiao, Wen Trigoni, Niki Markham, Andrew Univ Oxford Oxford England Hong Kong Polytech Univ Hong Kong Peoples R China Sensat Ltd London England Newcastle Univ Newcastle Upon Tyne Tyne & Wear England

ISBN: (纸本)9781665445092

An essential prerequisite for unleashing the potential of supervised deep learning algorithms in the area of 3D scene understanding is the availability of large-scale and richly annotated datasets. However, publicly available datasets are either in relative small spatial scales or have limited semantic annotations due to the expensive cost of data acquisition and data annotation, which severely limits the development of fine-grained semantic understanding in the context of 3D point clouds. In this paper, we present an urban-scale photogrammetric point cloud dataset with nearly three billion richly annotated points, which is three times the number of labeled points than the existing largest photogrammetric point cloud dataset. Our dataset consists of large areas from three UK cities, covering about 7.6 km(2) of the city landscape. In the dataset, each 3D point is labeled as one of 13 semantic classes. We extensively evaluate the performance of state-of-the-art algorithms on our dataset and provide a comprehensive analysis of the results. In particular;we identify several key challenges towards urban-scale point cloud understanding. The dataset is available at https://***/QingyongHu/SensatUrban.

关键词： Deep learning computer vision Three-dimensional displays Costs Annotations Semantics Urban areas

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The Casual Conversations v2 Dataset A diverse, large benchmark for measuring fairness and robustness in audio/vision/speech models

The Casual Conversations v2 Dataset A diverse, large benchma...

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

作者： Porgali, Bilal Albiero, Vitor Ryda, Jordan Ferrer, Cristian Canton Hazirbas, Caner Meta AI Menlo Pk CA 94025 USA

ISBN: (纸本)9798350302493

This paper introduces a new large consent-driven dataset aimed at assisting in the evaluation of algorithmic bias and robustness of computer vision and audio speech models in regards to 11 attributes that are self-provided or labeled by trained annotators. The dataset includes 26,467 videos of 5,567 unique paid participants, with an average of almost 5 videos per person, recorded in Brazil, India, Indonesia, Mexico, Vietnam, Philippines, and the USA, representing diverse demographic characteristics. The participants agreed for their data to be used in assessing fairness of AI models and provided self-reported age, gender, language/dialect, disability status, physical adornments, physical attributes and geo-location information, while trained annotators labeled apparent skin tone using the Fitzpatrick Skin Type and Monk Skin Tone scales, and voice timbre. Annotators also labeled for different recording setups and per-second activity annotations.

关键词： Large dataset

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OW-DETR: Open-world Detection Transformer

OW-DETR: Open-world Detection Transformer

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

作者： Gupta, Akshita Narayan, Sanath Joseph, K. J. Khan, Salman Khan, Fahad Shahbaz Shah, Mubarak Incept Inst Artificial Intelligence Abu Dhabi U Arab Emirates IIT Hyderabad Hyderabad India Australian Natl Univ Canberra ACT Australia Mohamed Bin Zayed Univ Artificial Intelligence Abu Dhabi U Arab Emirates Linkoping Univ CVL Linkoping Sweden Univ Cent Florida Orlando FL 32816 USA

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Open-world object detection (OWOD) is a challenging computer vision problem, where the task is to detect a known set of object categories while simultaneously identifying unknown objects. Additionally, the model must incrementally learn new classes that become known in the next training episodes. Distinct from standard object detection, the OWOD setting poses significant challenges for generating quality candidate proposals on potentially unknown objects, separating the unknown objects from the background and detecting diverse unknown objects. Here, we introduce a novel end-to-end transformer-based framework, OW-DETR, for open-world object detection. The proposed OW-DETR comprises three dedicated components namely, attention-driven pseudo-labeling, novelty classification and objectness scoring to explicitly address the aforementioned OWOD challenges. Our OW-DETR explicitly encodes multi-scale contextual information, possesses less inductive bias, enables knowledge transfer from known classes to the unknown class and can better discriminate between unknown objects and background. Comprehensive experiments are performed on two benchmarks: MS-COCO and PASCAL VOC. The extensive ablations reveal the merits of our proposed contributions. Further, our model outperforms the recently introduced OWOD approach, ORE, with absolute gains ranging from 1.8% to 3.3% in terms of unknown recall on MS-COCO. In the case of incremental object detection, OW-DETR outperforms the state-of-theart for all settings on PASCAL VOC. Our code is available at https://***/akshitac8/OW-DETR.

关键词： Training computer vision Object detection Transformers pattern recognition Proposals Object recognition

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SMPLicit: Topology-aware Generative Model for Clothed People

SMPLicit: Topology-aware Generative Model for Clothed People

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

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

ISBN: (纸本)9781665445092

In this paper we introduce SMPLicit, a novel generative model to jointly represent body pose, shape and clothing geometry. In contrast to existing learning-based approaches that require training specific models for each type of garment, SMPLicit can represent in a unified manner different garment topologies (e.g. from sleeveless tops to hoodies and to open jackets), while controlling other properties like the garment size or tightness/looseness. We show our model to be applicable to a large variety of garments including T-shirts, hoodies, jackets, shorts, pants, skirts, shoes and even hair. The representation flexibility of SMPLicit builds upon an implicit model conditioned with the SMPL human body parameters and a learnable latent space which is semantically interpretable and aligned with the clothing attributes. The proposed model is fully differentiable, allowing for its use into larger end-to-end trainable systems. In the experimental section, we demonstrate SMPLicit can be readily used for fitting 3D scans and for 3D reconstruction in images of dressed people. In both cases we are able to go beyond state of the art, by retrieving complex garment geometries, handling situations with multiple clothing layers and providing a tool for easy outfit editing. To stimulate further research in this direction, we will make our code and model publicly available at http://***. edu/people/ecorona/smplicit/.

关键词： Geometry Training computer vision Three-dimensional displays Shape Computational modeling Clothing

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Efficient Video Instance Segmentation via Tracklet Query and Proposal

Efficient Video Instance Segmentation via Tracklet Query and...

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

作者： Wu, Jialian Yarram, Sudhir Liang, Hui Lan, Tian Yuan, Junsong Eledath, Jayan Medioni, Gerard SUNY Buffalo Buffalo NY 14260 USA Amazon Seattle WA USA

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Video Instance Segmentation (VIS) aims to simultaneously classify, segment, and track multiple object instances in videos. Recent clip-level VIS takes a short video clip as input each time showing stronger performance than frame-level VIS (tracking-by-segmentation), as more temporal context from multiple frames is utilized. Yet, most clip-level methods are neither end-to-end learnable nor real-time. These limitations are addressed by the recent VIS transformer (VisTR) [25] which performs VIS end-to-end within a clip. However, VisTR suffers from long training time due to its frame-wise dense attention. In addition, VisTR is not fully end-to-end learnable in multiple video clips as it requires a hand-crafted data association to link instance tracklets between successive clips. This paper proposes EfficientVIS, a Ally end-to-end framework with efficient training and inference. At the core are tracklet query and tracklet proposal that associate and segment regions-of-interest (RoIs) across space and time by an iterative query-video interaction. We further propose a correspondence learning that makes tracklets linking between clips end-to-end learnable. Compared to VisTR, EfficientVIS requires 15x fewer training epochs while achieving state-of-the-art accuracy on the YouTube-VIS benchmark. Meanwhile, our method enables whole video instance segmentation in a single end-to-end pass without data association at all.

关键词： Training computer vision Tracking Motion segmentation Streaming media Transformers Real-time systems

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C-CAM: Causal CAM for Weakly Supervised Semantic Segmentation on Medical Image

C-CAM: Causal CAM for Weakly Supervised Semantic Segmentatio...

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

作者： Chen, Zhang Tian, Zhiqiang Zhu, Jihua Li, Ce Du, Shaoyi Xi An Jiao Tong Univ Xian Peoples R China Lanzhou Univ Technol Xian Peoples R China

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Recently, many excellent weakly supervised semantic segmentation (WSSS) works are proposed based on class activation mapping (CAM). However, there are few works that consider the characteristics of medical images. In this paper, we find that there are mainly two challenges of medical images in WSSS: i) the boundary of object foreground and background is not clear;ii) the co-occurrence phenomenon is very severe in training stage. We thus propose a Causal CAM (C-CAM) method to overcome the above challenges. Our method is motivated by two cause-effect chains including category-causality chain and anatomy-causality chain. The category-causality chain represents the image content (cause) affects the category (effect). The anatomy-causality chain represents the anatomical structure (cause) affects the organ segmentation (effect). Extensive experiments were conducted on three public medical image data sets. Our C-CAM generates the best pseudo masks with the DSC of 77.26%, 80.34% and 78.15% on ProMRI, ACDC and CHAOS compared with other CAM-like methods. The pseudo masks of C-CAM are further used to improve the segmentation performance for organ segmentation tasks. Our C-CAM achieves DSC of 83.83% on ProMRI and DSC of 87.54% on ACDC, which outperforms state-of-the-art WSSS methods. Our code is available at https://***/Tian-lab/C-CAM.

关键词： Training Chaos Image segmentation computer vision Codes Semantics Anatomical structure

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