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StyleCineGAN: Landscape Cinemagraph Generation Using a Pre-trained StyleGAN

StyleCineGAN: Landscape Cinemagraph Generation Using a Pre-t...

引用

conference on computer vision and pattern recognition (cvpr)

作者： Jongwoo Choi Kwanggyoon Seo Amirsaman Ashtari Junyong Noh Visual Media Lab KAIST

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

We propose a method that can generate cinemagraphs automatically from a still landscape image using a pre-trained StyleGAN. Inspired by the success of recent un-conditional video generation, we leverage a powerful pre-trained image generator to synthesize high-quality cinema-graphs. Unlike previous approaches that mainly utilize the latent space of a pre-trained StyleGAN, our approach utilizes its deep feature space for both GAN inversion and cin-emagraph generation. Specifically, we propose multi-scale deep feature warping (MSDFW), which warps the intermediate features of a pre-trained StyleGAN at different resolutions. by using MSDFW, the generated cinemagraphs are of high resolution and exhibit plausible looping animation. We demonstrate the superiority of our method through user studies and quantitative comparisons with state-of-the-art cinemagraph generation methods and a video generation method that uses a pre-trained StyleGAN.

关键词： Training computer vision Semantics Animation Generators pattern recognition Spatial resolution

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Promptable Behaviors: Personalizing Multi-Objective Rewards from Human Preferences

Promptable Behaviors: Personalizing Multi-Objective Rewards ...

引用

conference on computer vision and pattern recognition (cvpr)

作者： Minyoung Hwang Luca Weihs Chanwoo Park Kimin Lee Aniruddha Kembhavi Kiana Ehsani PRIOR @ Allen Institute for AI Massachusetts Institute of Technology Korea Advanced Institute of Science and Technology

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

Customizing robotic behaviors to be aligned with di-verse human preferences is an underexplored challenge in the field of embodied AI. In this paper, we present Prompt-able Behaviors, a novel framework that facilitates efficient personalization of robotic agents to diverse human prefer-ences in complex environments. We use multi-objective re-inforcement learning to train a single policy adaptable to a broad spectrum of preferences. We introduce three distinct methods to infer human preferences by leveraging different types of interactions: (1) human demonstrations, (2) prefer-ence feedback on trajectory comparisons, and (3) language instructions. We evaluate the proposed method in person-alized object-goal navigation and flee navigation tasks in ProcTHOR [18] and RoboTHOR [17], demonstrating the ability to prompt agent behaviors to satisfy human prefer-ences in various scenarios. Project page: https://***

关键词： computer vision Navigation Prediction methods Behavioral sciences Trajectory pattern recognition

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Uncertainty-Aware Camera Pose Estimation from Points and Lines

Uncertainty-Aware Camera Pose Estimation from Points and Lin...

引用

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

作者： Vakhitov, Alexander Ferraz Colomina, Luis Agudo, Antonio Moreno-Noguer, Francesc SLAMCore Ltd London England Kognia Sports Intelligence Barcelona Spain CSIC UPC Inst Robot & Informat Ind Barcelona Spain

ISBN: (纸本)9781665445092

Perspective-n-Point-and-Line (PnPL) algorithms aim at fast, accurate, and robust camera localization with respect to a 3D model from 2D-3D feature correspondences, being a major part of modern robotic and AR/VR systems. Current point-based pose estimation methods use only 2D feature detection uncertainties, and the line-based methods do not take uncertainties into account. In our setup, both 3D coordinates and 2D projections of the features are considered uncertain. We propose PnP(L) solvers based on EPnP [20] and DLS [14] for the uncertainty-aware pose estimation. We also modify motion-only bundle adjustment to take 3D uncertainties into account. We perform exhaustive synthetic and real experiments on two different visual odometry datasets. The new PnP(L) methods outperform the state-of-the-art on real data in isolation, showing an increase in mean translation accuracy by 18% on a representative subset of KITTI, while the new uncertain refinement improves pose accuracy for most of the solvers, e.g. decreasing mean translation error for the EPnP by 16% compared to the standard refinement on the same dataset. The code is available at https://***/uncertain-pnp/.

关键词： Solid modeling Uncertainty Feature detection Pose estimation Robot vision systems Cameras Robustness

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ViPNAS: Efficient Video Pose Estimation via Neural Architecture Search

ViPNAS: Efficient Video Pose Estimation via Neural Architect...

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

作者： Xu, Lumin Guan, Yingda Jin, Sheng Liu, Wentao Qian, Chen Luo, Ping Ouyang, Wanli Wang, Xiaogang Chinese Univ Hong Kong Hong Kong Peoples R China SenseTime Res Beijing Peoples R China Univ Hong Kong Hong Kong Peoples R China Tetras AI Beijing Peoples R China Univ Sydney Camperdown NSW Australia

ISBN: (纸本)9781665445092

Human pose estimation has achieved significant progress in recent years. However, most of the recent methods focus on improving accuracy using complicated models and ignoring real-time efficiency. To achieve a better trade-off between accuracy and efficiency, we propose a novel neural architecture search (NAS) method, termed ViP-NAS, to search networks in both spatial and temporal levels for fast online video pose estimation. In the spatial level, we carefully design the search space with five different dimensions including network depth, width, kernel size, group number, and attentions. In the temporal level, we search from a series of temporal feature fusions to optimize the total accuracy and speed across multiple video frames. To the best of our knowledge, we are the first to search for the temporal feature fusion and automatic computation allocation in videos. Extensive experiments demonstrate the effectiveness of our approach on the challenging COCO2017 and PoseTrack2018 datasets. Our discovered model family, S-ViPNAS and T-ViPNAS, achieve significantly higher inference speed (CPU real-time) without sacrificing the accuracy compared to the previous state-of-the-art methods.

关键词： Training computer vision Costs Pose estimation computer architecture Streaming media Real-time systems

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Improved Image Matting via Real-time User Clicks and Uncertainty Estimation

Improved Image Matting via Real-time User Clicks and Uncerta...

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

作者： Wei, Tianyi Chen, Dongdong Zhou, Wenbo Liao, Jing Zhao, Hanqing Zhang, Weiming Yu, Nenghai Univ Sci & Technol China Hefei Anhui Peoples R China Microsoft Cloud AI Hefei Peoples R China City Univ Hong Kong Hong Kong Peoples R China

ISBN: (纸本)9781665445092

Image matting is a fundamental and challenging problem in computer vision and graphics. Most existing matting methods leverage a user-supplied trimap as an auxiliary input to produce good alpha matte. However, obtaining high quality trimap itself is arduous, thus restricting the application of these methods. Recently, some trimap free methods have emerged, however, the matting quality is still far behind the trimap-based methods. The main reason is that, without the trimap guidance in some cases, the target network is ambiguous about which is the foreground target. In fact, choosing the foreground is a subjective procedure and depends on the user's intention. To this end, this paper proposes an improved deep image matting framework which is trimap free and only needs several user click interactions to eliminate the ambiguity. Moreover, we introduce a new uncertainty estimation module that can predict which parts need polishing and a following local refinement module. Based on the computation budget, users can choose how many local parts to improve with the uncertainty guidance. Quantitative and qualitative results show that our method performs better than existing trimap free methods and comparably to state-of-the-art trimap-based methods with minimal user effort.

关键词： Graphics computer vision Uncertainty Estimation Real-time systems pattern recognition

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Robust Instance Segmentation through Reasoning about Multi-Object Occlusion

Robust Instance Segmentation through Reasoning about Multi-O...

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

作者： Yuan, Xiaoding Kortylewski, Adam Sun, Yihong Yuille, Alan Tongji Univ Shanghai Peoples R China Johns Hopkins Univ Baltimore MD 21218 USA

ISBN: (纸本)9781665445092

Analyzing complex scenes with Deep Neural Networks is a challenging task, particularly when images contain multiple objects that partially occlude each other. Existing approaches to image analysis mostly process objects independently and do not take into account the relative occlusion of nearby objects. In this paper, we propose a deep network for multi-object instance segmentation that is robust to occlusion and can be trained from bounding box supervision only. Our work builds on Compositional Networks, which learn a generative model of neural feature activations to locate occluders and to classify objects based on their non-occluded parts. We extend their generative model to include multiple objects and introduce a framework for efficient inference in challenging occlusion scenarios. In particular, we obtain feed-forward predictions of the object classes and their instance and occluder segmentations. We introduce an Occlusion Reasoning Module (ORM) that locates erroneous segmentations and estimates the occlusion order to correct them. The improved segmentation masks are, in turn, integrated into the network in a top-down manner to improve the image classification. Our experiments on the KITTI INStance dataset (KINS) and a synthetic occlusion dataset demonstrate the effectiveness and robustness of our model at multi-object instance segmentation under occlusion. Code is publically available at https://***/XD7479/Multi-Object-Occlusion.

关键词： Deep learning Image segmentation computer vision Codes computer architecture Cognition Robustness

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Connecting vision and Language with Video Localized Narratives

Connecting Vision and Language with Video Localized Narrativ...

引用

conference on computer vision and pattern recognition (cvpr)

作者： Paul Voigtlaender Soravit Changpinyo Jordi Pont-Tuset Radu Soricut Vittorio Ferrari Google Research

We propose Video Localized Narratives, a new form of multimodal video annotations connecting vision and language. In the original Localized Narratives [36], annotators speak and move their mouse simultaneously on an image, thus grounding each word with a mouse trace segment. However, this is challenging on a video. Our new protocol empowers annotators to tell the story of a video with Localized Narratives, capturing even complex events involving multiple actors interacting with each other and with several passive objects. We annotated 20k videos of the OVIS, UVO, and Oops datasets, totalling 1.7M words. Based on this data, we also construct new benchmarks for the video narrative grounding and video question answering tasks, and provide reference results from strong baseline models. Our annotations are available at https://***/video-localized-narratives/

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Atom-Level Optical Chemical Structure recognition with Limited Supervision

Atom-Level Optical Chemical Structure Recognition with Limit...

引用

conference on computer vision and pattern recognition (cvpr)

作者： Martijn Oldenhof Edward De Brouwer Adam Arany Yves Moreau ESAT - STADIUS KU Leuven Belgium Yale University USA

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

Identifying the chemical structure from a graphical representation, or image, of a molecule is a challenging pattern recognition task that would greatly benefit drug development. Yet, existing methods for chemical structure recognition do not typically generalize well, and show diminished effectiveness when confronted with domains where data is sparse, or costly to generate, such as hand-drawn molecule images. To address this limitation, we propose a new chemical structure recognition tool that delivers state-of-the-art performance and can adapt to new domains with a limited number of data samples and supervision. Unlike previous approaches, our method provides atom-level localization, and can therefore segment the image into the different atoms and bonds. Our model is the first model to perform OCSR with atom-level entity detection with only SMILES supervision. Through rigorous and extensive benchmarking, we demonstrate the preeminence of our chemical structure recognition approach in terms of data efficiency, accuracy, and atom-level entity prediction.

关键词： Training Location awareness Adaptation models Image recognition Atom optics Predictive models Optical imaging

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Refine Myself by Teaching Myself : Feature Refinement via Self-Knowledge Distillation

Refine Myself by Teaching Myself : Feature Refinement via Se...

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

作者： Ji, Mingi Shin, Seungjae Hwang, Seunghyun Park, Gibeom Moon, Il-Chul Korea Adv Inst Sci & Technol KAIST Daejeon South Korea Looko Inc Los Angeles CA USA Summary AI Seoul South Korea

ISBN: (纸本)9781665445092

Knowledge distillation is a method of transferring the knowledge from a pretrained complex teacher model to a student model, so a smaller network can replace a large teacher network at the deployment stage. To reduce the necessity of training a large teacher model, the recent literatures introduced a self-knowledge distillation, which trains a student network progressively to distill its own knowledge without a pretrained teacher network. While Selfknowledge distillation is largely divided into a data augmentation based approach and an auxiliary network based approach, the data augmentation approach looses its local information in the augmentation process, which hinders its applicability to diverse vision tasks, such as semantic segmentation. Moreover, these knowledge distillation approaches do not receive the refined feature maps, which are prevalent in the object detection and semantic segmentation community. This paper proposes a novel self-knowledge distillation method, Feature Refinement via Self-Knowledge Distillation (FRSKD), which utilizes an auxiliary self-teacher network to transfer a refined knowledge for the classifier network. Our proposed method, FRSKD, can utilize both soft label and feature-map distillations for the self-knowledge distillation. Therefore, FRSKD can be applied to classification, and semantic segmentation, which emphasize preserving the local information. We demonstrate the effectiveness of FRSKD by enumerating its performance improvements in diverse tasks and benchmark datasets. The implemented code is available at https://***/MingiJi/FRSKD.

关键词： Knowledge engineering Training computer vision Codes Semantics Neural networks Object detection

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Retrieval-Augmented Layout Transformer for Content-Aware Layout Generation

Retrieval-Augmented Layout Transformer for Content-Aware Lay...

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conference on computer vision and pattern recognition (cvpr)

作者： Daichi Horita Naoto Inoue Kotaro Kikuchi Kota Yamaguchi Kiyoharu Aizawa The University of Tokyo CyberAgent

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

Content-aware graphic layout generation aims to automatically arrange visual elements along with a given content, such as an e-commerce product image. In this paper, we argue that the current layout generation approaches suffer from the limited training data for the high-dimensional layout structure. We show that a simple retrieval augmentation can significantly improve the generation quality. Our model, which is named Retrieval-Augmented Layout Transformer (RALF),retrieves nearest neighbor layout examples based on an input image and feeds these results into an autoregressive generator. Our model can apply retrieval augmentation to various controllable generation tasks and yield high-quality layouts within a unified architecture. Our extensive experiments show that RALF successfully generates content-aware layouts in both constrained and unconstrained settings and significantly outperforms the baselines. 1

关键词： Visualization computer vision Layout Training data computer architecture Transformers Generators

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