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Learned Scanpaths Aid Blind Panoramic Video Quality Assessment

Learned Scanpaths Aid Blind Panoramic Video Quality Assessme...

引用

conference on computer vision and pattern recognition (cvpr)

作者： Kanglong Fan Wen Wen Mu Li Yifan Peng Kede Ma City University of Hong Kong Harbin Institute of Technology Shenzhen The University of Hong Kong

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

Panoramic videos have the advantage of providing an immersive and interactive viewing experience. Nevertheless, their spherical nature gives rise to various and uncertain user viewing behaviors, which poses significant challenges for panoramic video quality assessment (PVQA). In this work, we propose an end-to-end optimized, blind PVQA method with explicit modeling of user viewing patterns through visual scanpaths. Our method consists of two modules: a scanpath generator and a quality assessor. The scanpath generator is initially trained to predict future scanpaths by minimizing their expected code length and then jointly optimized with the quality assessor for quality prediction. Our blind PVQA method enables direct quality assessment of panoramic images by treating them as videos composed of identical frames. Experiments on three public panoramic image and video quality datasets, encompassing both synthetic and authentic distortions, validate the superiority of our blind PVQA model over existing methods.

关键词： Training Visualization computer vision Distortion Generators Quality assessment pattern recognition

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GenHowTo: Learning to Generate Actions and State Transformations from Instructional Videos

GenHowTo: Learning to Generate Actions and State Transformat...

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conference on computer vision and pattern recognition (cvpr)

作者： Tomáš Souček Dima Damen Michael Wray Ivan Laptev Josef Sivic CIIRC CTU Czech Institute of Informatics Robotics and Cybernetics at the Czech Technical University in Prague. University of Bristol MBZUAI Mohamed bin Zayed University of Artificial Intelligence.

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

We address the task of generating temporally consistent and physically plausible images of actions and object state transformations. Given an input image and a text prompt describing the targeted transformation, our generated images preserve the environment and transform objects in the initial image. Our contributions are threefold. First, we leverage a large body of instructional videos and automatically mine a dataset of triplets of consecutive frames corresponding to initial object states, actions, and resulting object transformations. Second, equipped with this data, we develop and train a conditioned diffusion model dubbed GenHowTo. Third, we evaluate GenHowTo on a variety of objects and actions and show superior performance compared to existing methods. In particular, we introduce a quantitative evaluation where GenHowTo achieves 88% and 74% on seen and unseen interaction categories, respectively, outperforming prior work by a large margin.

关键词： computer vision Computational modeling Transforms Diffusion models pattern recognition Videos

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Context-Based and Diversity-Driven Specificity in Compositional Zero-Shot Learning

Context-Based and Diversity-Driven Specificity in Compositio...

引用

conference on computer vision and pattern recognition (cvpr)

作者： Yun Li Zhe Liu Hang Chen Lina Yao CSIRO‘s Data61 Bytedance Ltd. Snap Inc.

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

Compositional Zero-Shot Learning (CZSL) aims to recognize unseen attribute-object pairs based on a limited set of observed examples. Current CZSL methodologies, despite their advancements, tend to neglect the distinct specificity levels present in attributes. For instance, given images of sliced strawberries, they may fail to prioritize ‘Sliced-Strawberry’ over a generic ‘Red-Strawberry’, despite the former being more informative. They also suffer from ballooning search space when shifting from Close-World (CW) to Open-World (OW) CZSL. To address the issues, we introduce the Context-based and Diversity-driven Specificity learning framework for CZSL (CDS-CZSL). Our framework evaluates the specificity of attributes by considering the diversity of objects they apply to and their related context. This novel approach allows for more accurate predictions by emphasizing specific attribute-object pairs and improves composition filtering in OW-CZSL. We conduct experiments in both CW and OW scenarios, and our model achieves state-of-the-art results across three datasets.

关键词： computer vision Accuracy Zero-shot learning Filtering Computational modeling pattern recognition

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Brain Decodes Deep Nets

Brain Decodes Deep Nets

引用

conference on computer vision and pattern recognition (cvpr)

作者： Huzheng Yang James Gee Jianbo Shi University of Pennsylvania

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

We developed a tool for visualizing and analyzing large pre-trained vision models by mapping them onto the brain, thus exposing their hidden inside. Our innovation arises from a surprising usage of brain encoding: predicting brain fMRI measurements in response to images. We report two findings. First, explicit mapping between the brain and deep-network features across dimensions of space, layers, scales, and channels is crucial. This mapping method, Fac-torTopy, is plug-and-play for any deep-network; with it, one can paint a picture of the network onto the brain (liter-ally!). Second, our visualization shows how different training methods matter: they lead to remarkable differences in hierarchical organization and scaling behavior, growing with more data or network capacity. It also provides in-sight into fine-tuning: how pre-trained models change when adapting to small datasets. We found brain-like hierarchi-cally organized network suffer less from catastrophic for-getting after fine-tuned.

关键词： Training Technological innovation computer vision Image coding Computational modeling Data visualization Organizations

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CapsuleRRT: Relationships-aware Regression Tracking via Capsules

CapsuleRRT: Relationships-aware Regression Tracking via Caps...

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

作者： Ma, Ding Wu, Xiangqian Harbin Inst Technol Sch Comp Sci & Technol Harbin Peoples R China

ISBN: (纸本)9781665445092

Regression tracking has gained more and more attention thanks to its easy-to-implement characteristics, while existing regression trackers rarely consider the relationships between the object parts and the complete object. This would ultimately result in drift from the target object when missing some parts of the target object. Recently, Capsule Network (CapsNet) has shown promising results for image classification benefits from its part-object relationships mechanism, while CapsNet is known for its high computational demand even when carrying out simple tasks. Therefore, a primitive adaptation of CapsNet to regression tracking does not make sense, since this will seriously affect speed of a tracker. To solve these problems, we first explore the spatial-temporal relationships endowed by the CapsNet for regression tracking. The entire regression framework, dubbed CapsuleRRT, consists of three parts. One is S-Caps, which captures the spatial relationships between the parts and the object. Meanwhile, a T-Caps module is designed to exploit the temporal relationships within the target. The response of the target is obtained by STCaps Learning. Further, a priorguided capsule routing algorithm is proposed to generate more accurate capsule assignments for subsequent frames. Apart from this, the heavy computation burden in CapsNet is addressed with a knowledge distillation pose matrix compression strategy that exploits more tight and discriminative representation with few samples. Extensive experimental results show that CapsuleRRT performs favorably against state-of-the-art methods in terms of accuracy and speed.

关键词： computer vision Target tracking Meetings Routing pattern recognition Task analysis Image classification

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vision Transformers are Good Mask Auto-Labelers

Vision Transformers are Good Mask Auto-Labelers

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conference on computer vision and pattern recognition (cvpr)

作者： Shiyi Lan Xitong Yang Zhiding Yu Zuxuan Wu Jose M. Alvarez Anima Anandkumar NVIDIA Meta AI FAIR Fudan University Caltech

We propose Mask Auto-Labeler (MAL), a high-quality Transformer-based mask auto-labeling framework for instance segmentation using only box annotations. MAL takes box-cropped images as inputs and conditionally generates their mask pseudo-labels. We show that vision Transformers are good mask auto-labelers. Our method significantly reduces the gap between auto-labeling and human annotation regarding mask quality. Instance segmentation models trained using the MAL-generated masks can nearly match the performance of their fully-supervised counterparts, retaining up to 97.4% performance of fully supervised models. The best model achieves 44.1% mAP on COCO instance segmentation (test-dev 2017), outperforming state-of-the-art box-supervised methods by significant margins. Qualitative results indicate that masks produced by MAL are, in some cases, even better than human annotations.

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SRDAN: Scale-aware and Range-aware Domain Adaptation Network for Cross-dataset 3D Object Detection

SRDAN: Scale-aware and Range-aware Domain Adaptation Network...

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

作者： Zhang, Weichen Li, Wen Xu, Dong Univ Sydney Sch Elect & Informat Engn Sydney NSW Australia Univ Elect Sci & Technol China Sch Comp Sci & Engn Langfang Peoples R China

ISBN: (纸本)9781665445092

Geometric characteristic plays an important role in the representation of an object in 3D point clouds. For example, large objects often contain more points, while small ones contain fewer points. The points from objects near the capture device are denser, while those from far-range objects are sparser. These issues bring new challenges to 3D object detection, especially under the domain adaptation scenarios. In this work, we propose a new cross-dataset 3D object detection method named Scale-aware and Range-aware Domain Adaptation Network (SRDAN). We take advantage of the geometric characteristics of 3D data (i.e., size and distance), and propose the scale-aware domain alignment and the range-aware domain alignment strategies to guide the distribution alignment between two domains. For scale-aware domain alignment, we design a 3D voxel-based feature pyramid network to extract multi-scale semantic voxel features, and align the features and instances with similar scales between two domains. For range-aware domain alignment, we introduce a range-guided domain alignment module to align the features of objects according to their distance to the capture device. Extensive experiments under three different scenarios demonstrate the effectiveness of our SRDAN approach, and comprehensive ablation study also validates the importance of geometric characteristics for cross-dataset 3D object detection.

关键词： computer vision Three-dimensional displays Semantics Object detection Feature extraction pattern recognition Task analysis

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NTIRE 2022 Burst Super-Resolution Challenge

NTIRE 2022 Burst Super-Resolution Challenge

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

作者： Bhat, Goutam Danelljan, Martin Timofte, Radu Cao, Yizhen Cao, Yuntian Chen, Meiya Chen, Xihao Cheng, Shen Dudhane, Akshay Fan, Haoqiang Gang, Ruipeng Gao, Jian Gu, Yan Huang, Jie Huang, Liufeng Jo, Youngsu Kang, Sukju Khan, Salman Khan, Fahad Shahbaz Kondo, Yuki Li, Chenghua Li, Fangya Li, Jinjing Li, Youwei Li, Zechao Liu, Chenming Liu, Shuaicheng Liu, Zikun Liu, Zhuoming Luo, Ziwei Luo, Zhengxiong Mehta, Nancy Murala, Subrahmanyam Nam, Yoonchan Nakatani, Chihiro Ostyakov, Pavel Pan, Jinshan Song, Ge Sun, Jian Sun, Long Tang, Jinhui Ukita, Norimichi Wen, Zhihong Wu, Qi Wu, Xiaohe Xiao, Zeyu Xiong, Zhiwei Xu, Rongjian Xu, Ruikang Yan, Youliang Yang, Jialin Yang, Wentao Yang, Zhongbao Yasue, Fuma Yao, Mingde Yu, Lei Zhang, Cong Zamir, Syed Waqas Zhang, Jianxing Zhang, Shuohao Zhang, Zhilu Zheng, Qian Zhou, Gaofeng Zhussip, Magauiya Zou, Xueyi Zuo, Wangmeng Swiss Fed Inst Technol Comp Vis Lab Zurich Switzerland Julius Maximilian Univ Wurzburg Wurzburg Germany Commun Univ China Beijing Peoples R China UHDTV Res & Applicat Lab Beijing Peoples R China Chinese Acad Sci Inst Automat Beijing Peoples R China Harbin Inst Technol Harbin Peoples R China South China Univ Technol Guangzhou Peoples R China Megvii Technol Beijing Peoples R China Univ Elect Sci & Technol China UESTC Chengdu Peoples R China Xiaomi Beijing Peoples R China Huawei Noahs Ark Lab Shenzhen Peoples R China Nanjing Univ Sci & Technol Nanjing Peoples R China SRC B Beijing Peoples R China CASIA Beijing Peoples R China Indian Inst Technol Ropar IIT Ropar Rupnagar Punjab India Mohamed Bin Zayed Univ AI MBZUAI Abu Dhabi U Arab Emirates Incept Inst Artificial Intelligence IIAI Abu Dhabi U Arab Emirates MBZUAI Abu Dhabi U Arab Emirates Australian Natl Univ ANU Canberra ACT Australia Linkoping Univ Linkoping Sweden Toyota Technol Inst TTI Nagoya Aichi Japan Sogang Univ Seoul South Korea UESTC Chengdu Peoples R China USTC Hefei Peoples R China WHU Wuhan Peoples R China Univ Sci & Technol China Hefei Peoples R China

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Burst super-resolution has received increased attention in recent years due to its applications in mobile photography. By merging information from multiple shifted images of a scene, burst super-resolution aims to recover details which otherwise cannot be obtained using a simple input image. This paper reviews the NTIRE 2022 challenge on burst super-resolution. In the challenge, the participants were tasked with generating a clean RGB image with 4x higher resolution, given a RAW noisy burst as input. That is, the methods need to perform joint denoising, demosaicking, and super-resolution. The challenge consisted of 2 tracks. Track 1 employed synthetic data, where pixel-accurate high-resolution ground truths are available. Track 2 on the other hand used real-world bursts captured from a handheld camera, along with approximately aligned reference images captured using a DSLR. 14 teams participated in the final testing phase. The top performing methods establish a new state-of-the-art on the burst super-resolution task.

关键词： Training Photography Superresolution Noise reduction Network architecture Transformers pattern recognition

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InstanceDiffusion: Instance-Level Control for Image Generation

InstanceDiffusion: Instance-Level Control for Image Generati...

引用

conference on computer vision and pattern recognition (cvpr)

作者： Xudong Wang Trevor Darrell Sai Saketh Rambhatla Rohit Girdhar Ishan Misra GenAI Meta UC Berkeley

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

Text-to-image diffusion models produce high quality images but do not offer control over individual instances in the image. We introduce InstanceDiffusion that adds precise instance-level control to text-to-image diffusion models. InstanceDiffusion supports free-form language conditions per instance and allows flexible ways to specify instance locations such as simple single points, scribbles, bounding boxes or intricate instance segmentation masks, and combinations thereof We propose three major changes to text-to-image models that enable precise instance-level control. Our UniFusion block enables instance-level conditions for text-to-image models, the ScaleU block improves image fidelity, and our Multi-instance Sampler improves generations for multiple instances. InstanceDiffusion significantly surpasses specialized state-of-the-art models for each location condition. Notably, on the COCO dataset, we out-perform previous state-of-the-art by 20.4% $AP_{50}^{box}$ for box inputs, and 25.4% IoU for mask inputs.

关键词： Instance segmentation computer vision Image synthesis Computational modeling Text to image Diffusion models pattern recognition

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Deep Learning based Spatial-Temporal In-loop filtering for Versatile Video Coding

Deep Learning based Spatial-Temporal In-loop filtering for V...

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

作者： Pham, Chi D. K. Fu, Chen Zhou, Jinjia Hosei Univ Tokyo Japan JST PRESTO Saitama Japan

ISBN: (纸本)9781665448994

The existing deep learning-based Versatile Video Coding (VVC) in-loop filtering (ILF) enhancement works mainly focus on learning the one-to-one mapping between the reconstructed and the original video frame, ignoring the potential resources at encoder and decoder. This work proposes a deep learning-based Spatial-Temporal In-Loop filtering (STILF) that takes advantage of the coding information to improve VVC in-loop filtering. Each CTU is filtered by VVC default in-loop filtering, self-enhancement Convolutional neural network (CNN) with CU map (SEC), and the reference-based enhancement CNN with the optical flow (REO). Bits indicating ILF mode are encoded under CABAC regular mode. Experimental results show that 3.78%, 6.34%, 6%, and 4.64% BD-rate reductions are obtained under All Intra, Low Delay P, Low Delay B, and Random Access configurations, respectively.

关键词： Video coding Optical filters Filtering Delays Decoding pattern recognition Convolutional neural networks

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