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LFNAT 2023 Challenge on Light Field Depth Estimation: Methods and Results

LFNAT 2023 Challenge on Light Field Depth Estimation: Method...

引用

ieee computer Society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Hao Sheng Yebin Liu Jingyi Yu Gaochang Wu Wei Xiong Ruixuan Cong Rongshan Chen Longzhao Guo Yanlin Xie Shuo Zhang Song Chang Youfang Lin Wentao Chao Xuechun Wang Guanghui Wang Fuqing Duan Tun Wang Da Yang Zhenglong Cui Sizhe Wang Mingyuan Zhao Qiong Wang Qianyu Chen Zhengyu Liang Yingqian Wang Jungang Yang Xueting Yang Junli Deng LFNAT 2023 Challenge State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beihang University and Beihang Hangzhou Innovation Institute Yuhang

This paper reviews the 1st LFNAT challenge on light field depth estimation, which aims at predicting disparity information of central view image in a light field (i.e., pixel offset between central view image and adjacent view image). Compared to multi-view stereo matching, light field depth estimation emphasizes efficient utilization of the 2D angular information from multiple regularly varying views. This challenge specifies UrbanLF [20] light field dataset as the sole data source. There are two phases in total: submission phase and final evaluation phase, in which 75 registered participants successfully submit their predicted results in the first phase and 7 eligible teams compete in the second phase. The performance of all submissions is carefully reviewed and shown in this paper as a new standard for the current state-of-the-art in light field depth estimation. Moreover, the implementation details of these methods are also provided to stimulate related advanced research.

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StyleSpace Analysis: Disentangled Controls for StyleGAN Image Generation

StyleSpace Analysis: Disentangled Controls for StyleGAN Imag...

引用

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

作者： Wu, Zongze Lischinski, Dani Shechtman, Eli Hebrew Univ Jerusalem Jerusalem Israel Adobe Res San Jose CA USA

ISBN: (纸本)9781665445092

We explore and analyze the latent style space of StyleGAN2, a state-of-the-art architecture for image generation, using models pretrained on several different datasets. We first show that StyleSpace, the space of channel-wise style parameters, is significantly more disentangled than the other intermediate latent spaces explored by previous works. Next, we describe a method for discovering a large collection of style channels, each of which is shown to control a distinct visual attribute in a highly localized and disentangled manner. Third, we propose a simple method for identifying style channels that control a specific attribute, using a pretrained classifier or a small number of example images. Manipulation of visual attributes via these StyleSpace controls is shown to be better disentangled than via those proposed in previous works. To show this, we make use of a newly proposed Attribute Dependency metric. Finally, we demonstrate the applicability of StyleSpace controls to the manipulation of real images. Our findings pave the way to semantically meaningful and well-disentangled image manipulations via simple and intuitive interfaces.

关键词： Measurement Visualization computer vision Analytical models Image synthesis computer architecture Aerospace electronics

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Shot Contrastive Self-Supervised Learning for Scene Boundary Detection

Shot Contrastive Self-Supervised Learning for Scene Boundary...

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

作者： Chen, Shixing Nie, Xiaohan Fan, David Zhang, Dongqing Bhat, Vimal Hamid, Raffay Amazon Prime Video Seattle WA 98109 USA

ISBN: (纸本)9781665445092

Scenes play a crucial role in breaking the storyline of movies and TV episodes into semantically cohesive parts. However, given their complex temporal structure, finding scene boundaries can be a challenging task requiring large amounts of labeled training data. To address this challenge, we present a self-supervised shot contrastive learning approach (ShotCoL) to learn a shot representation that maximizes the similarity between nearby shots compared to randomly selected shots. We show how to apply our learned shot representation for the task of scene boundary detection to offer state-of-the-art performance on the MovieNet [33] dataset while requiring only similar to 25% of the training labels, using 9x fewer model parameters and offering 7x faster runtime. To assess the effectiveness of ShotCoL on novel applications of scene boundary detection, we take on the problem of finding timestamps in movies and TV episodes where video-ads can be inserted while offering a minimally disruptive viewing experience. To this end, we collected a new dataset called AdCuepoints with 3;975 movies and TV episodes, 2:2 million shots and 19;119 minimally disruptive ad cue-point labels. We present a thorough empirical analysis on this dataset demonstrating the effectiveness of ShotCoL for ad cue-points detection.

关键词： Training computer vision TV Runtime Training data Motion pictures pattern recognition

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A Realistic Evaluation of Semi-Supervised Learning for Fine-Grained Classification

A Realistic Evaluation of Semi-Supervised Learning for Fine-...

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

作者： Su, Jong-Chyi Cheng, Zezhou Maji, Subhransu Univ Massachusetts Amherst Amherst MA 01003 USA

ISBN: (纸本)9781665445092

We evaluate the effectiveness of semi-supervised learning (SSL) on a realistic benchmark where data exhibits considerable class imbalance and contains images from novel classes. Our benchmark consists of two fine-grained classification datasets obtained by sampling classes from the Aves and Fungi taxonomy. We find that recently proposed SSL methods provide significant benefits, and can effectively use out-of-class data to improve performance when deep networks are trained from scratch. Yet their performance pales in comparison to a transfer learning baseline, an alternative approach for learning from a few examples. Furthermore, in the transfer setting, while existing SSL methods provide improvements, the presence of out-of-class is often detrimental. In this setting, standard fine-tuning followed by distillation-based self-training is the most robust. Our work suggests that semi-supervised learning with experts on realistic datasets may require different strategies than those currently prevalent in the literature.

关键词： Fungi Technological innovation Transfer learning Taxonomy Benchmark testing Semisupervised learning pattern recognition

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PointPrompt: A Multi-modal Prompting Dataset for Segment Anything Model

PointPrompt: A Multi-modal Prompting Dataset for Segment Any...

引用

ieee computer Society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Jorge Quesada Mohammad Alotaibi Mohit Prabhushankar Ghassan AlRegib OLIVES Lab Georgia Institute of Technology Atlanta GA USA

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

The capabilities of foundation models, most recently the Segment Anything Model, have gathered a large degree of attention for providing a versatile framework for tackling a wide array of image segmentation tasks. However, the interplay between human prompting strategies and the segmentation performance of these models remains understudied, as does the role played by the domain knowledge that humans (by previous exposure) and models (by pretraining) bring to the prompting process. To bridge this gap, we present the PointPrompt dataset compiled across multiple image modalities as well as multiple prompting annotators per modality. We collected a total of 16 image datasets from the natural, underwater, medical and seismic domain in order to create a comprehensive resource to facilitate the study of prompting behavior and agreement across modalities. Overall, our prompting dataset contains 158880 inclusion points and 52594 exclusion points over a total of 6000 images. Our analysis highlights the following: (i) viability of prompts across heterogeneous data, (ii) that point prompts are a valuable resource in the effort for enhancing the robustness and generalizability of segmentation models across diverse domains, (iii) prompts facilitate an understanding of the dynamics between annotation strategies and neural network outcomes. Information on downloading the dataset, images, and prompting tool is provided on our project website https://***/pointprompt/.

关键词： Bridges Image segmentation computer vision Computational modeling conferences Neural networks Dynamic scheduling

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Hierarchical Motion Understanding via Motion Programs

Hierarchical Motion Understanding via Motion Programs

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

作者： Kulal, Sumith Mao, Jiayuan Aiken, Alex Wu, Jiajun Stanford Univ Stanford CA 94305 USA MIT Cambridge MA 02139 USA

ISBN: (纸本)9781665445092

Current approaches to video analysis of human motion focus on raw pixels or keypoints as the basic units of reasoning. We posit that adding higher-level motion primitives, which can capture natural coarser units of motion such as "backswing" or "follow-through", can be used to improve downstream analysis tasks. This higher level of abstraction can also capture key features, such as loops of repeated primitives, that are currently inaccessible at lower levels of representation. We therefore introduce Motion Programs, a neuro-symbolic, program-like representation that expresses motions as a composition of high-level primitives. We also present a system for automatically inducing motion programs from videos of human motion and for leveraging motion programs in video synthesis. Experiments show that motion programs can accurately describe a diverse set of human motions and the inferred programs contain semantically meaningful motion primitives, such as arm swings and jumping jacks. Our representation also benefits downstream tasks such as video interpolation and video prediction and outperforms off-the-shelf models. We further demonstrate how these programs can detect diverse kinds of repetitive motion and facilitate interactive video editing.

关键词： Interpolation computer vision Computational modeling Predictive models Cognition pattern recognition Task analysis

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SSAN: Separable Self-Attention Network for Video Representation Learning

SSAN: Separable Self-Attention Network for Video Representat...

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

作者： Guo, Xudong Guo, Xun Lu, Yan Tsinghua Univ Beijing Peoples R China Microsoft Res Asia Beijing Peoples R China MSRA Beijing Peoples R China

ISBN: (纸本)9781665445092

Self-attention has been successfully applied to video representation learning due to the effectiveness of modeling long range dependencies. Existing approaches build the dependencies merely by computing the pairwise correlations along spatial and temporal dimensions simultaneously. However;spatial correlations and temporal correlations represent different contextual information of scenes and temporal reasoning. Intuitively, learning spatial contextual information first will benefit temporal modeling. In this paper;we propose a separable self-attention (SSA) module, which models spatial and temporal correlations sequentially so that spatial contexts can be efficiently used in temporal modeling. By adding SSA module into 21) CNN, we build a SSA network (SSAN) for video representation learning. On the task of video action recognition, our approach outperforms state-of:the-art methods on Something Something and Kinetics-400 datasets. Our models often outperform counterparts with shallower network and fewer modalities. We further verify the semantic learning ability of our method in visual-language task of video retrieval, which showcases the homogeneity of video representations and text embeddings. On MSR-VIT and Youcook2 dowsers, video representations learnt by SSA significantly improve the state-of-the-art performance.

关键词： computer vision Correlation Pairwise error probability Computational modeling Semantics Cognition pattern recognition

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General Multi-label Image Classification with Transformers

General Multi-label Image Classification with Transformers

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

作者： Lanchantin, Jack Wang, Tianlu Ordonez, Vicente Qi, Yanjun Univ Virginia Charlottesville VA 22903 USA

ISBN: (纸本)9781665445092

Multi-label image classification is the task of predicting a set of labels corresponding to objects, attributes or other entities present in an image. In this work we propose the Classification Transformer (C-Tran), a general framework for multi-label image classification that leverages Transformers to exploit the complex dependencies among visual features and labels. Our approach consists of a Transformer encoder trained to predict a set of target labels given an input set of masked labels, and visual features from a convolutional neural network. A key ingredient of our method is a label mask training objective that uses a ternary encoding scheme to represent the state of the labels as positive, negative, or unknown during training. Our model shows state-of-the-art performance on challenging datasets such as COCO and Visual Genome. Moreover, because our model explicitly represents the label state during training, it is more general by allowing us to produce improved results for images with partial or extra label annotations during inference. We demonstrate this additional capability in the COCO, Visual Genome, News-500, and CUB image datasets.

关键词： Training Deep learning Visualization Genomics Transformers Encoding pattern recognition

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Self-Promoted Prototype Refinement for Few-Shot Class-Incremental Learning

Self-Promoted Prototype Refinement for Few-Shot Class-Increm...

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

作者： Zhu, Kai Cao, Yang Zhai, Wei Cheng, Jie Zha, Zheng-Jun Univ Sci & Technol China Hefei Peoples R China Huawei Technol Co Ltd Shenzhen Peoples R China

ISBN: (纸本)9781665445092

Few-shot class-incremental learning is to recognize the new classes given few samples and not forget the old classes. It is a challenging task since representation optimization and prototype reorganization can only be achieved under little supervision. To address this problem, we propose a novel incremental prototype learning scheme. Our scheme consists of a random episode selection strategy that adapts the feature representation to various generated incremental episodes to enhance the corresponding extensibility, and a self-promoted prototype refinement mechanism which strengthens the expression ability of the new classes by explicitly considering the dependencies among different classes. Particularly, a dynamic relation projection module is proposed to calculate the relation matrix in a shared embedding space and leverage it as the factor for bootstrapping the update of prototypes. Extensive experiments on three benchmark datasets demonstrate the above-par incremental performance, outperforming state-of-the-art methods by a margin of 13%, 17% and 11%, respectively.

关键词： computer vision Adaptation models Computational modeling Prototypes Benchmark testing Power capacitors pattern recognition

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Complementary Relation Contrastive Distillation

Complementary Relation Contrastive Distillation

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

作者： Zhu, Jinguo Tang, Shixiang Chen, Dapeng Yu, Shijie Liu, Yakun Rong, Mingzhe Yang, Aijun Wang, Xiaohua Xi An Jiao Tong Univ Xian Peoples R China Univ Sydney Sydney NSW Australia Sensetime Grp Ltd Hong Kong Peoples R China Chinese Acad Sci Shenzhen Inst Adv Technol Shenzhen Peoples R China

ISBN: (纸本)9781665445092

Knowledge distillation aims to transfer representation ability from a teacher model to a student model. Previous approaches focus on either individual representation distillation or inter-sample similarity preservation. While we argue that the inter-sample relation conveys abundant information and needs to be distilled in a more effective way. In this paper, we propose a novel knowledge distillation method, namely Complementary Relation Contrastive Distillation (CRCD), to transfer the structural knowledge from the teacher to the student. Specifically, we estimate the mutual relation in an anchor-based way and distill the anchor-student relation under the supervision of its corresponding anchor-teacher relation. To make it more robust, mutual relations are modeled by two complementary elements: the feature and its gradient. Furthermore, the low bound of mutual information between the anchor-teacher relation distribution and the anchor-student relation distribution is maximized via relation contrastive loss, which can distill both the sample representation and the inter-sample relations. Experiments on different benchmarks demonstrate the effectiveness of our proposed CRCD.

关键词： computer vision Benchmark testing pattern recognition Mutual information

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