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Image Inpainting with External-internal Learning and Monochromic Bottleneck

Image Inpainting with External-internal Learning and Monochr...

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

作者： Wang, Tengfei Ouyang, Hao Chen, Qifeng Hong Kong Univ Sci & Technol Hong Kong Peoples R China

ISBN: (纸本)9781665445092

Although recent inpainting approaches have demonstrated significant improvement with deep neural networks, they still suffer from artifacts such as blunt structures and abrupt colors when filling in the missing regions. To address these issues, we propose an external-internal inpainting scheme with a monochromic bottleneck that helps image inpainting models remove these artifacts. In the external learning stage, we reconstruct missing structures and details in the monochromic space to reduce the learning dimension. In the internal learning stage, we propose a novel internal color propagation method with progressive learning strategies for consistent color restoration. Extensive experiments demonstrate that our proposed scheme helps image inpainting models produce more structure-preserved and visually compelling results.

关键词： Deep learning computer vision Image color analysis Superresolution Semantics Filling pattern recognition

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Rotation-Only Bundle Adjustment

Rotation-Only Bundle Adjustment

引用

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

作者： Lee, Seong Hun Civera, Javier Univ Zaragoza I3A Zaragoza Spain

ISBN: (纸本)9781665445092

We propose a novel method for estimating the global rotations of the cameras independently of their positions and the scene structure. When two calibrated cameras observe five or more of the same points, their relative rotation can be recovered independently of the translation. We extend this idea to multiple views, thereby decoupling the rotation estimation from the translation and structure estimation. Our approach provides several benefits such as complete immunity to inaccurate translations and structure, and the accuracy improvement when used with rotation averaging. We perform extensive evaluations on both synthetic and real datasets, demonstrating consistent and significant gains in accuracy when used with the state-of-the-art rotation averaging method.

关键词： Bundle adjustment computer vision Estimation Cameras pattern recognition Optimization

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BlockGCN: Redefine Topology Awareness for Skeleton-Based Action recognition

BlockGCN: Redefine Topology Awareness for Skeleton-Based Act...

引用

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

作者： Zhou, Yuxuan Yan, Xudong Cheng, Zhi-Qi Yan, Yan Dai, Qi Hua, Xian-Sheng Univ Mannheim Mannheim Germany City Univ Macau Taipa Macao Peoples R China Carnegie Mellon Univ Pittsburgh PA 15213 USA Chinese Acad Sci SIAT Beijing Peoples R China Microsoft Res Redmond WA USA Terminus Grp Hyderabad India

ISBN: (纸本)9798350353013;9798350353006

Graph Convolutional Networks (GCNs) have long set the state-of- the-art in skeleton-based action recognition, leveraging their ability to unravel the complex dynamics of human joint topology through the graph's adjacency matrix. However, an inherent flaw has come to light in these cutting-edge models: they tend to optimize the adjacency matrix jointly with the model weights. This process, while seemingly efficient, causes a gradual decay of bone connectiv-ity data, resulting in a model indifferent to the very topology it sought to represent. To remedy this, we propose a two-fold strategy: (1) We introduce an innovative approach that encodes bone connectivity by harnessing the power of graph distances to describe the physical topology;we further incorporate action-specific topological representation via persistent homology analysis to depict systemic dynamics. This preserves the vital topological nuances often lost in conventional GCNs. (2) Our investigation also reveals the redundancy in existing GCNs for multi-relational modeling, which we address by proposing an efficient refinement to Graph Convolutions ( GC) - the BlockGC. This significantly reduces parameters while improving performance beyond original GCNs. Our full model, BlockGCN, establishes new benchmarks in skeleton-based action recognition across all model categories. Its high accuracy and lightweight design, most notably on the large-scale NTU RGB+D 120 dataset, stand as strong validation of the efficacy of BlockGCN.

关键词： Benchmarking

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Exploring Facial Expression recognition through Semi-Supervised Pre-training and Temporal Modeling

Exploring Facial Expression Recognition through Semi-Supervi...

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

作者： Yu, Jun Wei, Zhihong Cai, Zhongpeng Zhao, Gongpeng Zhang, Zerui Wang, Yongqi Xie, Guochen Zhu, Jichao Zhu, Wangyuan Liu, Qingsong Liang, Jiaen Univ Sci & Technol China Hefei Peoples R China Unisound AI Technol Co Ltd Beijing Peoples R China

ISBN: (纸本)9798350365474

Facial Expression recognition (FER) plays a crucial role in computer vision and finds extensive applications across various fields. This paper aims to present our approach for the 6th Affective Behavior Analysis in-the-Wild (ABAW) competition, scheduled to be held at cvpr2024. In the facial expression recognition task, the limited size of the FER dataset poses a challenge to the expression recognition model's generalization ability, resulting in subpar recognition performance. To address this problem, we employ a Semi-supervised learning technique to generate expression category pseudo labels for unlabeled face data. At the same time, we uniformly sampled the labeled facial expression samples and implemented a debiased feedback learning strategy to address the problem of category imbalance in the dataset and the possible data bias in semi-supervised learning. Moreover, to further compensate for the limitation and bias of features obtained only from static images, we introduced a Temporal Encoder to capture temporal relationships between neighbouring expression image features. In the 6th ABAW competition, our method achieved the third place in the official test set, a result that fully demonstrates the effectiveness and competitiveness of our proposed method.

关键词： Facial Expression recognition Semi-Supervised Learning Temporal Modeling

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Seeing the World in a Bag of Chips

Seeing the World in a Bag of Chips

引用

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

作者： Park, Jeong Joon Holynski, Aleksander Seitz, Steven M. Univ Washington Seattle WA 98195 USA

ISBN: (纸本)9781728171685

We address the dual problems of novel view synthesis and environment reconstruction from hand-held RGBD sensors. Our contributions include 1) modeling highly specular objects, 2) modeling inter-reflections and Fresnel effects, and 3) enabling surface light field reconstruction with the same input needed to reconstruct shape alone. In cases where scene surface has a strong mirror-like material component, we generate highly detailed environment images, revealing room composition, objects, people, buildings, and trees visible through windows. Our approach yields state of the art view synthesis techniques, operates on low dynamic range imagery, and is robust to geometric and calibration errors.

关键词： computer vision

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Masked Autoencoders for Microscopy are Scalable Learners of Cellular Biology

Masked Autoencoders for Microscopy are Scalable Learners of ...

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

作者： Krauss, Oren Kenyon-Dean, Kian Saberian, Saber Fallah, Maryam McLean, Peter Leung, Jess Sharma, Vasudev Khan, Ayla Balakrishnan, Jia Celik, Safiye Beaini, Dominique Sypetkowski, Maciej Cheng, Chi Vicky Morsel, Kristen Makes, Maureen Mabey, Ben Earnshaw, Berton Recurs Salt Lake City UT 84101 USA Valence Labs Rajpura India

ISBN: (纸本)9798350353006

Featurizing microscopy images for use in biological research remains a significant challenge, especially for large-scale experiments spanning millions of images. This work explores the scaling properties of weakly supervised classifiers and self-supervised masked autoencoders (MAEs) when training with increasingly larger model backbones and microscopy datasets. Our results show that ViT-based MAEs outperform weakly supervised classifiers on a variety of tasks, achieving as much as a 11.5% relative improvement when recalling known biological relationships curated from public databases. Additionally, we develop a new channel-agnostic MAE architecture (CA-MAE) that allows for inputting images of different numbers and orders of channels at inference time. We demonstrate that CA-MAEs effectively generalize by inferring and evaluating on a microscopy image dataset (JUMP-CP) generated under different experimental conditions with a different channel structure than our pretraining data (RPI-93M). Our findings motivate continued research into scaling self-supervised learning on microscopy data in order to create powerful foundation models of cellular biology that have the potential to catalyze advancements in drug discovery and beyond. Relevant code and select models released with this work can be found at: https://***/recursionpharma/maes_microscopy.

关键词： cell biology cell morphology microscopy self-supervised learning SSL vision transformer ViT

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What Do You See in Vehicle? Comprehensive vision Solution for In-Vehicle Gaze Estimation

What Do You See in Vehicle? Comprehensive Vision Solution fo...

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

作者： Cheng, Yihua Zhu, Yaning Wang, Zongji Hao, Hongquan Liu, Yongwei Cheng, Shiqing Wang, Xi Chang, Hyung Jin Univ Birmingham Birmingham W Midlands England Huazhong Univ Sci & Technol Wuhan Hubei Peoples R China Chinese Acad Sci NIST Beijing Peoples R China CalmCar Suzhou Jiangsu Peoples R China

ISBN: (纸本)9798350353013;9798350353006

Driver's eye gaze holds a wealth of cognitive and intentional cues crucial for intelligent vehicles. Despite its significance, research on in-vehicle gaze estimation remains limited due to the scarcity of comprehensive and well-annotated datasets in real driving scenarios. In this paper, we present three novel elements to advance in-vehicle gaze research. Firstly, we introduce IVGaze, a pioneering dataset capturing in-vehicle gaze, collected from 125 subjects and covering a large range of gaze and head poses within vehicles. In this dataset, we propose a new vision-based solution for in-vehicle gaze collection, introducing a refined gaze target calibration method to tackle annotation challenges. Second, our research focuses on in-vehicle gaze estimation leveraging the IVGaze. In-vehicle face images often suffer from low resolution, prompting our introduction of a gaze pyramid transformer that leverages transformer-based multilevel features integration. Expanding upon this, we introduce the dual-stream gaze pyramid transformer (GazeDPTR). Employing perspective transformation, we rotate virtual cameras to normalize images, utilizing camera pose to merge normalized and original images for accurate gaze estimation. GazeDPTR shows state-of-the-art performance on the IVGaze dataset. Thirdly, we explore a novel strategy for gaze zone classification by extending the GazeDPTR. A foundational tri-plane and project gaze onto these planes are newly defined. Leveraging both positional features from the projection points and visual attributes from images, we achieve superior performance compared to relying solely on visual features, substantiating the advantage of gaze estimation. The project is available at https://***/work/ivgaze.

关键词： Image annotation

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PartDistill: 3D Shape Part Segmentation by vision-Language Model Distillation

PartDistill: 3D Shape Part Segmentation by Vision-Language M...

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

作者： Umam, Ardian Yang, Cheng-Kun Chen, Min-Hung Chuang, Jen-Hui Lin, Yen-Yu Natl Yang Ming Chiao Tung Univ Hsinchu Taiwan MediaTek Hsinchu Taiwan NVIDIA Taipei Taiwan

ISBN: (纸本)9798350353013;9798350353006

This paper proposes a cross-modal distillation framework, PartDistill, which transfers 2D knowledge from vision-language models (VLMs) to facilitate 3D shape part segmentation. PartDistill addresses three major challenges in this task: the lack of 3D segmentation in invisible or undetected regions in the 2D projections, inconsistent 2D predictions by VLMs, and the lack of knowledge accumulation across different 3D shapes. PartDistill consists of a teacher network that uses a VLM to make 2D predictions and a student network that learns from the 2D predictions while extracting geometrical features from multiple 3D shapes to carry out 3D part segmentation. A bidirectional distillation, including forward and backward distillations, is carried out within the framework, where the former forward distills the 2D predictions to the student network, and the latter improves the quality of the 2D predictions, which subsequently enhances the final 3D segmentation. Moreover, PartDistill can exploit generative models that facilitate effortless 3D shape creation for generating knowledge sources to be distilled. Through extensive experiments, PartDistill boosts the existing methods with substantial margins on widely used ShapeNetPart and PartNetE datasets, by more than 15% and 12% higher mIoU scores, respectively. The code for this work is available at https://***/ardianumam/PartDistill.

关键词： 3d part segmentation cross-modal distillation zero-shot segmentation

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Learning to Name Classes for vision and Language Models

Learning to Name Classes for Vision and Language Models

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

作者： Parisot, Sarah Yang, Yongxin McDonagh, Steven Huawei Noahs Ark Lab Montreal PQ Canada

ISBN: (纸本)9798350301298

Large scale vision and language models can achieve impressive zero-shot recognition performance by mapping class specific text queries to image content. Two distinct challenges that remain however, are high sensitivity to the choice of handcrafted class names that define queries, and the difficulty of adaptation to new, smaller datasets. Towards addressing these problems, we propose to leverage available data to learn, for each class, an optimal word embedding as a function of the visual content. By learning new word embeddings on an otherwise frozen model, we are able to retain zero-shot capabilities for new classes, easily adapt models to new datasets, and adjust potentially erroneous, non-descriptive or ambiguous class names. We show that our solution can easily be integrated in image classification and object detection pipelines, yields significant performance gains in multiple scenarios and provides insights into model biases and labelling errors.

关键词： language reasoning vision

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ViTamin: Designing Scalable vision Models in the vision-Language Era

ViTamin: Designing Scalable Vision Models in the Vision-Lang...

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

作者： Chen, Jieneng Yu, Qihang Shen, Xiaohui Yuille, Alan Chen, Liang-Chieh Johns Hopkins Univ Baltimore MD 21218 USA ByteDance Beijing Peoples R China

ISBN: (纸本)9798350353006

Recent breakthroughs in vision-language models (VLMs) start a new page in the vision community. The VLMs provide stronger and more generalizable feature embeddings compared to those from ImageNet-pretrained models, thanks to the training on the large-scale Internet image-text pairs. However, despite the amazing achievement from the VLMs, vanilla vision Transformers (ViTs) remain the default choice for the image encoder. Although pure transformer proves its effectiveness in the text encoding area, it remains questionable whether it is also the case for image encoding, especially considering that various types of networks are proposed on the ImageNet benchmark, which, unfortunately, are rarely studied in VLMs. Due to small data/model scale, the original conclusions of model design on ImageNet can be limited and biased. In this paper, we aim at building an evaluation protocol of vision models in the vision-language era under the contrastive language-image pretraining (CLIP) framework. We provide a comprehensive way to benchmark different vision models, covering their zero-shot performance and scalability in both model and training data sizes. To this end, we introduce ViTamin, a new vision models tailored for VLMs. ViTamin-L significantly outperforms ViT-L by 2.0% ImageNet zero-shot accuracy, when using the same publicly available DataComp-1B dataset and the same OpenCLIP training scheme. ViTamin-L presents promising results on 60 diverse benchmarks, including classification, retrieval, open-vocabulary detection and segmentation, and large multi-modal models. When further scaling up the model size, our ViTamin-XL with only 436M parameters attains 82.9% ImageNet zero-shot accuracy, surpassing 82.0% achieved by EVA-E that has ten times more parameters (4.4B).

关键词： Architectural Design vision-Language Models

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