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检索条件"任意字段=2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2023"

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Photorealistic Arm Robot Simulation for 3D Plant Reconstruction and Automatic Annotation using Unreal Engine 5

Photorealistic Arm Robot Simulation for 3D Plant Reconstruct...

引用

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

作者： Xingjian Li Jeremy Park Chris Reberg-Horton Steven Mirsky Edgar Lobaton Lirong Xiang Department of Biological and Agricultural Engineering North Carolina State University Department of Electrical and Computer Engineering North Carolina State University Department of Computer Science North Carolina State University Department of Crop and Soil Science North Carolina State University Sustainable Agricultural Systems Laboratory USDA-ARS

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Robotics paired with computer vision are widely used in precision agriculture. Simulations are critical for safety and performance estimation by verifying their routine in a virtual world before real-world testing and deployment. However, many simulators used in agricultural robots lack photorealism in their virtual worlds compared to the real world. We implemented Unreal Engine 5 (UE5) and the Robot Operating System (ROS) to develop a robot simulator tailored to agricultural tasks and synthetic data generation with RGB, segmentation, and depth images. We designed a method for assigning multiple segmentation labels within a single plant mesh. We experimented with a semi-spherical routine for two robot arms to perform 3D point cloud reconstruction across 10 plant assets. We showed our simulator produces much more accurate segmentation images and reconstruction compared to existing UE5 solutions. We extend our results with Neural Radiance Field (NeRF) reconstructions. The packaged simulator, UE5 project, and ROS package with the Python routine can be found at https://***/NCSU-BAE-ARLab/AgriRoboSimUE5.

关键词： Image segmentation computer vision Three-dimensional displays Computational modeling Neural radiance field Manipulators Image reconstruction

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OGRMPI: An Efficient Multiview Integrated Multiplane Image based on Occlusion Guided Residuals

OGRMPI: An Efficient Multiview Integrated Multiplane Image b...

引用

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

作者： Dae Yeol Lee Guan-Ming Su Peng Yin Dolby Laboratories Sunnyvale CA USA

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Multiplane Image (MPI) is a volumetric scene representation method that uses multiple layers of texture (RGB) and alpha (A) planes. It offers high deployability due to its compatibility with standard codecs and low rendering complexity. While existing MPI methods have shown promising results, they are constrained by either a limited range of pose span or necessitates transmitting multiple MPIs. In this paper, we present a novel framework to generate an efficient single MPI that integrates the information from multiple views. The key idea is to utilize the surface opacity estimates (A) to locate and retrieve occluded RGB pixels from other camera views that share matching depth, which we call Occlusion Guided Residuals (OGR). Additionally, we introduce an inter-layer texture filler, which is a learned RGB texture on intermediate depth between MPI layers to deal with scenes with continuous depth with a limited number of MPI layers. We composite MPI using the aforementioned RGB textures and refine alpha layers through training with multiview rendering supervision. Thus, through iterations of training, we jointly optimize scene opacity (A) and textures (RGB) leading to an accurate MPI representation. Experiments on various multiview image and video datasets demonstrate that the proposed method achieves state-of-the-art performances with data efficiency. Notably, with just 16 layers, the proposed method attains performance on par with other methods that use twice the layer number or fuse multiple MPIs.

关键词： Training Interpolation computer vision Accuracy Three-dimensional displays Fuses conferences

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Diagnostic Benchmark and Iterative Inpainting for Layout-Guided Image Generation

Diagnostic Benchmark and Iterative Inpainting for Layout-Gui...

引用

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

作者： Jaemin Cho Linjie Li Zhengyuan Yang Zhe Gan Lijuan Wang Mohit Bansal UNC Chapel Hill Microsoft Research

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Spatial control is a core capability in controllable image generation. Advancements in layout-guided image generation have shown promising results on in-distribution (ID) datasets with similar spatial configurations. However, it is unclear how these models perform when facing out-of-distribution (OOD) samples with arbitrary, unseen layouts. In this paper, we propose LayoutBench, a diagnostic benchmark for layout-guided image generation that examines four categories of spatial control skills: number, position, size, and shape. We benchmark two recent representative layout-guided image generation methods and observe that the good ID layout control may not generalize well to arbitrary layouts in the wild (e.g., objects at the boundary). Next, we propose IterInpaint, a new baseline that generates foreground and background regions step-by-step via inpainting, demonstrating stronger generalizability than existing models on OOD layouts in LayoutBench. We perform quantitative and qualitative evaluation and fine- grained analysis on the four LayoutBench skills to pinpoint the weaknesses of existing models. We show comprehensive ablation studies on IterInpaint, including training task ratio, crop&paste vs. repaint, and generation order. Lastly, we evaluate the zero-shot performance of different pretrained layout-guided image generation models on LayoutBench-COCO, our new benchmark for OOD layouts with real objects, where our IterInpaint consistently outperforms SOTA baselines in all four splits.

关键词： Training computer vision Image synthesis Shape conferences Layout Benchmark testing

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ATOM: Attention Mixer for Efficient Dataset Distillation

ATOM: Attention Mixer for Efficient Dataset Distillation

引用

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

作者： Samir Khaki Ahmad Sajedi Kai Wang Lucy Z. Liu Yuri A. Lawryshyn Konstantinos N. Plataniotis University of Toronto National University of Singapore Royal Bank of Canada (RBC)

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Recent works in dataset distillation seek to minimize training expenses by generating a condensed synthetic dataset that encapsulates the information present in a larger real dataset. These approaches ultimately aim to attain test accuracy levels akin to those achieved by models trained on the entirety of the original dataset. Previous studies in feature and distribution matching have achieved significant results without incurring the costs of bi-level optimization in the distillation process. Despite their convincing efficiency, many of these methods suffer from marginal downstream performance improvements, limited distillation of contextual information, and subpar cross-architecture generalization. To address these challenges in dataset distillation, we propose the ATtentiOn Mixer (ATOM) module to efficiently distill large datasets using a mixture of channel and spatial-wise attention in the feature matching process. Spatial-wise attention helps guide the learning process based on consistent localization of classes in their respective images, allowing for distillation from a broader receptive field. Meanwhile, channel-wise attention captures the contextual information associated with the class itself, thus making the synthetic image more informative for training. By integrating both types of attention, our ATOM module demonstrates superior performance across various computer vision datasets, including CIFAR10/100 and Tiny-Imagenet. Notably, our method significantly improves performance in scenarios with a low number of images per class, thereby enhancing its potential. Furthermore, we maintain the improvement on cross-architectures and applications such as neural architecture search.

关键词： Training Location awareness computer vision Costs Atomic layer deposition Performance gain Transformers

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COVER: A Comprehensive Video Quality Evaluator

COVER: A Comprehensive Video Quality Evaluator

引用

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

作者： Chenlong He Qi Zheng Ruoxi Zhu Xiaoyang Zeng Yibo Fan Zhengzhong Tu Fudan University University of Texas at Austin

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Video quality assessment, especially for a massive scale of user-generated content, is an essential yet challenging computer vision and video analysis problem. Prior methods have been shown to be effective in mirroring subjective human opinion scores; however, they fail to capture the complicated, multi-dimensional aspects of factors that impact the overall perceptual quality. In this paper, we introduce COVER, a comprehensive video quality evaluator, a novel framework designed to evaluate video quality holistically — from a technical, aesthetic, and semantic perspective. Specifically, COVER leverages three parallel branches: (1) a Swin Transformer backbone implemented on spatially sampled crops to predict technical quality; (2) a ConvNet employed on subsampled frames to derive aesthetic quality; (3) a CLIP image encoder executed on re-sized frames to obtain semantic quality. We further propose a simplified cross-gating block to interact with the three branches before feeding into the predicting head. The final quality score is attained using a weighted sum of each sub-score, making a multi-faceted metric. Our experimental results demonstrate that COVER exceeds the state-of-the-art models in multiple UGC video quality datasets. Moreover, COVER offers a diagnosable quality report to explain the quality score in multiple pillars, while it is capable of processing 1080p videos at 3x faster speed than the real-time requirement. To facilitate future research on efficient and explainable video quality research, the code is available at https://***/vztu/COVER.

关键词： computer vision Visualization Semantics User-generated content Streaming media Feature extraction Transformers

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NTIRE 2023 Challenge on Stereo Image Super-Resolution: Methods and Results

NTIRE 2023 Challenge on Stereo Image Super-Resolution: Metho...

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

作者： Longguang Wang Yulan Guo Yingqian Wang Juncheng Li Shuhang Gu Radu Timofte Ming Cheng Haoyu Ma Qiufang Ma Xiaopeng Sun Shijie Zhao Xuhan Sheng Yukan Ding Ming Sun Xing Wen Dafeng Zhang Jia Li Fan Wang Zheng Xie Zongyao He Zidian Qiu Zilin Pan Zhihao Zhan Xingyuan Xian Zhi Jin Yuanbo Zhou Wei Deng Ruofeng Nie Jiajun Zhang Qinquan Gao Tong Tong Kexin Zhang Junpei Zhang Rui Peng Yanbiao Ma Licheng Jiao Haoran Bai Lingshun Kong Jinshan Pan Jiangxin Dong Jinhui Tang Pu Cao Tianrui Huang Lu Yang Qing Song Bingxin Chen Chunhua He Meiyun Chen Zijie Guo Shaojuan Luo Chengzhi Cao Kunyu Wang Fanrui Zhang Qiang Zhang Nancy Mehta Subrahmanyam Murala Akshay Dudhane Yujin Wang Lingen Li Garas Gendy Nabil Sabor Jingchao Hou Guanghui He Junyang Chen Hao Li Yukai Shi Zhijing Yang Wenbin Zou Yunchen Zhang Mingchao Jiang Zhongxin Yu Ming Tan Hongxia Gao Ziwei Luo Fredrik K. Gustafsson Zheng Zhao Jens Sjölund Thomas B. Schön Jingxiang Chen Bo Yang XiSheryl Zhang Chenghua Li Weijun Yuan Zhan Li Ruting Deng Jintao Zeng Pulkit Mahajan Sahaj Mistry Shreyas Chatterjee Vinit Jakhetiya Badri Subudhi Sunil Jaiswal Zhao Zhang Huan Zheng Suiyi Zhao Yangcheng Gao Yanyan Wei Bo Wang Gen Li Aijin Li Lei Sun Ke Chen Congling Tang Yunzhe Li Jun Chen Yuan-Chun Chiang Yi-Chung Chen Zhi-Kai Huang Hao-Hsiang Yang I-Hsiang Chen Sy-Yen Kuo Yiheng Wang Gang Zhu Xingyi Yang Songhua Liu Yongcheng Jing Xingyu Hu Jianwen Song Changming Sun Arcot Sowmya Seung Ho Park Xiaoyan Lei Jingchao Wang Chenbo Zhai Yufei Zhang Weifeng Cao Wenlong Zhang NTIRE 2023 Challenge Organizers

This paper summarizes the 2nd NTIRE challenge on stereo image super-resolution (SR) with a focus on new solutions and results. The task of the challenge is to super-resolve a low-resolution stereo image pair to a high-resolution one with a magnification factor of ×4. Compared with single image SR, the major challenge of this challenge lies in how to exploit additional information in another viewpoint and how to maintain stereo consistency in the results. This challenge has 3 tracks, including one track on distortion (e.g., PSNR) and bicubic degradation, one track on perceptual quality (e.g., LPIPS) and bicubic degradation, as well as another track on real degradations. In total, 175, 93, and 103 participants were successfully registered for each track, respectively. In the test phase, 21, 17, and 12 teams successfully submitted results with PSNR (RGB) scores better than the baseline. This challenge establishes a new benchmark for stereo image SR.

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NeRF as Pretraining at Scale: Generalizable 3D-Aware Semantic Representation Learning from View Prediction

NeRF as Pretraining at Scale: Generalizable 3D-Aware Semanti...

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

作者： Wenyan Cong Hanxue Liang Zhiwen Fan Peihao Wang Yifan Jiang Dejia Xu A. Cengiz Oztireli Zhangyang Wang University of Texas at Austin University of Cambridge

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Cross-scene generalizable NeRF models, which could directly synthesize novel views using several source views of unseen scenes, are gaining prominence in the NeRF field. Discovering the potential signal of emerging capabilities in existing methods, we draw a parallel between BERT’s "drop-and-predict" Masked Language Model (MLM) pre-training and novel view synthesis (NVS) in generalizable NeRF. In this work, we pioneer the scaling up of NVS as an effective pretraining strategy in a multi-view context. To bolster generalizability in pretraining, we incorporate a large-scale, minimally annotated dataset and proportionally increase the model size, revealing a neural scaling law akin to that observed in BERT. We also introduce innovative hardness-aware training techniques to enhance robust feature learning. Our model, named "NPS", demonstrates remarkable generalizability in both zero-shot and few-shot novel view synthesis. It further shows emergent capabilities in downstream tasks like few-shot multi-view semantic segmentation and depth estimation. Significantly, NPS reduces the necessity of training separate models for each task, underlining its versatility and efficiency. This approach sets a new precedent in the NeRF field, and highlights the vast possibilities opened up by scaling up generalizable novel view synthesis.

关键词： Training Representation learning computer vision Semantic segmentation conferences Semantics Estimation

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DiffLight: Integrating Content and Detail for Low-light Image Enhancement

DiffLight: Integrating Content and Detail for Low-light Imag...

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

作者： Yixu Feng Shuo Hou Haotian Lin Yu Zhu Peng Wu Wei Dong Jinqiu Sun Qingsen Yan Yanning Zhang Northwestern Polytechnical University Xi’an University of Architecture and Technology

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

The Low Light Image Enhancement (LLIE) task has been a hotspot in low-level computer vision research. The camera sensor can only capture a small amount of ambient light signal in low-light condition, resulting in significant noise black pseudo artifacts in images, which not only degrade visual quality but also affect the performance of down-stream visual tasks. However, current methods often produce overly smoothed and distorted results, or introduce strong noise artifacts. Moreover, for recent UHD high-definition low-light images, due to GPU memory limitations, LLIE must be conducted in patches, leading to block artifacts. Faced with these challenges, we propose a dual-branch pipeline called DiffLight. Specifically, it consists of the Denoising Enhancement (DE) branch and the Detail Preservation (DP) branch. The DE-branch adopts a combination of DiffIR and LEDNet to reduce noise and enhance brightness, while the DP-branch utilizes a novel Light Full-Former (LFF) method, which comprises 20 Full-Attention (LFA) modules to preserve full-scale image details. To tackle block artifacts, we further introduce Progressive Patch Fusion (PPF) for patch fusion. Experimental results demonstrate that our approach is high-ranked in the CVPR2024 NTIRE Low Light Enhancement challenge and produced state-of-the (SOTA) results on other datasets.

关键词： Visualization computer vision Image color analysis conferences Pipelines Noise reduction Graphics processing units

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Task Navigator: Decomposing Complex Tasks for Multimodal Large Language Models

Task Navigator: Decomposing Complex Tasks for Multimodal Lar...

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

作者： Feipeng Ma Yizhou Zhou Yueyi Zhang Siying Wu Zheyu Zhang Zilong He Fengyun Rao Xiaoyan Sun University of Science and Technology of China WeChat Tencent Inc. Institute of Artificial Intelligence Hefei Comprehensive National Science Center

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Inspired by the remarkable progress achieved by recent Large Language Models (LLMs), Multimodal Large Language Models (MLLMs) take LLMs as their brains, and have achieved surprising results in many downstream tasks by training on a large amount of task-specific data. However, when faced with complex tasks that require the collaboration of multiple capabilities, existing MLLMs recollect training data and retrain the model, ignoring the systematic utilization of LLMs and their possessed capabilities learned in downstream tasks. Inspired by the way humans tackle complex questions, in this paper, we propose a novel framework called Task Navigator. In our framework, LLMs act as navigators to chart a viable path for solving complex tasks and guide MLLMs through the process step by step. Specifically, LLMs iteratively break down sub-problems and refine them to be more reasonable and answerable, which are subsequently resolved by MLLMs to obtain relevant subanswers, until the LLMs have collected enough information to answer the initial question. Task Navigator provides an effective way to extend MLLMs to tackle complex tasks, thus broadening MLLMs’ applicability. To evaluate the performance of the proposed framework, we have curated a carefully designed benchmark called VersaChallenge. Experiments on VersaChallenge demonstrate the effectiveness of our proposed method.

关键词： Training computer vision Systematics Navigation Large language models conferences Training data

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An Effective Ensemble Learning Framework for Affective Behaviour Analysis

An Effective Ensemble Learning Framework for Affective Behav...

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

作者： Wei Zhang Feng Qiu Chen Liu Lincheng Li Heming Du Tianchen Guo Xin Yu Netease Fuxi AI Lab The University of Queensland

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Affective Behavior Analysis aims to facilitate technology emotionally smart, creating a world where devices can understand and react to our emotions as humans do. To comprehensively evaluate the authenticity and applicability of emotional behavior analysis techniques in natural environments, the 6th competition on Affective Behavior Analysis in-the-wild (ABAW) utilizes the Aff-Wild2, Hume-Vidmimic2, and C-EXPR-DB datasets to set up five competitive tracks, i.e., Valence-Arousal (VA) Estimation, Expression (EXPR) recognition, Action Unit (AU) Detection, Compound Expression (CE) recognition, and Emotional Mimicry Intensity (EMI) Estimation. In this paper, we present our method designs for VA estimation, expression recognition, and AU detection tracks. Specifically, our framework mainly includes three aspects: 1) To achieve high-quality facial feature representations, we employ Masked-Auto Encoder as the visual features extraction model and fine-tune it with our facial dataset. 2) Utilizing a transformer-based feature fusion module to fully integrate emotional information provided by audio signals, visual images, and transcripts, offering high-quality expression features for the downstream tasks. 3) Considering the complexity of the video collection scenes, we conduct a more detailed dataset division based on scene characteristics and train the classifier for each scene. Extensive experiments demonstrate the superiority of our designs. Our work won the championship in the AU, EXPR, and VA tracks at the ABAW6 competition.

关键词： Human computer interaction Gold Visualization Emotion recognition Design methodology Estimation Feature extraction

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