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A Single Residual Network with ESA Modules and Distillation

A Single Residual Network with ESA Modules and Distillation

引用

2023 ieee/cvf conference on computer vision and pattern recognition workshops, cvprw 2023

作者： Wang, Yucong Cai, Minjie Hunan University College of Computer Science and Electronic Engineering China

ISBN: (纸本)9798350302493

Although there are many methods based on deep learning that have superior performance on single image super-resolution (SISR), it is difficult to run in real time on devices with limited computing power. Some recent studies have found that simply relying on reducing parameters or reducing the theoretical FLOPs of the model does not speed up the inference time of the network in a practical sense. Actual speed on the device is probably a better measure than FLOPs. In this work, we propose a new single residual network (SRN). On the one hand, we try to introduce and optimize an attention mechanism module to improve the performance of the network with a relatively small speed loss. On the other hand, we find that residuals in residual blocks do not have a positive impact on networks with adjusted ESA. Therefore, the residual of the network residual block is removed, which not only improves the speed of the network, but also improves the performance of the network. Finally, we reduced the number of channels and the number of residual blocks of the classic model EDSR, and removed the last convolution before the long residual. We set this tuned EDSR as the teacher model and our newly proposed SRN as the student model. Under the joint effect of the original loss and the distillation loss, the performance of the network can be improved without losing the inference time. Combining the above strategies, our proposed model runs much faster than similarly performing models. As an example, we built a Fast and Efficient Network (SRN) and its small version SRN-S, which run 30%-37% faster than the state-of-the-art EISR model: a paper champion RLFN. Furthermore, the shallow version of SRN-S achieves the second-shortest inference time as well as the second-smallest number of activations in the NTIRE2023 challenge. Code will be available at https://***/wnxbwyc/SRN. © 2023 ieee.

关键词： Deep learning

来源：评论

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A Generative Exploration of Cuisine Transfer

A Generative Exploration of Cuisine Transfer

引用

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

作者： Philip Wootaek Shin Ajay Narayanan Sridhar Jack Sampson Vijaykrishnan Narayanan The Pennsylvania State University

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Recent research has made significant progress in text-to-image editing, yet numerous areas remain under explored. In this work, we propose a novel application in the culinary arts, leveraging diffusion models to adjust a range of dishes into a variety of cuisines. Our approach infuses each dish with unique twists representative of diverse culinary traditions and ingredient profiles. We introduce the Cuisine Transfer task and a comprehensive framework for its execution, along with a curated dataset comprising over 1600 unique food samples at the ingredient level. Additionally, we propose three Cuisine Transfer task specific metrics to accurately evaluate our method and address common failure scenarios in existing image editing techniques. Our evaluations demonstrate that our method significantly outperforms baseline models on the Cuisine Transfer task.

关键词： Measurement Training computer vision Art Computational modeling conferences Text to image

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Discovering interpretable models of scientific image data with deep learning

Discovering interpretable models of scientific image data wi...

引用

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

作者： Christopher J. Soelistyo Alan R. Lowe The Alan Turing Institute

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

In this study, we demonstrate the possibility of finding interpretable, domain-appropriate models of biological images, and propose that such a strategy can be used to derive scientific insight in domains involving raw data. This is achieved by the novel, concerted application of existing methods, namely, disentangled representation learning, sparse deep neural network training and symbolic regression. We demonstrate their relevance to the field of bioimaging using a well-studied test problem of classifying cell states in microscopy data. We find that such methods can produce highly parsimonious models that achieve ~ 98% of the accuracy of black-box benchmark models, with a tiny fraction of the complexity, and greater domain-appropriateness, as tested by adversarial attacks. As such, we provide proof of concept that interpretable, high-performing models can be used to produce scientific explanations of some underlying biological phenomenon.

关键词： Training Deep learning computer vision Biological system modeling Computational modeling Microscopy conferences

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Disentangling Local and Global Information for Light Field Depth Estimation

Disentangling Local and Global Information for Light Field D...

引用

2023 ieee/cvf conference on computer vision and pattern recognition workshops, cvprw 2023

作者： Yang, Xueting Deng, Junli Chen, Rongshan Cong, Ruixuan Ke, Wei Sheng, Hao Communication University of China School of Information and Communication Engineering Beijing100024 China Beihang University State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beijing100191 China Beihang Hangzhou Innovation Institute Yuhang Xixi Octagon City Hangzhou310023 China Macao Polytechnic University Faculty of Applied Sciences 999078 China

ISBN: (纸本)9798350302493

Accurate depth estimation from light field images is essential for various applications. Deep learning-based techniques have shown great potential in addressing this problem while still face challenges such as sensitivity to occlusions and difficulties in handling untextured areas. To overcome these limitations, we propose a novel approach that utilizes both local and global features in the cost volume for depth estimation. Specifically, our hybrid cost volume network consists of two complementary sub-modules: a 2D ContextNet for global context information and a matching cost volume for local feature information. We also introduce an occlusion-aware loss that accounts for occlusion areas to improve depth estimation quality. We demonstrate the effectiveness of our approach on the UrbanLF and HCInew datasets, showing significant improvements over existing methods, especially in occluded and untextured regions. Our method disentangles local feature and global semantic information explicitly, reducing the occlusion and untextured area reconstruction error and improving the accuracy of depth estimation. © 2023 ieee.

关键词： Semantics

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Deep Video Codec Control for vision Models

Deep Video Codec Control for Vision Models

引用

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

作者： Christoph Reich Biplob Debnath Deep Patel Tim Prangemeier Daniel Cremers Srimat Chakradhar NEC Laboratories America Inc. TU Munich TU Darmstadt Munich Center for Machine Learning (MCML)

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Standardized lossy video coding is at the core of almost all real-world video processing pipelines. Rate control is used to enable standard codecs to adapt to different network bandwidth conditions or storage constraints. However, standard video codecs (e.g., H.264) and their rate control modules aim to minimize video distortion w.r.t. human quality assessment. We demonstrate empirically that standard-coded videos vastly deteriorate the performance of deep vision models. To overcome the deterioration of vision performance, this paper presents the first end-to-end learnable deep video codec control that considers both bandwidth constraints and downstream deep vision performance, while adhering to existing standardization. We demonstrate that our approach better preserves downstream deep vision performance than traditional standard video coding.

关键词： Video coding Computational modeling Face recognition Pipelines Bandwidth Self-supervised learning Distortion

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FlowIBR: Leveraging Pre-Training for Efficient Neural Image-Based Rendering of Dynamic Scenes

FlowIBR: Leveraging Pre-Training for Efficient Neural Image-...

引用

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

作者： Marcel Büsching Josef Bengtson David Nilsson Mårten Björkman KTH Royal Insitute of Technology Chalmers University of Technology

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

We introduce FlowIBR, a novel approach for efficient monocular novel view synthesis of dynamic scenes. Existing techniques already show impressive rendering quality but tend to focus on optimization within a single scene without leveraging prior knowledge, resulting in long optimization times per scene. FlowIBR circumvents this limitation by integrating a neural image-based rendering method, pretrained on a large corpus of widely available static scenes, with a per-scene optimized scene flow field. Utilizing this flow field, we bend the camera rays to counteract the scene dynamics, thereby presenting the dynamic scene as if it were static to the rendering network. The proposed method reduces per-scene optimization time by an order of magnitude, achieving comparable rendering quality to existing methods — all on a single consumer-grade GPU.

关键词： computer vision conferences Graphics processing units Rendering (computer graphics) Cameras pattern recognition Optimization

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CNT-NeRF: Carbon Nanotube Forest Depth Layer Decomposition in SEM Imagery using Generative Adversarial Networks

CNT-NeRF: Carbon Nanotube Forest Depth Layer Decomposition i...

引用

2023 ieee/cvf conference on computer vision and pattern recognition workshops, cvprw 2023

作者： Nguyen, Nguyen P. Surya, Ramakrishna Calyam, Prasad Palaniappan, Kannappan Maschmann, Matthew Bunyak, Filiz University of Missouri Department of Electrical Engineering and Computer Science ColumbiaMO United States University of Missouri Department of Mechanical and Aerospace Engineering ColumbiaMO United States

ISBN: (纸本)9798350302493

Carbon nanotube (CNT) forests are imaged using scanning electron microscopes (SEMs) that project their multilayered 3D structure into a single 2D image. Image analytics, particularly instance segmentation is needed to quantify structural characteristics and to predict correlations between structural morphology and physical properties. The inherent complexity of individual CNT structures is further increased in CNT forests due to density of CNTs, interactions between CNTs, occlusions, and lack of 3D information to resolve correspondences when multiple CNTs from different depths appear to cross in 2D. In this paper, we propose CNT-NeRF, a generative adversarial network (GAN) for simultaneous depth layer decomposition and segmentation of CNT forests in SEM images. The proposed network is trained using a multi-layer, photo-realistic synthetic dataset obtained by transferring the style of real CNT images to physics-based simulation data. Experiments show promising depth layer decomposition and accurate CNT segmentation results not only for the front layer but also for the partially occluded middle and back layers. This achievement is a significant step towards automated, image-based CNT forest structure characterization and physical property prediction. © 2023 ieee.

关键词： Carbon nanotubes

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Cluster Self-Refinement for Enhanced Online Multi-Camera People Tracking

Cluster Self-Refinement for Enhanced Online Multi-Camera Peo...

引用

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

作者： Jeongho Kim Wooksu Shin Hancheol Park Donghyuk Choi Nota Inc. Republic of Korea

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Recently, there has been a significant amount of research on Multi-Camera People Tracking (MCPT). MCPT presents more challenges compared to Multi-Object Single Camera Tracking, leading many existing studies to address them using offline methods. However, offline methods can only analyze pre-recorded videos, which presents less practical application in real industries compared to online methods. Therefore, we aimed to focus on resolving major problems that arise when using the online approach. Specifically, to address problems that could critically affect the performance of the online MCPT, such as storing inaccurate or low-quality appearance features and situations where a person is assigned multiple IDs, we proposed a Cluster Self-Refinement module. We achieved a third-place at the 2024 AI City Challenge Track 1 with a HOTA score of 60.9261%, and our code is available at https://***/nota-github/AIC2024_Track1_Nota.

关键词： Industries computer vision Accuracy conferences Urban areas Refining Pose estimation

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Multi-camera People Tracking With Mixture of Realistic and Synthetic Knowledge

Multi-camera People Tracking With Mixture of Realistic and S...

引用

2023 ieee/cvf conference on computer vision and pattern recognition workshops, cvprw 2023

作者： Nguyen, Quang Qui-Vinh Le, Huy Dinh-Anh Chau, Truc Thi-Thanh Luu, Duc Trung Chung, Nhat Minh Ha, Synh Viet-Uyen International University School of Computer Science and Engineering Ho Chi Minh City Viet Nam Vietnam National University Ho Chi Minh City Viet Nam

ISBN: (纸本)9798350302493

This paper presents a solution for Track 1 of the AI City Challenge 2023, which involves Multi-Camera People Tracking in indoor scenarios. The proposed framework comprises four modules: Vehicle detection, ReID feature extraction, single-camera multi-target tracking (SCMT), single-camera matching, and multi-camera matching. A significant contribution of our approach is the introduction of ID switch detection and ID switch splitting using the Gaussian mixture model, which efficiently addresses the problem of tracklets with ID switches. Furthermore, our system performs well in matching both synthetic and real data. The proposed R-matching algorithm performs exceptionally well in real scenarios despite being trained on synthetic data. Experimental results on the public test set of 2023 AI City Challenge Track 1 demonstrate the efficacy of the proposed approach, achieving an IDF1 of 94.17% and securing 2nd position on the leaderboard. Codes will be available at https://***/nguyenquivinhquang/Multi-camera-People-Tracking-With-Mixture-of-Realistic-and-Synthetic-Knowledge © 2023 ieee.

关键词： Feature extraction

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MIPI 2024 Challenge on Nighttime Flare Removal: Methods and Results

MIPI 2024 Challenge on Nighttime Flare Removal: Methods and ...

引用

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

作者： Yuekun Dai Dafeng Zhang Xiaoming Li Zongsheng Yue Chongyi Li Shangchen Zhou Ruicheng Feng Peiqing Yang Zhezhu Jin Guanqun Liu Chen Change Loy S-Lab Nanyang Technological University Samsung Research China Nankai University

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

The increasing demand for computational photography and imaging on mobile platforms has led to the widespread development and integration of advanced image sensors with novel algorithms in camera systems. However, the scarcity of high-quality data for research and the rare opportunity for in-depth exchange of views from industry and academia constrain the development of mobile intelligent photography and imaging (MIPI). Building on the achievements of the previous MIPI workshops held at ECCV 2022 and CVPR 2023, we introduce our third MIPI challenge including three tracks focusing on novel image sensors and imaging algorithms. In this paper, we summarize and review the Nighttime Flare Removal track on MIPI 2024. In total, 170 participants were successfully registered, and 14 teams submitted results in the final testing phase. The developed solutions in this challenge achieved state-of-the-art performance on Nighttime Flare Removal. More details of this challenge and the link to the dataset can be found at https://***/MIPI2024.

关键词： Photography Image sensors Industries computer vision Reviews conferences Focusing

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