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Danna Xue; Fei Yang; Pei Wang; Luis Herranz; Jinqiu Sun; Yu Zhu; Yanning Zhang |
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Title |
SlimSeg: Slimmable Semantic Segmentation with Boundary Supervision |
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Conference Article |
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2022 |
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30th ACM International Conference on Multimedia |
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6539-6548 |
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Accurate semantic segmentation models typically require significant computational resources, inhibiting their use in practical applications. Recent works rely on well-crafted lightweight models to achieve fast inference. However, these models cannot flexibly adapt to varying accuracy and efficiency requirements. In this paper, we propose a simple but effective slimmable semantic segmentation (SlimSeg) method, which can be executed at different capacities during inference depending on the desired accuracy-efficiency tradeoff. More specifically, we employ parametrized channel slimming by stepwise downward knowledge distillation during training. Motivated by the observation that the differences between segmentation results of each submodel are mainly near the semantic borders, we introduce an additional boundary guided semantic segmentation loss to further improve the performance of each submodel. We show that our proposed SlimSeg with various mainstream networks can produce flexible models that provide dynamic adjustment of computational cost and better performance than independent models. Extensive experiments on semantic segmentation benchmarks, Cityscapes and CamVid, demonstrate the generalization ability of our framework. |
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Lisboa, Portugal, October 2022 |
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Association for Computing Machinery |
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978-1-4503-9203-7 |
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MM |
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MACO; 600.161; 601.400 |
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Admin @ si @ XYW2022 |
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3758 |
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Zhaocheng Liu; Luis Herranz; Fei Yang; Saiping Zhang; Shuai Wan; Marta Mrak; Marc Gorriz |
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Title |
Slimmable Video Codec |
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Conference Article |
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2022 |
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CVPR 2022 Workshop and Challenge on Learned Image Compression (CLIC 2022, 5th Edition) |
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1742-1746 |
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Neural video compression has emerged as a novel paradigm combining trainable multilayer neural net-works and machine learning, achieving competitive rate-distortion (RD) performances, but still remaining impractical due to heavy neural architectures, with large memory and computational demands. In addition, models are usually optimized for a single RD tradeoff. Recent slimmable image codecs can dynamically adjust their model capacity to gracefully reduce the memory and computation requirements, without harming RD performance. In this paper we propose a slimmable video codec (SlimVC), by integrating a slimmable temporal entropy model in a slimmable autoencoder. Despite a significantly more complex architecture, we show that slimming remains a powerful mechanism to control rate, memory footprint, computational cost and latency, all being important requirements for practical video compression. |
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Virtual; 19 June 2022 |
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MACO; 601.379; 601.161 |
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Admin @ si @ LHY2022 |
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3687 |
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Jaykishan Patel; Alban Flachot; Javier Vazquez; David H. Brainard; Thomas S. A. Wallis; Marcus A. Brubaker; Richard F. Murray |
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Title |
A deep convolutional neural network trained to infer surface reflectance is deceived by mid-level lightness illusions |
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Year |
2023 |
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Journal of Vision |
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JV |
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23 |
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9 |
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4817-4817 |
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A long-standing view is that lightness illusions are by-products of strategies employed by the visual system to stabilize its perceptual representation of surface reflectance against changes in illumination. Computationally, one such strategy is to infer reflectance from the retinal image, and to base the lightness percept on this inference. CNNs trained to infer reflectance from images have proven successful at solving this problem under limited conditions. To evaluate whether these CNNs provide suitable starting points for computational models of human lightness perception, we tested a state-of-the-art CNN on several lightness illusions, and compared its behaviour to prior measurements of human performance. We trained a CNN (Yu & Smith, 2019) to infer reflectance from luminance images. The network had a 30-layer hourglass architecture with skip connections. We trained the network via supervised learning on 100K images, rendered in Blender, each showing randomly placed geometric objects (surfaces, cubes, tori, etc.), with random Lambertian reflectance patterns (solid, Voronoi, or low-pass noise), under randomized point+ambient lighting. The renderer also provided the ground-truth reflectance images required for training. After training, we applied the network to several visual illusions. These included the argyle, Koffka-Adelson, snake, White’s, checkerboard assimilation, and simultaneous contrast illusions, along with their controls where appropriate. The CNN correctly predicted larger illusions in the argyle, Koffka-Adelson, and snake images than in their controls. It also correctly predicted an assimilation effect in White's illusion. It did not, however, account for the checkerboard assimilation or simultaneous contrast effects. These results are consistent with the view that at least some lightness phenomena are by-products of a rational approach to inferring stable representations of physical properties from intrinsically ambiguous retinal images. Furthermore, they suggest that CNN models may be a promising starting point for new models of human lightness perception. |
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MACO; CIC |
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Admin @ si @ PFV2023 |
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3890 |
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Author |
Marcos V Conde; Florin Vasluianu; Javier Vazquez; Radu Timofte |
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Title |
Perceptual image enhancement for smartphone real-time applications |
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Conference Article |
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Year |
2023 |
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Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision |
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1848-1858 |
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Recent advances in camera designs and imaging pipelines allow us to capture high-quality images using smartphones. However, due to the small size and lens limitations of the smartphone cameras, we commonly find artifacts or degradation in the processed images. The most common unpleasant effects are noise artifacts, diffraction artifacts, blur, and HDR overexposure. Deep learning methods for image restoration can successfully remove these artifacts. However, most approaches are not suitable for real-time applications on mobile devices due to their heavy computation and memory requirements. In this paper, we propose LPIENet, a lightweight network for perceptual image enhancement, with the focus on deploying it on smartphones. Our experiments show that, with much fewer parameters and operations, our model can deal with the mentioned artifacts and achieve competitive performance compared with state-of-the-art methods on standard benchmarks. Moreover, to prove the efficiency and reliability of our approach, we deployed the model directly on commercial smartphones and evaluated its performance. Our model can process 2K resolution images under 1 second in mid-level commercial smartphones. |
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Waikoloa; Hawai; USA; January 2023 |
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WACV |
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MACO; CIC |
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no |
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Admin @ si @ CVV2023 |
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3900 |
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Yawei Li; Yulun Zhang; Radu Timofte; Luc Van Gool; Zhijun Tu; Kunpeng Du; Hailing Wang; Hanting Chen; Wei Li; Xiaofei Wang; Jie Hu; Yunhe Wang; Xiangyu Kong; Jinlong Wu; Dafeng Zhang; Jianxing Zhang; Shuai Liu; Furui Bai; Chaoyu Feng; Hao Wang; Yuqian Zhang; Guangqi Shao; Xiaotao Wang; Lei Lei; Rongjian Xu; Zhilu Zhang; Yunjin Chen; Dongwei Ren; Wangmeng Zuo; Qi Wu; Mingyan Han; Shen Cheng; Haipeng Li; Ting Jiang; Chengzhi Jiang; Xinpeng Li; Jinting Luo; Wenjie Lin; Lei Yu; Haoqiang Fan; Shuaicheng Liu; Aditya Arora; Syed Waqas Zamir; Javier Vazquez; Konstantinos G. Derpanis; Michael S. Brown; Hao Li; Zhihao Zhao; Jinshan Pan; Jiangxin Dong; Jinhui Tang; Bo Yang; Jingxiang Chen; Chenghua Li; Xi Zhang; Zhao Zhang; Jiahuan Ren; Zhicheng Ji; Kang Miao; Suiyi Zhao; Huan Zheng; YanYan Wei; Kangliang Liu; Xiangcheng Du; Sijie Liu; Yingbin Zheng; Xingjiao Wu; Cheng Jin; Rajeev Irny; Sriharsha Koundinya; Vighnesh Kamath; Gaurav Khandelwal; Sunder Ali Khowaja; Jiseok Yoon; Ik Hyun Lee; Shijie Chen; Chengqiang Zhao; Huabin Yang; Zhongjian Zhang; Junjia Huang; Yanru Zhang |
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Title |
NTIRE 2023 challenge on image denoising: Methods and results |
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Conference Article |
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2023 |
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Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops |
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1904-1920 |
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This paper reviews the NTIRE 2023 challenge on image denoising (σ = 50) with a focus on the proposed solutions and results. The aim is to obtain a network design capable to produce high-quality results with the best performance measured by PSNR for image denoising. Independent additive white Gaussian noise (AWGN) is assumed and the noise level is 50. The challenge had 225 registered participants, and 16 teams made valid submissions. They gauge the state-of-the-art for image denoising. |
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Vancouver; Canada; June 2023 |
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CVPRW |
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MACO; CIC |
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no |
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Admin @ si @ LZT2023 |
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3910 |
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Author |
Justine Giroux; Mohammad Reza Karimi Dastjerdi; Yannick Hold-Geoffroy; Javier Vazquez; Jean François Lalonde |
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Title |
Towards a Perceptual Evaluation Framework for Lighting Estimation |
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2024 |
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Arxiv |
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rogress in lighting estimation is tracked by computing existing image quality assessment (IQA) metrics on images from standard datasets. While this may appear to be a reasonable approach, we demonstrate that doing so does not correlate to human preference when the estimated lighting is used to relight a virtual scene into a real photograph. To study this, we design a controlled psychophysical experiment where human observers must choose their preference amongst rendered scenes lit using a set of lighting estimation algorithms selected from the recent literature, and use it to analyse how these algorithms perform according to human perception. Then, we demonstrate that none of the most popular IQA metrics from the literature, taken individually, correctly represent human perception. Finally, we show that by learning a combination of existing IQA metrics, we can more accurately represent human preference. This provides a new perceptual framework to help evaluate future lighting estimation algorithms. |
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Seattle; USA; June 2024 |
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CVPR |
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MACO; CIC |
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Admin @ si @ GDH2024 |
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3999 |
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Trevor Canham; Javier Vazquez; D Long; Richard F. Murray; Michael S Brown |
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Noise Prism: A Novel Multispectral Visualization Technique |
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2021 |
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31st Color and Imaging Conference |
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A novel technique for visualizing multispectral images is proposed. Inspired by how prisms work, our method spreads spectral information over a chromatic noise pattern. This is accomplished by populating the pattern with pixels representing each measurement band at a count proportional to its measured intensity. The method is advantageous because it allows for lightweight encoding and visualization of spectral information
while maintaining the color appearance of the stimulus. A four alternative forced choice (4AFC) experiment was conducted to validate the method’s information-carrying capacity in displaying metameric stimuli of varying colors and spectral basis functions. The scores ranged from 100% to 20% (less than chance given the 4AFC task), with many conditions falling somewhere in between at statistically significant intervals. Using this data, color and texture difference metrics can be evaluated and optimized to predict the legibility of the visualization technique. |
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CIC |
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MACO; CIC |
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Admin @ si @ CVL2021 |
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4000 |
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Chengyi Zou; Shuai Wan; Tiannan Ji; Marc Gorriz Blanch; Marta Mrak; Luis Herranz |
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Chroma Intra Prediction with Lightweight Attention-Based Neural Networks |
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2023 |
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IEEE Transactions on Circuits and Systems for Video Technology |
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TCSVT |
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34 |
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1 |
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549 - 560 |
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Neural networks can be successfully used for cross-component prediction in video coding. In particular, attention-based architectures are suitable for chroma intra prediction using luma information because of their capability to model relations between difierent channels. However, the complexity of such methods is still very high and should be further reduced, especially for decoding. In this paper, a cost-effective attention-based neural network is designed for chroma intra prediction. Moreover, with the goal of further improving coding performance, a novel approach is introduced to utilize more boundary information effectively. In addition to improving prediction, a simplification methodology is also proposed to reduce inference complexity by simplifying convolutions. The proposed schemes are integrated into H.266/Versatile Video Coding (VVC) pipeline, and only one additional binary block-level syntax flag is introduced to indicate whether a given block makes use of the proposed method. Experimental results demonstrate that the proposed scheme achieves up to −0.46%/−2.29%/−2.17% BD-rate reduction on Y/Cb/Cr components, respectively, compared with H.266/VVC anchor. Reductions in the encoding and decoding complexity of up to 22% and 61%, respectively, are achieved by the proposed scheme with respect to the previous attention-based chroma intra prediction method while maintaining coding performance. |
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MACO; LAMP |
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Admin @ si @ ZWJ2023 |
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3875 |
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Mingyi Yang; Luis Herranz; Fei Yang; Luka Murn; Marc Gorriz Blanch; Shuai Wan; Fuzheng Yang; Marta Mrak |
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Semantic Preprocessor for Image Compression for Machines |
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Conference Article |
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2023 |
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IEEE International Conference on Acoustics, Speech and Signal Processing |
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Visual content is being increasingly transmitted and consumed by machines rather than humans to perform automated content analysis tasks. In this paper, we propose an image preprocessor that optimizes the input image for machine consumption prior to encoding by an off-the-shelf codec designed for human consumption. To achieve a better trade-off between the accuracy of the machine analysis task and bitrate, we propose leveraging pre-extracted semantic information to improve the preprocessor’s ability to accurately identify and filter out task-irrelevant information. Furthermore, we propose a two-part loss function to optimize the preprocessor, consisted of a rate-task performance loss and a semantic distillation loss, which helps the reconstructed image obtain more information that contributes to the accuracy of the task. Experiments show that the proposed preprocessor can save up to 48.83% bitrate compared with the method without the preprocessor, and save up to 36.24% bitrate compared to existing preprocessors for machine vision. |
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Rodhes Islands; Greece; June 2023 |
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ICASSP |
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MACO; LAMP |
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Admin @ si @ YHY2023 |
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3912 |
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Saiping Zhang, Luis Herranz, Marta Mrak, Marc Gorriz Blanch, Shuai Wan, Fuzheng Yang |
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PeQuENet: Perceptual Quality Enhancement of Compressed Video with Adaptation-and Attention-based Network |
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2022 |
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Arxiv |
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In this paper we propose a generative adversarial network (GAN) framework to enhance the perceptual quality of compressed videos. Our framework includes attention and adaptation to different quantization parameters (QPs) in a single model. The attention module exploits global receptive fields that can capture and align long-range correlations between consecutive frames, which can be beneficial for enhancing perceptual quality of videos. The frame to be enhanced is fed into the deep network together with its neighboring frames, and in the first stage features at different depths are extracted. Then extracted features are fed into attention blocks to explore global temporal correlations, followed by a series of upsampling and convolution layers. Finally, the resulting features are processed by the QP-conditional adaptation module which leverages the corresponding QP information. In this way, a single model can be used to enhance adaptively to various QPs without requiring multiple models specific for every QP value, while having similar performance. Experimental results demonstrate the superior performance of the proposed PeQuENet compared with the state-of-the-art compressed video quality enhancement algorithms. |
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MACO; no proj |
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Admin @ si @ ZHM2022b |
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3819 |
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Petia Radeva; J. Guerrero; C. Molina |
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A Physics-Based Kohonen Ring. |
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1998 |
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SPIE – Medical Imaging. |
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San Diego; CA; USA |
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MILAB |
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BCNPCL @ bcnpcl @ RGM1998 |
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19 |
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C. Molina; G.P. Prause; Petia Radeva; M. Sonka |
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Catheter Path Reconstruction from Biplane Angiography using 3D Snakes. |
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1998 |
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SPIE – Medical imaging. |
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San Diego; CA; USA |
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MILAB |
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no |
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Call Number |
BCNPCL @ bcnpcl @ MPR1998 |
Serial |
22 |
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| Permanent link to this record |
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Author |
Xose M. Pardo; Petia Radeva; Juan J. Villanueva |
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Title |
Self-Training Statistic Snake for Image Segmentation and Tracking. |
Type |
Miscellaneous |
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Year |
1999 |
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Venice |
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no |
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Call Number |
BCNPCL @ bcnpcl @ PRV1999 |
Serial |
26 |
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| Permanent link to this record |
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Author |
X. Binefa; Petia Radeva; J.A. Cortijo; J. Garcia |
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Title |
Contour detection and color influence in defocused environtments. |
Type |
Miscellaneous |
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Year |
1998 |
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MILAB |
Approved |
no |
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Call Number |
BCNPCL @ bcnpcl @ BRC1998 |
Serial |
28 |
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| Permanent link to this record |
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Author |
V. Valev; B. Sankur; Petia Radeva |
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Title |
Generalized Non-Reducible Descriptors. |
Type |
Miscellaneous |
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Year |
1997 |
Publication |
Technical Report. |
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Notes |
MILAB |
Approved |
no |
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Call Number |
BCNPCL @ bcnpcl @ VSR1997 |
Serial |
65 |
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| Permanent link to this record |
