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Cross-Domain Multi-task Learning for Object Detection and Saliency Estimation

Cross-Domain Multi-task Learning for Object Detection and Sa...

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

作者： Khattar, Apoorv Hegde, Srinidhi Hebbalaguppe, Ramya TCS Res Mumbai Maharashtra India

ISBN: (纸本)9781665448994

Multi-task learning (MTL) is a learning paradigm that aims at joint optimization of multiple tasks using a single neural network for better performance and generalization. In practice, MTL rests on the inherent assumption of availability of common datasets with ground truth labels for each of the downstream tasks. However, collecting such a common annotated dataset is laborious for complex computer vision tasks such as the saliency estimation which would require the eye fixation points as the ground truth data. To this end, we propose a novel MTL framework in the absence of common annotated dataset for joint estimation of important downstream tasks in computer vision - object detection and saliency estimation. Unlike many state-of-the-art methods, that rely on common annotated datasets for training, we consider the annotations from different datasets for jointly training different tasks, calling this setting as cross-domain MTL. We adapt MUTAN [3] framework to fuse features from different datasets to learn domain invariant features capturing the relatedness of different tasks. We demonstrate the improvement in the performance and generalizability of our MTL architecture. We also show that the proposed MTL network offers a 13% reduction in memory footprint due to parameter sharing between the related tasks.

关键词： Training computer vision Neural networks Estimation Object detection computer architecture pattern recognition

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Subjective Quality Optimized Efficient Image Compression

Subjective Quality Optimized Efficient Image Compression

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

作者： Wang, Xining Chen, Tong Ma, Zhan Nanjing Univ Vis Lab Nanjing Peoples R China

ISBN: (纸本)9781665448994

In this paper, we propose an efficient image compression framework that is optimized for subjective quality. Our framework is mainly based on the NLAIC (NonLocal Attention Optimized Image Coding) model which applied Variational Autoencoder (VAE) and non-local attention module to end-to-end image compression. This work makes two major contributions to the NLAIC framework. First, our models are optimized for subjective-friendly loss functions rather than conventional MSE (Mean Squared Error) or MS-SSIM (Multiscale Structural Similarity) which was widely used in previous works. Second, we introduce block-based inference mechanism to reduce the running memory consumption of the image compression network, and suggest a partial post-processing step to alleviate block artifacts caused by block-based inference in a lightweight computational fashion. Experiments have proved that the image reconstructed by our method can preserve more texture details than models trained for optimal MSE or MS-SSIM and also present capability for high-throughput decoding.

关键词： Measurement computer vision Image coding Inference mechanisms conferences Memory management Distortion

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NTIRE 2022 Burst Super-Resolution Challenge

NTIRE 2022 Burst Super-Resolution Challenge

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

作者： Bhat, Goutam Danelljan, Martin Timofte, Radu Cao, Yizhen Cao, Yuntian Chen, Meiya Chen, Xihao Cheng, Shen Dudhane, Akshay Fan, Haoqiang Gang, Ruipeng Gao, Jian Gu, Yan Huang, Jie Huang, Liufeng Jo, Youngsu Kang, Sukju Khan, Salman Khan, Fahad Shahbaz Kondo, Yuki Li, Chenghua Li, Fangya Li, Jinjing Li, Youwei Li, Zechao Liu, Chenming Liu, Shuaicheng Liu, Zikun Liu, Zhuoming Luo, Ziwei Luo, Zhengxiong Mehta, Nancy Murala, Subrahmanyam Nam, Yoonchan Nakatani, Chihiro Ostyakov, Pavel Pan, Jinshan Song, Ge Sun, Jian Sun, Long Tang, Jinhui Ukita, Norimichi Wen, Zhihong Wu, Qi Wu, Xiaohe Xiao, Zeyu Xiong, Zhiwei Xu, Rongjian Xu, Ruikang Yan, Youliang Yang, Jialin Yang, Wentao Yang, Zhongbao Yasue, Fuma Yao, Mingde Yu, Lei Zhang, Cong Zamir, Syed Waqas Zhang, Jianxing Zhang, Shuohao Zhang, Zhilu Zheng, Qian Zhou, Gaofeng Zhussip, Magauiya Zou, Xueyi Zuo, Wangmeng Swiss Fed Inst Technol Comp Vis Lab Zurich Switzerland Julius Maximilian Univ Wurzburg Wurzburg Germany Commun Univ China Beijing Peoples R China UHDTV Res & Applicat Lab Beijing Peoples R China Chinese Acad Sci Inst Automat Beijing Peoples R China Harbin Inst Technol Harbin Peoples R China South China Univ Technol Guangzhou Peoples R China Megvii Technol Beijing Peoples R China Univ Elect Sci & Technol China UESTC Chengdu Peoples R China Xiaomi Beijing Peoples R China Huawei Noahs Ark Lab Shenzhen Peoples R China Nanjing Univ Sci & Technol Nanjing Peoples R China SRC B Beijing Peoples R China CASIA Beijing Peoples R China Indian Inst Technol Ropar IIT Ropar Rupnagar Punjab India Mohamed Bin Zayed Univ AI MBZUAI Abu Dhabi U Arab Emirates Incept Inst Artificial Intelligence IIAI Abu Dhabi U Arab Emirates MBZUAI Abu Dhabi U Arab Emirates Australian Natl Univ ANU Canberra ACT Australia Linkoping Univ Linkoping Sweden Toyota Technol Inst TTI Nagoya Aichi Japan Sogang Univ Seoul South Korea UESTC Chengdu Peoples R China USTC Hefei Peoples R China WHU Wuhan Peoples R China Univ Sci & Technol China Hefei Peoples R China

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Burst super-resolution has received increased attention in recent years due to its applications in mobile photography. By merging information from multiple shifted images of a scene, burst super-resolution aims to recover details which otherwise cannot be obtained using a simple input image. This paper reviews the NTIRE 2022 challenge on burst super-resolution. In the challenge, the participants were tasked with generating a clean RGB image with 4x higher resolution, given a RAW noisy burst as input. That is, the methods need to perform joint denoising, demosaicking, and super-resolution. The challenge consisted of 2 tracks. Track 1 employed synthetic data, where pixel-accurate high-resolution ground truths are available. Track 2 on the other hand used real-world bursts captured from a handheld camera, along with approximately aligned reference images captured using a DSLR. 14 teams participated in the final testing phase. The top performing methods establish a new state-of-the-art on the burst super-resolution task.

关键词： Training Photography Superresolution Noise reduction Network architecture Transformers pattern recognition

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BiCnet-TKS: Learning Efficient Spatial-Temporal Representation for Video Person Re-Identification

BiCnet-TKS: Learning Efficient Spatial-Temporal Representati...

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

作者： Hou, Ruibing Chang, Hong Ma, Bingpeng Huang, Rui Shan, Shiguang Chinese Acad Sci Inst Comp Technol CAS Key Lab Intelligent Informat Proc Beijing 100190 Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China Chinese Univ Hong Kong Shenzhen Inst Artificial Intelligence & Robot Soc Shenzhen 518172 Guangdong Peoples R China CAS Ctr Excellence Brain Sci & Intelligence Techn Shanghai 200031 Peoples R China

ISBN: (纸本)9781665445092

In this paper, we present an efficient spatial-temporal representation for video person re-identification (reID). Firstly, we propose a Bilateral Complementary Network (BiCnet) for spatial complementarity modeling. Specifically, BiCnet contains two branches. Detail Branch processes frames at original resolution to preserve the detailed visual clues, and Context Branch with a down-sampling strategy is employed to capture long-range contexts. On each branch, BiCnet appends multiple parallel and diverse attention modules to discover divergent body parts for consecutive frames, so as to obtain an integral characteristic of target identity. Furthermore, a Temporal Kernel Selection (TKS) block is designed to capture short-term as well as long-term temporal relations by an adaptive mode. TKS can be inserted into BiCnet at any depth to construct BiCnet-TKS for spatial-temporal modeling. Experimental results on multiple benchmarks show that BiCnet-TKS outperforms state-of-the-arts with about 50% less computations.

关键词： Visualization computer vision Codes Computational modeling Benchmark testing pattern recognition Computational efficiency

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CNN-based morphological decomposition of X-ray images for details and defects contrast enhancement

CNN-based morphological decomposition of X-ray images for de...

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

作者： Madmad, Tahani Delinte, Nicolas De Vleeschouwer, Christophe UCLouvain ICTEAM Louvain La Neuve Belgium

ISBN: (纸本)9781665448994

This paper introduces a new learning based framework for X-ray images that relies on a morphological decomposition of the signal into two main components, separating images into local textures and piecewise smooth (cartoon) parts. The piecewise smooth component corresponds to the spatial variation of the average density of the objects, whereas the local texture component presents the inspected objects singularities. Our method builds on two convolutional neural network (CNN) branches to decompose an input image into its two morphological components. This CNN is trained with synthetic data, generated by randomly picking piecewise smooth and singular patterns in a parametric dictionary and enforcing the sum of the CNN branches to approximate the identity mapping. We demonstrate the relevance of the decomposition by enhancing the local textures component compared to the piecewise smooth one. Those enhanced images compare favorably to the ones obtained with existing works destined to visualize High Dynamic Range (HDR) images such as tone-mapping algorithms.

关键词： computer vision Dictionaries Heuristic algorithms conferences Data visualization Dynamic range Approximation algorithms

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A Universal Encoder Rate Distortion Optimization Framework for Learned Compression

A Universal Encoder Rate Distortion Optimization Framework f...

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

作者： Zhao, Jing Li, Bin Li, Jiahao Xiong, Ruiqin Lu, Yan Peking Univ Beijing Peoples R China Microsoft Res Asia Beijing Peoples R China

ISBN: (纸本)9781665448994

Learning-based image compression has drawn increasing attention in recent years. Despite impressive progress has been made, it still lacks a universal encoder optimization method to seek efficient representation for different images. In this paper, we develop a universal rate distortion optimization framework for learning-based compression, which adaptively optimizes latents and side information together for each image. The proposed framework is independent of network architecture and can be flexibly applied to existing and potential future compression networks. Experimental results demonstrate that we can achieve 6.6% bit rate saving against the latest traditional codec, i.e., VVC, yielding the state-of-the-art compression ratio. Moreover, with the proposed optimization framework, we win the first place in CLIC validation phase for all the three different bit rates in terms of PSNR.

关键词： computer vision Image coding Codecs conferences Bit rate Rate-distortion Optimization methods

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DeepObjStyle: Deep Object-based Photo Style Transfer

DeepObjStyle: Deep Object-based Photo Style Transfer

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

作者： Mastan, Indra Deep Raman, Shanmuganathan Indian Inst Technol Gandhinagar Gandhinagar Gujarat India

ISBN: (纸本)9781665448994

One of the major challenges of style transfer is the appropriate image features supervision between the output image and the input images (style and content). An efficient strategy would be to define an object map between the objects of the style and the content images. However, such a mapping is not well established when there are semantic objects of different types and numbers in the style and the content images. It also leads to content mismatch in the style transfer output, which could reduce the visual quality of the results. We propose an object-based style transfer approach, called DeepObjStyle, for the style supervision in the training data-independent framework. DeepObjStyle preserves the semantics of the objects and achieves better style transfer in the challenging scenario when the style and the content images have a mismatch of image features. We also perform style transfer of images containing a word cloud to demonstrate that DeepObjStyle enables an appropriate image features supervision. We validate the results using quantitative comparisons and user studies.

关键词： Training Visualization computer vision conferences Semantics Tag clouds Quality assessment

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Deep Learning based Spatial-Temporal In-loop filtering for Versatile Video Coding

Deep Learning based Spatial-Temporal In-loop filtering for V...

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

作者： Pham, Chi D. K. Fu, Chen Zhou, Jinjia Hosei Univ Tokyo Japan JST PRESTO Saitama Japan

ISBN: (纸本)9781665448994

The existing deep learning-based Versatile Video Coding (VVC) in-loop filtering (ILF) enhancement works mainly focus on learning the one-to-one mapping between the reconstructed and the original video frame, ignoring the potential resources at encoder and decoder. This work proposes a deep learning-based Spatial-Temporal In-Loop filtering (STILF) that takes advantage of the coding information to improve VVC in-loop filtering. Each CTU is filtered by VVC default in-loop filtering, self-enhancement Convolutional neural network (CNN) with CU map (SEC), and the reference-based enhancement CNN with the optical flow (REO). Bits indicating ILF mode are encoded under CABAC regular mode. Experimental results show that 3.78%, 6.34%, 6%, and 4.64% BD-rate reductions are obtained under All Intra, Low Delay P, Low Delay B, and Random Access configurations, respectively.

关键词： Video coding Optical filters Filtering Delays Decoding pattern recognition Convolutional neural networks

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Multi-task Learning with Attention for End-to-end Autonomous Driving

Multi-task Learning with Attention for End-to-end Autonomous...

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

作者： Ishihara, Keishi Kanervisto, Anssi Miura, Jun Hautamaki, Ville Toyohashi Univ Technol Toyohashi Aichi Japan Univ Eastern Finland Kuopio Finland

ISBN: (纸本)9781665448994

Autonomous driving systems need to handle complex scenarios such as lane following, avoiding collisions, taking turns, and responding to traffic signals. In recent years, approaches based on end-to-end behavioral cloning have demonstrated remarkable performance in point-to-point navigational scenarios, using a realistic simulator and standard benchmarks. Offline imitation learning is readily available, as it does not require expensive hand annotation or interaction with the target environment, but it is difficult to obtain a reliable system. In addition, existing methods have not specifically addressed the learning of reaction for traffic lights, which are a rare occurrence in the training datasets. Inspired by the previous work on multi-task learning and attention modeling, we propose a novel multi-task attention-aware network in the conditional imitation learning (CIL) framework. This does not only improve the success rate of standard benchmarks, but also the ability to react to traffic lights, which we show with standard benchmarks.

关键词： Training Visualization Benchmark testing pattern recognition Automobiles Reliability Task analysis

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EBSR: Feature Enhanced Burst Super-Resolution with Deformable Alignment

EBSR: Feature Enhanced Burst Super-Resolution with Deformabl...

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

作者： Luo, Ziwei Yu, Lei Mo, Xuan Li, Youwei Jia, Lanpeng Fan, Haoqiang Sun, Jian Liu, Shuaicheng Megvii Technol Beijing Peoples R China Univ Elect Sci & Technol China Chengdu Sichuan Peoples R China

ISBN: (纸本)9781665448994

We propose a novel architecture to handle the problem of multi frame super-resolution (MFSR). The proposed framework is known as Enhanced Burst Super-Resolution (EBSR), which divides the MFSR problem into three parts: alignment, fusion, and reconstruction. We propose a Feature Enhanced Pyramid Cascading and Deformable convolution (FEPCD) module to align multiple low-resolution burst images in the feature level. And then the aligned features are fused by a Cross Non-Local Fusion (CNLF) module. Finally, the SR image is reconstructed by the Long Range Concatenation Network (LRCN). In addition, we build a cascading residual pathway structure (CR) to improve the performance. We conduct several experiments to analyze and demonstrate these modules. Our EBSR model won the champion in the real track and second place in the synthetic track in the NTIRE21 Burst Super-Resolution Challenge.

关键词： Training computer vision Correlation Tracking Convolution conferences Superresolution

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