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Pre-Trained Image Processing Transformer

Pre-Trained Image Processing Transformer

引用

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

作者： Chen, Hanting Wang, Yunhe Guo, Tianyu Xu, Chang Deng, Yiping Liu, Zhenhua Ma, Siwei Xu, Chunjing Xu, Chao Gao, Wen Peking Univ Dept Machine Intelligence Key Lab Machine Percept MOE Beijing Peoples R China Huawei Technol Noahs Ark Lab Shenzhen Peoples R China Univ Sydney Fac Engn Sch Comp Sci Sydney NSW Australia Huawei Technol Cent Software Inst Shenzhen Peoples R China Peking Univ Sch Elect Engn & Comp Sci Inst Digital Media Beijing Peoples R China Peng Cheng Lab Shenzhen Peoples R China

ISBN: (纸本)9781665445092

As the computing power of modern hardware is increasing strongly, pre-trained deep learning models (e.g., BERT, GPT-3) learned on large-scale datasets have shown their effectiveness over conventional methods. The big progress is mainly contributed to the representation ability of transformer and its variant architectures. In this paper, we study the low-level computer vision task (e.g., denoising, super-resolution and deraining) and develop a new pre-trained model, namely, image processing transformer (IPT). To maximally excavate the capability of transformer, we present to utilize the well-known ImageNet benchmark for generating a large amount of corrupted image pairs. The IPT model is trained on these images with multi-heads and multi-tails. In addition, the contrastive learning is introduced for well adapting to different image processing tasks. The pre-trained model can therefore efficiently employed on desired task after fine-tuning. With only one pre-trained model, IPT outperforms the current state-of-the-art methods on various low-level benchmarks.

关键词： Adaptation models computer vision Computational modeling Image processing Superresolution Noise reduction Benchmark testing

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Leveraging per Image-Token Consistency for vision-Language Pre-training

Leveraging per Image-Token Consistency for Vision-Language P...

引用

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

作者： Gou, Yunhao Ko, Tom Yang, Hansi Kwok, James Zhang, Yu Wang, Mingxuan Southern Univ Sci & Technol Shenzhen Peoples R China Hong Kong Univ Sci & Technol Hong Kong Peoples R China ByteDance Ai Lab Beijing Peoples R China Peng Cheng Lab Shenzhen Peoples R China

ISBN: (纸本)9798350301298

Most existing vision-language pre-training (VLP) approaches adopt cross-modal masked language modeling (CMLM) to learn vision-language associations. However, we find that CMLM is insufficient for this purpose according to our observations: (1) Modality bias: a considerable amount of masked tokens in CMLM can be recovered with only the language information, ignoring the visual inputs. (2) Under-utilization of the unmasked tokens: CMLM primarily focuses on the masked token but it cannot simultaneously leverage other tokens to learn vision-language associations. To handle those limitations, we propose EPIC (lEveraging Per Image-Token Consistency for vision-language pre-training). In EPIC, for each image-sentence pair, we mask tokens that are salient to the image (i.e., Saliency-based Masking Strategy) and replace them with alternatives sampled from a language model (i.e., Inconsistent Token Generation Procedure), and then the model is required to determine for each token in the sentence whether it is consistent with the image (i.e., Image-Token Consistency Task). The proposed EPIC method is easily combined with pre-training methods. Extensive experiments show that the combination of the EPIC method and state-of-the-art pre-training approaches, including ViLT, ALBEF, METER, and X-VLM, leads to significant improvements on downstream tasks. Our coude is released at https://***/gyhdog99/epic

关键词： Multi-modal learning

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DVC: An End-to-end Deep Video Compression Framework 32

DVC: An End-to-end Deep Video Compression Framework

引用

32nd ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Lu, Guo Ouyang, Wanli Xu, Dong Zhang, Xiaoyun Cai, Chunlei Gao, Zhiyong Shanghai Jiao Tong Univ Shanghai Peoples R China Univ Sydney SenseTime Comp Vis Res Grp Sydney NSW Australia Univ Sydney Sydney NSW Australia

ISBN: (纸本)9781728132938

Conventional video compression approaches use the predictive coding architecture and encode the corresponding motion information and residual information. In this paper, taking advantage of both classical architecture in the conventional video compression method and the powerful nonlinear representation ability of neural networks, we propose the first end-to-end video compression deep model that jointly optimizes all the components for video compression. Specifically, learning based optical flow estimation is utilized to obtain the motion information and reconstruct the current frames. Then we employ two auto-encoder style neural networks to compress the corresponding motion and residual information. All the modules are jointly learned through a single loss function, in which they collaborate with each other by considering the trade-off between reducing the number of compression bits and improving quality of the decoded video. Experimental results show that the proposed approach can outperform the widely used video coding standard H.264 in terms of PSNR and be even on par with the latest standard H.265 in terms of MS-SSIM.

关键词： Low-level vision vision Applications and Systems

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Learning to Segment Actions from Visual and Language Instructions via Differentiable Weak Sequence Alignment

Learning to Segment Actions from Visual and Language Instruc...

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

作者： Shen, Yuhan Wang, Lu Elhamifar, Ehsan Northeastern Univ Boston MA 02115 USA Univ Michigan Ann Arbor MI 48109 USA

ISBN: (纸本)9781665445092

We address the problem of unsupervised localization of task-relevant actions (key-steps) and feature learning in instructional videos using both visual and language instructions. Our key observation is that the sequences of visual and linguistic key-steps are weakly aligned: there is an ordered one-to-one correspondence between most visual and language key-steps, while some key-steps in one modality are absent in the other. To recover the two sequences, we develop an ordered prototype learning module, which extracts visual and linguistic prototypes representing key-steps. To find weak alignment and perform feature learning, we develop a differentiable weak sequence alignment (DWSA) method that finds ordered one-to-one matching between sequences while allowing some items in a sequence to stay unmatched. We develop an efficient forward and backward algorithm for computing the alignment and the loss derivative with respect to parameters of visual and language feature learning modules. By experiments on two instructional video datasets, we show that our method significantly improves the state of the art.

关键词： Location awareness Visualization computer vision Computational modeling Prototypes Linguistics Feature extraction

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Implicit Feature Alignment: Learn to Convert Text Recognizer to Text Spotter

Implicit Feature Alignment: Learn to Convert Text Recognizer...

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

作者： Wang, Tianwei Zhu, Yuanzhi Jin, Lianwen Peng, Dezhi Li, Zhe He, Mengchao Wang, Yongpan Luo, Canjie South China Univ Technol Sch Elect & Informat Engn Guangzhou Peoples R China Alibaba Grp Hangzhou Peoples R China Guangdong Artificial Intelligence & Digital Econ Guangzhou Peoples R China

ISBN: (纸本)9781665445092

Text recognition is a popular research subject with many associated challenges. Despite the considerable progress made in recent years, the text recognition task itself is still constrained to solve the problem of reading cropped line text images and serves as a subtask of optical character recognition (OCR) systems. As a result, the final text recognition result is limited by the performance of the text detector. In this paper, we propose a simple, elegant and effective paradigm called Implicit Feature Alignment (IFA), which can be easily integrated into current text recognizers, resulting in a novel inference mechanism called IFA-inference. This enables an ordinary text recognizer to process multi-line text such that text detection can be completely freed. Specifically, we integrate IFA into the two most prevailing text recognition streams (attention-based and CTC-based) and propose attention-guided dense prediction (ADP) and Extended CTC (ExCTC). Furthermore, the Wasserstein-based Hollow Aggregation Cross-Entropy (WH-ACE) is proposed to suppress negative predictions to assist in training ADP and ExCTC. We experimentally demonstrate that IFA achieves state-of-the-art performance on end-to-end document recognition tasks while maintaining the fastest speed, and ADP and ExCTC complement each other on the perspective of different application scenarios. Code will be available at https://***/Wang-Tianwei/Implicit-feature-alignment.

关键词： Training Text recognition Inference mechanisms Pipelines Neural networks Optical detectors Optical imaging

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Physical Inertial Poser (PIP): Physics-aware Real-time Human Motion Tracking from Sparse Inertial Sensors

Physical Inertial Poser (PIP): Physics-aware Real-time Human...

引用

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

作者： Yi, Xinyu Zhou, Yuxiao Habermann, Marc Shimada, Soshi Golyanik, Vladislav Theobalt, Christian Xu, Feng Tsinghua Univ Sch Software Beijing Peoples R China Tsinghua Univ BNRist Beijing Peoples R China Saarland Informat Campus Max Planck Inst Informat Saarbrucken Germany

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Motion capture from sparse inertial sensors has shown great potential compared to image-based approaches since occlusions do not lead to a reduced tracking quality and the recording space is not restricted to be within the viewing frustum of the camera. However, capturing the motion and global position only from a sparse set of inertial sensors is inherently ambiguous and challenging. In consequence, recent state-of-the-art methods can barely handle very long period motions, and unrealistic artifacts are common due to the unawareness of physical constraints. To this end, we present the first method which combines a neural kinematics estimator and a physics-aware motion optimizer to track body motions with only 6 inertial sensors. The kinematics module first regresses the motion status as a reference, and then the physics module refines the motion to satisfy the physical constraints. Experiments demonstrate a clear improvement over the state of the art in terms of capture accuracy, temporal stability, and physical correctness.

关键词： computer vision Tracking Inertial sensors Pose estimation Cameras Real-time systems Motion capture

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Generative Flows with Invertible Attentions

Generative Flows with Invertible Attentions

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

作者： Sukthanker, Rhea Sanjay Huang, Zhiwu Kumar, Suryansh Timofte, Radu Van Gool, Luc Swiss Fed Inst Technol CVL Zurich Switzerland SMU SAVG Singapore Singapore Katholieke Univ Leuven PSI Leuven Belgium

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Flow-based generative models have shown an excellent ability to explicitly learn the probability density function of data via a sequence of invertible transformations. Yet, learning attentions in generative flows remains understudied, while it has made breakthroughs in other domains. To fill the gap, this paper introduces two types of invertible attention mechanisms, i.e., map-based and transformer-based attentions, for both unconditional and conditional generative flows. The key idea is to exploit a masked scheme of these two attentions to learn long-range data dependencies in the context of generative flows. The masked scheme allows for invertible attention modules with tractable Jacobian determinants, enabling its seamless integration at any positions of the flow-based models. The proposed attention mechanisms lead to more efficient generative flows, due to their capability of modeling the long-term data dependencies. Evaluation on multiple image synthesis tasks shows that the proposed attention flows result in efficient models and compare favorably against the state-of-the-art unconditional and conditional generative flows.

关键词： Jacobian matrices computer vision Image synthesis Computational modeling Probability density function Transformers Data models

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Shortcomings of Top-Down Randomization-Based Sanity Checks for Evaluations of Deep Neural Network Explanations

Shortcomings of Top-Down Randomization-Based Sanity Checks f...

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

作者： Binder, Alexander Weber, Leander Lapuschkin, Sebastian Montavon, Gregoire Muller, Klaus-Robert Samek, Wojciech SIT Singapore IICT Cluster Singapore Singapore UiO Oslo Oslo Norway Fraunhofer HHI Berlin Germany FU Berlin Berlin Germany BIFOLD Berlin Berlin Germany TU Berlin Berlin Germany Korea Univ Seoul Seoul South Korea MPI Saarbrucken Saarbrucken Germany SUTD Singapore Singapore Singapore

ISBN: (纸本)9798350301298

While the evaluation of explanations is an important step towards trustworthy models, it needs to be done carefully, and the employed metrics need to be well-understood. Specifically model randomization testing can be overinterpreted if regarded as a primary criterion for selecting or discarding explanation methods. To address shortcomings of this test, we start by observing an experimental gap in the ranking of explanation methods between randomization-based sanity checks [1] and model output faithfulness measures (e.g. [20]). We identify limitations of model-randomization-based sanity checks for the purpose of evaluating explanations. Firstly, we show that uninformative attribution maps created with zero pixel-wise covariance easily achieve high scores in this type of checks. Secondly, we show that top-down model randomization preserves scales of forward pass activations with high probability. That is, channels with large activations have a high probility to contribute strongly to the output, even after randomization of the network on top of them. Hence, explanations after randomization can only be expected to differ to a certain extent. This explains the observed experimental gap. In summary, these results demonstrate the inadequacy of model-randomization-based sanity checks as a criterion to rank attribution methods.

关键词： Explainable computer vision

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Few-Shot Human Motion Transfer by Personalized Geometry and Texture Modeling

Few-Shot Human Motion Transfer by Personalized Geometry and ...

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

作者： Huang, Zhichao Han, Xintong Xu, Jia Zhang, Tong Hong Kong Univ Sci & Technol Hong Kong Peoples R China Huya Inc Guangzhou Peoples R China

ISBN: (纸本)9781665445092

We present a new method for few-shot human motion transfer that achieves realistic human image generation with only a small number of appearance inputs. Despite recent advances in single person motion transfer, prior methods often require a large number of training images and take long training time. One promising direction is to perform few-shot human motion transfer, which only needs a few of source images for appearance transfer. However, it is particularly challenging to obtain satisfactory transfer results. In this paper, we address this issue by rendering a human texture map to a surface geometry (represented as a UV map), which is personalized to the source person. Our geometry generator combines the shape information from source images, and the pose information from 2D keypoints to synthesize the personalized UV map. A texture generator then generates the texture map conditioned on the texture of source images to fill out invisible parts. Furthermore, we may fine-tune the texture map on the manifold of the texture generator from a few source images at the test time, which improves the quality of the texture map without over-fitting or artifacts. Extensive experiments show the proposed method outperforms state-of-the-art methods both qualitatively and quantitatively.

关键词： Geometry Training Manifolds Hair Shape Image synthesis Rendering (computer graphics)

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Virtual Correspondence: Humans as a Cue for Extreme-View Geometry

Virtual Correspondence: Humans as a Cue for Extreme-View Geo...

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

作者： Ma, Wei-Chiu Yang, Anqi Joyce Wang, Shenlong Urtasun, Raquel Torralba, Antonio MIT 77 Massachusetts Ave Cambridge MA 02139 USA Univ Toronto Toronto ON Canada Univ Illinois Urbana IL USA Waabi Toronto ON Canada Vector Inst Toronto ON Canada

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Recovering the spatial layout of the cameras and the geometry of the scene from extreme-view images is a long-standing challenge in computer vision. Prevailing 3D reconstruction algorithms often adopt the image matching paradigm and presume that a portion of the scene is co-visible across images, yielding poor performance when there is little overlap among inputs. In contrast, humans can associate visible parts in one image to the corresponding invisible components in another image via prior knowledge of the shapes. Inspired by this fact, we present a novel concept called virtual correspondences (VCs). VCs are a pair of pixels from two images whose camera rays intersect in 3D. Similar to classic correspondences, VCs conform with epipolar geometry;unlike classic correspondences, VCs do not need to be co-visible across views. Therefore VCs can be established and exploited even if images do not overlap. We introduce a method to find virtual correspondences based on humans in the scene. We showcase how VCs can be seamlessly integrated with classic bundle adjustment to recover camera poses across extreme views. Experiments show that our method significantly outperforms state-of-the-art camera pose estimation methods in challenging scenarios and is comparable in the traditional densely captured setup. Our approach also unleashes the potential of multiple downstream tasks such as scene reconstruction from multi-view stereo and novel view synthesis in extreme-view scenarios(1).

关键词： Geometry computer vision Visualization Shape Pose estimation Semantics Layout

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