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Extracting Motion and Appearance via Inter-Frame Attention for Efficient Video Frame Interpolation

Extracting Motion and Appearance via Inter-Frame Attention f...

引用

ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Zhang, Guozhen Zhu, Yuhan Wang, Haonan Chen, Youxin Wu, Gangshan Wang, Limin Nanjing Univ State Key Lab Novel Software Technol Nanjing Peoples R China Shanghai AI Lab Shanghai Peoples R China Samsung Elect China R&D Ctr Beijing Peoples R China

ISBN: (纸本)9798350301298

Effectively extracting inter-frame motion and appearance information is important for video frame interpolation (VFI). Previous works either extract both types of information in a mixed way or devise separate modules for each type of information, which lead to representation ambiguity and low efficiency. In this paper, we propose a new module to explicitly extract motion and appearance information via a unified operation. Specifically, we rethink the information process in inter-frame attention and reuse its attention map for both appearance feature enhancement and motion information extraction. Furthermore, for efficient VFI, our proposed module could be seamlessly integrated into a hybrid CNN and Transformer architecture. This hybrid pipeline can alleviate the computational complexity of inter-frame attention as well as preserve detailed low-level structure information. Experimental results demonstrate that, for both fixed- and arbitrary-timestep interpolation, our method achieves state-of-the-art performance on various datasets. Meanwhile, our approach enjoys a lighter computation overhead over models with close performance. The source code and models are available at https://***/MCG-NJU/EMA-VFI.

关键词： Low-level vision

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Learning Imbalanced Data with vision Transformers

Learning Imbalanced Data with Vision Transformers

引用

ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Xu, Zhengzhuo Liu, Ruikang Yang, Shuo Chai, Zenghao Yuan, Chun Tsinghua Univ Shenzhen Int Grad Sch Beijing Peoples R China

ISBN: (纸本)9798350301298

The real-world data tends to be heavily imbalanced and severely skew the data-driven deep neural networks, which makes Long-Tailed recognition (LTR) a massive challenging task. Existing LTR methods seldom train vision Transformers (ViTs) with Long-Tailed (LT) data, while the off-the-shelf pretrain weight of ViTs always leads to unfair comparisons. In this paper, we systematically investigate the ViTs' performance in LTR and propose LiVT to train ViTs from scratch only with LT data. With the observation that ViTs suffer more severe LTR problems, we conduct Masked Generative Pretraining (MGP) to learn generalized features. With ample and solid evidence, we show that MGP is more robust than supervised manners. Although Binary Cross Entropy (BCE) loss performs well with ViTs, it struggles on the LTR tasks. We further propose the balanced BCE to ameliorate it with strong theoretical groundings. Specially, we derive the unbiased extension of Sigmoid and compensate extra logit margins for deploying it. Our Bal-BCE contributes to the quick convergence of ViTs in just a few epochs. Extensive experiments demonstrate that with MGP and Bal-BCE, LiVT successfully trains ViTs well without any additional data and outperforms comparable state-of-the-art methods significantly, e.g., our ViT-B achieves 81.0% Top-1 accuracy in iNaturalist 2018 without bells and whistles. Code is available at https://***/XuZhengzhuo/LiVT.

关键词： continual low-shot meta or long-tail learning Transfer

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Meta Distribution Alignment for Generalizable Person Re-Identification

Meta Distribution Alignment for Generalizable Person Re-Iden...

引用

ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Ni, Hao Song, Jingkuan Luo, Xiaopeng Zheng, Feng Li, Wen Shen, Heng Tao Univ Elect Sci & Technol China Sch Comp Sci & Engn Chengdu Peoples R China Southern Univ Sci & Technol Shenzhen Peoples R China

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Domain Generalizable (DG) person ReID is a challenging task which trains a model on source domains yet generalizes well on target domains. Existing methods use source domains to learn domain-invariant features, and assume those features are also irrelevant with target domains. However, they do not consider the target domain information which is unavailable in the training phrase of DG. To address this issue, we propose a novel Meta Distribution Alignment (MDA) method to enable them to share similar distribution in a test-time-training fashion. Specifically, since high-dimensional features are difficult to constrain with a known simple distribution, we first introduce an intermediate latent space constrained to a known prior distribution. The source domain data is mapped to this latent space and then reconstructed back. A meta-learning strategy is introduced to facilitate generalization and support fast adaption. To reduce their discrepancy, we further propose a test-time adaptive updating strategy based on the latent space which efficiently adapts model to unseen domains with a few samples. Extensive experimental results show that our model outperforms the state-of-the-art methods by up to 5.2% R-1 on average on the large-scale and 4.7% R-1 on the single-source domain generalization ReID benchmark. Source code is publicly available at https://***/haoni0812/***.

关键词： Training Adaptation models computer vision Codes Machine vision Computational modeling Benchmark testing

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Graph Convolutional Tracking 32

Graph Convolutional Tracking

引用

32nd ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Gao, Junyu Zhang, Tianzhu Xu, Changsheng Chinese Acad Sci CASIA Inst Automat Natl Lab Pattern Recognit NLPR Beijing Peoples R China Univ Chinese Acad Sci UCAS Beijing Peoples R China Peng Cheng Lab Shenzhen Peoples R China Univ Sci & Technol China Hefei Peoples R China

ISBN: (纸本)9781728132938

Tracking by siamese networks has achieved favorable performance in recent years. However, most of existing siamese methods do not take full advantage of spatial-temporal target appearance modeling under different contextual situations. In fact, the spatial-temporal information can provide diverse features to enhance the target representation, and the context information is important for online adaption of target localization. To comprehensively leverage the spatial-temporal structure of historical target exemplars and get benefit from the context information, in this work, we present a novel Graph Convolutional Tracking (GCT) method for high-performance visual tracking. Specifically, the GCT jointly incorporates two types of Graph Convolutional Networks (GCNs) into a siamese framework for target appearance modeling. Here, we adopt a spatial-temporal GCN to model the structured representation of historical target exemplars. Furthermore, a context GCN is designed to utilize the context of the current frame to learn adaptive features for target localization. Extensive results on 4 challenging benchmarks show that our GCT method performs favorably against state-of-the-art trackers while running around 50 frames per second.

关键词： Motion and Tracking

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UBnormal: New Benchmark for Supervised Open-Set Video Anomaly Detection

UBnormal: New Benchmark for Supervised Open-Set Video Anomal...

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ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Acsintoae, Andra Florescu, Andrei Georgescu, Mariana-Iuliana Mare, Tudor Sumedrea, Paul Ionescu, Radu Tudor Khan, Fahad Shahbaz Shah, Mubarak Univ Bucharest Bucharest Romania MBZ Univ Artificial Intelligence Abu Dhabi U Arab Emirates SecurifAI Bucharest Romania Linkoping Univ Linkoping Sweden Univ Cent Florida Orlando FL 32816 USA

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Detecting abnormal events in video is commonly framed as a one-class classification task, where training videos contain only normal events, while test videos encompass both normal and abnormal events. In this scenario, anomaly detection is an open-set problem. However, some studies assimilate anomaly detection to action recognition. This is a closed-set scenario that fails to test the capability of systems at detecting new anomaly types. To this end, we propose UBnormal, a new supervised open-set benchmark composed of multiple virtual scenes for video anomaly detection. Unlike existing data sets, we introduce abnormal events annotated at the pixel level at training time, for the first time enabling the use of fully-supervised learning methods for abnormal event detection. To preserve the typical open-set formulation, we make sure to include dis-joint sets of anomaly types in our training and test collections of videos. To our knowledge, UBnormal is the first video anomaly detection benchmark to allow a fair head-to-head comparison between one-class open-set models and supervised closed-set models, as shown in our experiments. Moreover, we provide empirical evidence showing that UB-normal can enhance the performance of a state-of-the-art anomaly detection framework on two prominent data sets, Avenue and ShanghaiTech. Our benchmark is freely available at https://***/lilygeorgescu/UBnormal.

关键词： Training Learning systems computer vision Event detection Benchmark testing Performance gain Data models

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Equivariant Multi-Modality Image Fusion

Equivariant Multi-Modality Image Fusion

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ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Zhao, Zixiang Hai, Haowen Zhang, Jiangshe Zhang, Yulun Zhane, Kai Xu, Shuang Chen, Dongdong Timofte, Radu Van Gool, Luc Xi An Jiao Tong Univ Xian Peoples R China Swiss Fed Inst Technol Zurich Switzerland Shanghai Jiao Tong Univ Shanghai Peoples R China Nanjing Univ Nanjing Peoples R China Northwestern Polytech Univ Xian Peoples R China Heriot Watt Univ Edinburgh Midlothian Scotland Univ Wurzburg Wurzburg Germany INSAIT Sofia Bulgaria

ISBN: (纸本)9798350353006

Multi-modality image fusion is a technique that combines information from different sensors or modalities, enabling the fused image to retain complementary features from each modality, such as functional highlights and texture details. However, effective training of such fusion models is challenging due to the scarcity of ground truth fusion data. To tackle this issue, we propose the Equivariant Multi-Modality imAge fusion (EMMA) paradigm for end-to-end self-supervised learning. Our approach is rooted in the prior knowledge that natural imaging responses are equivariant to certain transformations. Consequently, we introduce a novel training paradigm that encompasses a fusion module, a pseudo-sensing module, and an equivariant fusion module. These components enable the net training to follow the principles of the natural sensing-imaging process while satisfying the equivariant imaging prior. Extensive experiments confirm that EMMA yields high-quality fusion results for infraredvisible and medical images, concurrently facilitating downstream multi-modal segmentation and detection tasks. The code is available at https://***/Zhaozixiang1228/MMIF-EMMA.

关键词： image fusion low-level vision

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HeadNeRF: A Real-time NeRF-based Parametric Head Model

HeadNeRF: A Real-time NeRF-based Parametric Head Model

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ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Hong, Yang Peng, Bo Xiao, Haiyao Liu, Ligang Zhang, Juyong Univ Sci & Technol China Hefei Peoples R China

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

In this paper, we propose HeadNeRE a novel NeRF-based parametric head model that integrates the neural radiance field to the parametric representation of the human head. It can render high fidelity head images in real-time on modern GPUs, and supports directly controlling the generated images' rendering pose and various semantic attributes. Different from existing related parametric models, we use the neural radiance fields as a novel 3D proxy instead of the traditional 3D textured mesh, which makes that HeadNeRF is able to generate high fidelity images. How-even the computationally expensive rendering process of the original NeRF hinders the construction of the parametric NeRF model. To address this issue, we adopt the strategy of integrating 2D neural rendering to the rendering process of NeRF and design novel loss terms. As a result, the rendering speed of HeadNeRF can be significantly accelerated, and the rendering time of one frame is reduced from 5s to 25ms. The well designed loss terms also improve the rendering accuracy, and the fine-level details of the human head, such as the gaps between teeth, wrinkles, and beards, can be represented and synthesized by HeadNeRE Extensive experimental results and several applications demonstrate its effectiveness. The trained parametric model is available at https://***/CrisHY1995/headnerf.

关键词： Ethics computer vision Head Three-dimensional displays Semantics Rendering (computer graphics) Real-time systems

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Dimension Embeddings for Monocular 3D Object Detection

Dimension Embeddings for Monocular 3D Object Detection

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ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Zhang, Yunpeng Zheng, Wenzhao Zhu, Zheng Huang, Guan Du, Dalong Zhou, Jie Lu, Jiwen Beijing Natl Res Ctr Informat Sci & Technol Beijing Peoples R China Tsinghua Univ Dept Automat Beijing Peoples R China PhiGent Robot Beijing Peoples R China

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Most existing deep learning-based approaches for monocular 3D object detection directly regress the dimensions of objects and overlook their importance in solving the ill-posed problem. In this paper, we propose a general method to learn appropriate embeddings for dimension estimation in monocular 3D object detection. Specifically, we consider two intuitive clues in learning the dimension-aware embeddings with deep neural networks. First, we constrain the pair-wise distance on the embedding space to reflect the similarity of corresponding dimensions so that the model can take advantage of inter-object information to learn more discriminative embeddings for dimension estimation. Second, we propose to learn representative shape templates on the dimension-aware embedding space. Through the attention mechanism, each object can interact with the learnable templates and obtain the attentive dimensions as the initial estimation, which is further refined by the combined features from both the object and the attentive templates. Experimental results on the well-established KITII dataset demonstrate the proposed method of dimension embeddings can bring consistent improvements with negligible computation cost overhead. We achieve new state-of-the-art performance on the KITTI 3D object detection benchmark.

关键词： Deep learning computer vision Three-dimensional displays Image recognition Costs Shape Neural networks

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Rethinking Optical Flow from Geometric Matching Consistent Perspective

Rethinking Optical Flow from Geometric Matching Consistent P...

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ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Dong, Qiaole Cao, Chenjie Fu, Yanwei Fudan Univ Sch Data Sci Shanghai Peoples R China Shanghai Key Lab Intelligent Informat Proc Shanghai Peoples R China Zhejiang Normal Univ Fudan ISTBI ZJNU Algorithm Ctr Brain Inspired Int Jinhua Peoples R China

ISBN: (纸本)9798350301298

Optical flow estimation is a challenging problem remaining unsolved. Recent deep learning based optical flow models have achieved considerable success. However, these models often train networks from the scratch on standard optical flow data, which restricts their ability to robustly and geometrically match image features. In this paper, we propose a rethinking to previous optical flow estimation. We particularly leverage Geometric Image Matching (GIM) as a pre-training task for the optical flow estimation (MatchFlow) with better feature representations, as GIM shares some common challenges as optical flow estimation, and with massive labeled real-world data. Thus, matching static scenes helps to learn more fundamental feature correlations of objects and scenes with consistent displacements. Specifically, the proposed MatchFlow model employs a QuadTree attention-based network pre-trained on MegaDepth to extract coarse features for further flow regression. Extensive experiments show that our model has great cross-dataset generalization. Our method achieves 11.5% and 10.1% error reduction from GMA on Sintel clean pass and KITTI test set. At the time of anonymous submission, our MatchFlow(G) enjoys state-of-the-art performance on Sintel clean and final pass compared to published approaches with comparable computation and memory footprint. Codes and models will be released in https://***/DQiaole/MatchFlow.

关键词： Low-level vision

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Interpreting Super-Resolution Networks with Local Attribution Maps

Interpreting Super-Resolution Networks with Local Attributio...

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ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Gu, Jinjin Dong, Chao Univ Sydney Sch Elect & Informat Engn Sydney NSW Australia Chinese Acad Sci Shenzhen Inst Adv Technol Key Lab Human Machine Intelligence Synergy Syst Shenzhen Peoples R China Shenzhen Inst Artificial Intelligence & Robot Soc SIAT Branch Shenzhen Peoples R China

ISBN: (纸本)9781665445092

Image super-resolution (SR) techniques have been developing rapidly, benefiting from the invention of deep networks and its successive breakthroughs. However, it is acknowledged that deep learning and deep neural networks are difficult to interpret. SR networks inherit this mysterious nature and little works make attempt to understand them. In this paper, we perform attribution analysis of SR networks, which aims at finding the input pixels that strongly influence the SR results. We propose a novel attribution approach called local attribution map (LAM), which inherits the integral gradient method yet with two unique features. One is to use the blurred image as the baseline input, and the other is to adopt the progressive blurring function as the path function. Based on LAM, we show that: (1) SR networks with a wider range of involved input pixels could achieve better performance. (2) Attention networks and non-local networks extract features from a wider range of input pixels. (3) Comparing with the range that actually contributes, the receptive field is large enough for most deep networks. (4) For SR networks, textures with regular stripes or grids are more likely to be noticed, while complex semantics are difficult to utilize. Our work opens new directions for designing SR networks and interpreting low-level vision deep models.

关键词： Deep learning Visualization Technological innovation Gradient methods computer vision Superresolution Semantics

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