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CRAFT Objects from Images 29

CRAFT Objects from Images

引用

2016 ieee conference on computer vision and pattern recognition (cvpr)

作者： Yang, Bin Yang, Junjie Lei, Zhen Li, Stan Z. Chinese Acad Sci Inst Automat Natl Lab Pattern Recognit Beijing Peoples R China Tsinghua Univ Beijing Peoples R China

ISBN: (纸本)9781467388511

Object detection is a fundamental problem in image understanding. One popular solution is the R-CNN framework [15] and its fast versions [14, 27]. They decompose the object detection problem into two cascaded easier tasks: 1) generating object proposals from images, 2) classifying proposals into various object categories. Despite that we are handling with two relatively easier tasks, they are not solved perfectly and there's still room for improvement. In this paper, we push the "divide and conquer" solution even further by dividing each task into two sub-tasks. We call the proposed method "CRAFT" (Cascade Regionproposal-network And FasT-rcnn), which tackles each task with a carefully designed network cascade. We show that the cascade structure helps in both tasks: in proposal generation, it provides more compact and better localized object proposals;in object classification, it reduces false positives (mainly between ambiguous categories) by capturing both inter- and intra-category variances. CRAFT achieves consistent and considerable improvement over the state-of-the-art on object detection benchmarks like PASCAL VOC 07/12 and ILSVRC.

关键词： Object detection

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TCTrack: Temporal Contexts for Aerial Tracking

TCTrack: Temporal Contexts for Aerial Tracking

引用

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

作者： Cao, Ziang Huang, Ziyuan Pan, Liang Zhang, Shiwei Liu, Ziwei Fu, Changhong Tongji Univ Shanghai Peoples R China Natl Univ Singapore Singapore Singapore Nanyang Technol Univ S Lab Singapore Singapore Alibaba Grp DAMO Acad Hangzhou Peoples R China

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Temporal contexts among consecutive frames are far from being fully utilized in existing visual trackers. In this work, we present TCTrack(1), a comprehensive framework to fully exploit temporal contexts for aerial tracking. The temporal contexts are incorporated at two levels: the extraction of features and the refinement of similarity maps. Specifically, for feature extraction, an online temporally adaptive convolution is proposed to enhance the spatial features using temporal information, which is achieved by dynamically calibrating the convolution weights according to the previous frames. For similarity map refinement, we propose an adaptive temporal transformer, which first effectively encodes temporal knowledge in a memory-efficient way, before the temporal knowledge is decoded for accurate adjustment of the similarity map. TCTrack is effective and efficient: evaluation on four aerial tracking benchmarks shows its impressive performance;real-world UAV tests show its high speed of over 27 FPS on NVIDIA Jetson AGX Xavier.

关键词： Visualization computer vision Convolution Benchmark testing Feature extraction Transformers Autonomous aerial vehicles

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Failure Modes of Domain Generalization Algorithms

Failure Modes of Domain Generalization Algorithms

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

作者： Galstyan, Tigran Harutyunyan, Hrayr Khachatrian, Hrant Ver Steeg, Greg Galstyan, Aram YerevaNN Yerevan Armenia USC Informat Sci Inst Marina Del Rey CA USA Russian Armenian Univ Yerevan Armenia Yerevan State Univ Yerevan Armenia

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Domain generalization algorithms use training data from multiple domains to learn models that generalize well to unseen domains. While recently proposed benchmarks demonstrate that most of the existing algorithms do not outperform simple baselines, the established evaluation methods fail to expose the impact of various factors that contribute to the poor performance. In this paper we propose an evaluation framework for domain generalization algorithms that allows decomposition of the error into components capturing distinct aspects of generalization. Inspired by the prevalence of algorithms based on the idea of domain-invariant representation learning, we extend the evaluation framework to capture various types of failures in achieving invariance. We show that the largest contributor to the generalization error varies across methods, datasets, regularization strengths and even training lengths. We observe two problems associated with the strategy of learning domain-invariant representations. On Colored MNIST, most domain generalization algorithms fail because they reach domain-invariance only on the training domains. On Camelyon-17, domain-invariance degrades the quality of representations on unseen domains. We hypothesize that focusing instead on tuning the classifier on top of a rich representation can be a promising direction.

关键词： Training Representation learning computer vision Machine learning algorithms Training data Focusing Data models

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Unbiased Teacher v2: Semi-supervised Object Detection for Anchor-free and Anchor-based Detectors

Unbiased Teacher v2: Semi-supervised Object Detection for An...

引用

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

作者： Liu, Yen-Cheng Ma, Chih-Yao Kira, Zsolt Georgia Inst Technol Atlanta GA 30332 USA Meta Menlo Pk CA USA

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

With the recent development of Semi-Supervised Object Detection (SS-OD) techniques, object detectors can be improved by using a limited amount of labeled data and abundant unlabeled data. However, there are still two challenges that are not addressed: (1) there is no prior SS-OD work on anchor-free detectors, and (2) prior works are ineffective when pseudo-labeling bounding box regression. In this paper, we present Unbiased Teacher v2, which shows the generalization of SS-OD method to anchor-free detectors and also introduces Listen2Student mechanism for the unsupervised regression loss. Specifically, we first present a study examining the effectiveness of existing SS-OD methods on anchor-free detectors and find that they achieve much lower performance improvements under the semi-supervised setting. We also observe that box selection with centerness and the localization-based labeling used in anchor-free detectors cannot work well under the semi-supervised setting. On the other hand, our Listen2Student mechanism explicitly prevents misleading pseudo-labels in the training of bounding box regression;we specifically develop a novel pseudo-labeling selection mechanism based on the Teacher and Student's relative uncertainties. This idea contributes to favorable improvement in the regression branch in the semi-supervised setting. Our method, which works for both anchor-free and anchor-based methods, consistently performs favorably against the state-of-the-art methods in VOC, COCO-standard, and COCO-additional.

关键词： Training computer vision Uncertainty Detectors Object detection Benchmark testing pattern recognition

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DynTypo: Example-based Dynamic Text Effects Transfer 32

DynTypo: Example-based Dynamic Text Effects Transfer

引用

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

作者： Men, Yifang Lian, Zhouhui Tang, Yingmin Xiao, Jianguo Peking Univ Inst Comp Sci & Technol Beijing Peoples R China

ISBN: (纸本)9781728132938

In this paper, we present a novel approach for dynamic text effects transfer by using example-based texture synthesis. In contrast to previous works that require an input video of the target to provide motion guidance, we aim to animate a still image of the target text by transferring the desired dynamic effects from an observed exemplar. Due to the simplicity of target guidance and complexity of realistic effects, it is prone to producing temporal artifacts such as flickers and pulsations. To address the problem, our core idea is to find a common Nearest-neighbor Field (NNF) that would optimize the textural coherence across all keyframes simultaneously. With the static NNF for video sequences, we implicitly transfer motion properties from source to target. We also introduce a guided NNF search by employing the distance-based weight map and Simulated Annealing (SA) for deep direction-guided propagation to allow intense dynamic effects to be completely transferred with no semantic guidance provided. Experimental results demonstrate the effectiveness and superiority of our method in dynamic text effects transfer through extensive comparisons with state-of-the-art algorithms. We also show the potentiality of our method via multiple experiments for various application domains.

关键词： Image and Video Synthesis Low-level vision vision Applications and Systems

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On Generalizing Beyond Domains in Cross-Domain Continual Learning

On Generalizing Beyond Domains in Cross-Domain Continual Lea...

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

作者： Simon, Christian Faraki, Masoud Tsai, Yi-Hsuan Yu, Xiang Schulter, Samuel Suh, Yumin Harandi, Mehrtash Chandraker, Manmohan Australian Natl Univ Canberra ACT Australia NEC Labs Amer Princeton NJ USA Monash Univ Melbourne Vic Australia Univ Calif San Diego San Diego CA USA Data61 Sydney NSW Australia Phiar Technol Redwood City CA USA

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Humans have the ability to accumulate knowledge of new tasks in varying conditions, but deep neural networks often suffer from catastrophic forgetting of previously learned knowledge after learning a new task. Many recent methods focus on preventing catastrophic forgetting under the assumption of train and test data following similar distributions. In this work, we consider a more realistic scenario of continual learning under domain shifts where the model must generalize its inference to an unseen domain. To this end, we encourage learning semantically meaningful features by equipping the classifier with class similarity metrics as learning parameters which are obtained through Mahalanobis similarity computations. Learning of the backbone representation along with these extra parameters is done seamlessly in an end-to-end manner. In addition, we propose an approach based on the exponential moving average of the parameters for better knowledge distillation. We demonstrate that, to a great extent, existing continual learning algorithms fail to handle the forgetting issue under multiple distributions, while our proposed approach learns new tasks under domain shift with accuracy boosts up to 10% on challenging datasets such as DomainNet and OfficeHome.

关键词： Knowledge engineering Measurement Deep learning computer vision Computational modeling Neural networks pattern recognition

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Surface Reconstruction from Normals: A Robust DGP-based Discontinuity Preservation Approach 32

Surface Reconstruction from Normals: A Robust DGP-based Disc...

引用

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

作者： Xie, Wuyuan Wang, Miaohui Wei, Mingqiang Jiang, Jianmin Qin, Jing Shenzhen Univ SZU Coll Comp & Software Engn Shenzhen Peoples R China SZU Coll Informat Engn Guangdong Key Lab Intelligent Informat Proc Shenzhen Peoples R China Nanjing Univ Aeronaut & Astronaut Sch Comp Sci & Technol Nanjing Peoples R China Hong Kong Polytech Univ Sch Nursing Hong Kong Peoples R China

ISBN: (纸本)9781728132938

In 3D surface reconstruction from normals, discontinuity preservation is an important but challenging task. However, existing studies fail to address the discontinuous normal maps by enforcing the surface integrability in the continuous domain. This paper introduces a robust approach to preserve the surface discontinuity in the discrete geometry way. Firstly, we design two representative normal incompatibility features and propose an efficient discontinuity detection scheme to determine the splitting pattern for a discrete mesh. Secondly, we model the discontinuity preservation problem as a light-weight energy optimization framework by jointly considering the discontinuity detection and the overall reconstruction error. Lastly, we further shrink the feasible solution space to reduce the complexity based on the prior knowledge. Experiments show that the proposed method achieves the best performance on an extensive 3D dataset compared with the state-of-the-arts in terms of mean angular error and computational complexity.

关键词： 3D from Single Image Physics-based vision and Shape-from-X

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PA3D: Pose-Action 3D Machine for Video recognition 32

PA3D: Pose-Action 3D Machine for Video Recognition

引用

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

作者： Yan, An Wang, Yali Li, Zhifeng Qiao, Yu Chinese Acad Sci Shenzhen Inst Adv Technol Shenzhen Key Lab Comp Vis & Pattern Recognit SIAT SenseTime Joint Lab Shenzhen Peoples R China Tencent AI Lab Bellevue WA USA Univ Calif San Diego La Jolla CA 92093 USA Chinese Univ Hong Kong Hong Kong Peoples R China

ISBN: (纸本)9781728132938

Recent studies have witnessed the successes of using 3D CNNs for video action ***, most 3D models are built upon RGB and opticalflow streams, which may not fully exploit pose dynamics, i.e., an important cue of modeling human actions. To fill this gap, we propose a concise Pose-Action 3D Machine (PA3D), which can effectively encode multiple pose modalities within a unified 3D framework, and consequently learn spatio-temporalpose representationsfor action recognition. More specifically, we introduce a novel temporal pose convolution to aggregate spatial poses over frames. Unlike the classical temporal convolution, our operation can explicitly learn the pose motions that are discriminativeto recognize human actions. Extensive experiments on three popular benchmarks (i.e., JHMDB, HMDB, and Charades)show that, PA3D outperforms the recentpose-based approaches. Furthermore, PA3D is highly complementary to the recent 3D CNNs, e.g., 13D. Multi-stream fusion achieves the state-of-the-artperformance on all evaluateddata sets.

关键词： Action recognition

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GLID: Pre-training a Generalist Encoder-Decoder vision Model

GLID: Pre-training a Generalist Encoder-Decoder Vision Model

引用

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

作者： Liu, Jihao Zheng, Jinliang Liu, Yu Li, Hongsheng CUHK MMLab Hong Kong Peoples R China SenseTime Res Hong Kong Peoples R China Shanghai AI Lab Shanghai Peoples R China CPII InnoHK Hong Kong Peoples R China Tsinghua Univ Inst AI Ind Res AIR Shanghai Peoples R China

ISBN: (纸本)9798350353006

This paper proposes a GeneraLIst encoder-Decoder (GLID) pre-training method for better handling various downstream computer vision tasks. While self-supervised pre-training approaches, e.g., Masked Autoencoder, have shown success in transfer learning, task-specific sub-architectures are still required to be appended for different downstream tasks, which cannot enjoy the benefits of large-scale pre-training. GLID overcomes this challenge by allowing the pre-trained generalist encoder-decoder to be fine-tuned on various vision tasks with minimal task-specific architecture modifications. In the GLID training scheme, pre-training pretext task and other downstream tasks are modeled as "query-to-answer" problems, including the pre-training pretext task and other downstream tasks. We pre-train a task-agnostic encoder-decoder with query-mask pairs. During fine-tuning, GLID maintains the pre-trained encoder-decoder and queries, only replacing the topmost linear transformation layer with task-specific linear heads. This minimizes the pretrain-finetune architecture inconsistency and enables the pre-trained model to better adapt to downstream tasks. GLID achieves competitive performance on various vision tasks, including object detection, image segmentation, pose estimation, and depth estimation, outper-forming or matching specialist models such as Mask2Former, DETR, ViTPose, and BinsFormer.

关键词： Image segmentation

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Improving the Transferability of Targeted Adversarial Examples through Object-Based Diverse Input

Improving the Transferability of Targeted Adversarial Exampl...

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

作者： Byun, Junyoung Cho, Seungju Kwon, Myung-Joon Kim, Hee-Seon Kim, Changick Korea Adv Inst Sci & Technol KAIST Daejeon South Korea

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

The transferability of adversarial examples allows the deception on black-box models, and transfer-based targeted attacks have attracted a lot of interest due to their practical applicability. To maximize the transfer success rate, adversarial examples should avoid overfitting to the source model, and image augmentation is one of the primary approaches for this. However, prior works utilize simple image transformations such as resizing, which limits input diversity. To tackle this limitation, we propose the object-based diverse input (ODI) method that draws an adversarial image on a 3D object and induces the rendered image to be classified as the target class. Our motivation comes from the humans' superior perception of an image printed on a 3D object. If the image is clear enough, humans can recognize the image content in a variety of viewing conditions. Likewise, if an adversarial example looks like the target class to the model, the model should also classify the rendered image of the 3D object as the target class. The ODI method effectively diversifies the input by leveraging an ensemble of multiple source objects and randomizing viewing conditions. In our experimental results on the ImageNet-Compatible dataset, this method boosts the average targeted attack success rate from 28.3% to 47.0% compared to the state-of-the-art methods. We also demonstrate the applicability of the ODI method to adversarial examples on the face verification task and its superior performance improvement. Our code is available at https://***/dreamflake/ODI.

关键词： Solid modeling computer vision Three-dimensional displays Image recognition Codes Face recognition Computational modeling

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