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Look More Than Once: An Accurate Detector for Text of Arbitrary Shapes 32

Look More Than Once: An Accurate Detector for Text of Arbitr...

引用

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

作者： Zhang, Chengquan Liang, Borong Huang, Zuming En, Mengyi Han, Junyu Ding, Errui Ding, Xinghao Baidu Inc Dept Comp Vis Technol Vis Beijing Peoples R China Xiamen Univ Fujian Key Lab Sensing & Comp Smart City Xiamen Peoples R China

ISBN: (纸本)9781728132938

Previous scene text detection methods have progressed substantially over the past years. However, limited by the receptive field of CNNs and the simple representations like rectangle bounding box or quadrangle adopted to describe text, previous methods may fall short when dealing with more challenging text instances, such as extremely long text and arbitrarily shaped text. To address these two problems, we present a novel text detector namely LOMO, which localizes the text progressively for multiple times (or in other word, LOok More than Once). LOMO consists of a direct regressor (DR), an iterative refinement module (IRM) and a shape expression module (SEM). At first, text proposals in the form of quadrangle are generated by DR branch. Next, IRM progressively perceives the entire long text by iterative refinement based on the extracted feature blocks of preliminary proposals. Finally, a SEM is introduced to reconstruct more precise representation of irregular text by considering the geometry properties of text instance, including text region, text center line and border offsets. The state-of-the-art results on several public benchmarks including ICDAR2017-RCTW, SCUT-CTW1500, Total-Text, ICDAR2015 and ICDAR17-MLT confirm the striking robustness and effectiveness of LOMO.

关键词： Categorization Document Analysis Grouping and Shape recognition: Detection Retrieval Segmentation vision Applicat

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Anchor-based Robust Finetuning of vision-Language Models

Anchor-based Robust Finetuning of Vision-Language Models

引用

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

作者： Han, Jinwei Lin, Zhiwen Sun, Zhongyisun Gao, Yingguo Yan, Ke Ding, Shouhong Gao, Yuan Xia, Gui-Song Wuhan Univ Sch Comp Sci Wuhan Peoples R China Tencent YouTu Lab Shenzhen Peoples R China Wuhan Univ Elect Informat Sch Wuhan Peoples R China

ISBN: (纸本)9798350353006

We aim at finetuning a vision-language model without hurting its out-of-distribution (OOD) generalization. We address two types of OOD generalization, i.e., i) domain shift such as natural to sketch images, and ii) zero-shot capability to recognize the category that was not contained in the finetune data. Arguably, the diminished OOD generalization after finetuning stems from the excessively simplified finetuning target, which only provides the class information, such as "a photo of a [CLASS]". This is distinct from the process in that CLIP was pretrained, where there is abundant text supervision with rich semantic information. Therefore, we propose to compensate for the finetune process using auxiliary supervision with rich semantic information, which acts as anchors to preserve the OOD generalization. Specifically, two types of anchors are elaborated in our method, including i) text-compensated anchor which uses the images from the finetune set but enriches the text supervision from a pretrained captioner, ii) image-text-pair anchor which is retrieved from the dataset similar to pre-training data of CLIP according to the downstream task, associating with the original CLIP text with rich semantics. Those anchors are utilized as auxiliary semantic information to maintain the original feature space of CLIP, thereby preserving the OOD generalization capabilities. Comprehensive experiments demonstrate that our method achieves in-distribution performance akin to conventional finetuning while attaining new state-of-the-art results on domain shift and zero-shot learning benchmarks.

关键词： Anchor Robust Finetuning

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RegionPLC: Regional Point-Language Contrastive Learning for Open-World 3D Scene Understanding

RegionPLC: Regional Point-Language Contrastive Learning for ...

引用

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

作者： Yang, Jihan Ding, Runyu Deng, Weipeng Wang, Zhe Qi, Xiaojuan Univ Hong Kong Hong Kong Peoples R China SenseTime Res Hong Kong Peoples R China

ISBN: (纸本)9798350353006

We propose a lightweight and scalable Regional Point-Language Contrastive learning framework, namely RegionPLC, for open-world 3D scene understanding, aiming to identify and recognize open-set objects and categories. Specifically, based on our empirical studies, we introduce a 3D-aware SFusion strategy that fuses 3D vision-language pairs derived from multiple 2D foundation models, yielding high-quality, dense region-level language descriptions without human 3D annotations. Subsequently, we devise a region-aware point-discriminative contrastive learning objective to enable robust and effective 3D learning from dense regional language supervision. We carry out extensive experiments on ScanNet, ScanNet200, and nuScenes datasets, and our model outperforms prior 3D open-world scene understanding approaches by an average of 17.2% and 9.1% for semantic and instance segmentation, respectively, while maintaining greater scalability and lower resource demands. Furthermore, our method has the flexibility to be effortlessly integrated with language models to enable open-ended grounded 3D reasoning without extra task-specific training. Code will be released at github.

关键词： 3D vision embodied AI Open-world

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Scalable Convolutional Neural Network for Image Compressed Sensing 32

Scalable Convolutional Neural Network for Image Compressed S...

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

作者： Shi, Wuzhen Jiang, Feng Liu, Shaohui Zhao, Debin Harbin Inst Technol Sch Comp Sci & Technol Harbin Peoples R China Peng Cheng Lab Shenzhen Peoples R China

ISBN: (纸本)9781728132938

Recently, deep learning based image Compressed Sensing (CS) methods have been proposed and demonstrated superior reconstruction quality with low computational complexity. However, the existing deep learning based image CS methods need to train different models for different sampling ratios, which increases the complexity of the encoder and decoder. In this paper, we propose a scalable convolutional neural network (dubbed SCSNet) to achieve scalable sampling and scalable reconstruction with only one model. Specifically, SCSNet provides both coarse and fine granular scalability. For coarse granular scalability, SCSNet is designed as a single sampling matrix plus a hierarchical reconstruction network that contains a base layer plus multiple enhancement layers. The base layer provides the basic reconstruction quality, while the enhancement layers reference the lower reconstruction layers and gradually improve the reconstruction quality. For fine granular scalability, SCSNet achieves sampling and reconstruction at any sampling ratio by using a greedy method to select the measurement bases. Compared with the existing deep learning based image CS methods, SCSNet achieves scalable sampling and quality scalable reconstruction at any sampling ratio with only one model. Experimental results demonstrate that SCSNet has the state-of-the-art performance while maintaining a comparable running speed with the existing deep learning based image CS methods.

关键词： Big Data Deep Learning Large Scale Methods Low-level vision Others Representation Learning vision Applications

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D2-Net: A Trainable CNN for Joint Description and Detection of Local Features 32

D2-Net: A Trainable CNN for Joint Description and Detection ...

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

作者： Dusmanu, Mihai Rocco, Ignacio Pajdla, Tomas Pollefeys, Marc Sivic, Josef Torii, Akihiko Sattler, Torsten PSL Res Univ CNRS ENS DI F-75005 Paris France Inria Rocquencourt France Swiss Fed Inst Technol Dept Comp Sci Zurich Switzerland CTU CIIRC Prague Czech Republic Microsoft Redmond WA USA Tokyo Inst Technol Tokyo Japan Chalmers Univ Technol Gothenburg Sweden

ISBN: (纸本)9781728132938

In this work we address the problem of finding reliable pixel-level correspondences under difficult imaging conditions. We propose an approach where a single convolutional neural network plays a dual role: It is simultaneously a dense feature descriptor and a feature detector By postponing the detection to a later stage, the obtained keypoints are more stable than their traditionalcounterparts based on early detection of low-level structures. We show that this model can be trainedusing pixel correspondences extractedfrom readilyavailable large-scaleSfM reconstructions, without any further annotations. The proposed method obtains state-of-the-artperformance on both the difficult Aachen Day-Night localizationdataset and the InLoc indoor localization benchmark, as well as competitive performance on other benchmarksfor image matching and 3D reconstruction.

关键词： 3D from Multiview and Sensors Categorization Deep Learning Low-level vision recognition: Detection Retrieval

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Dynamic Prototype Convolution Network for Few-Shot Semantic Segmentation

Dynamic Prototype Convolution Network for Few-Shot Semantic ...

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

作者： Liu, Jie Bao, Yanqi Xie, Guo-Sen Xiong, Huan Sonke, Jan-Jakob Gavves, Efstratios Univ Amsterdam Amsterdam Netherlands Northeastern Univ Shenyang Peoples R China Netherlands Canc Inst Amsterdam Netherlands Nanjing Univ Sci & Technol Nanjing Peoples R China Mohamed Bin Zayed Univ Artificial Intelligence Abu Dhabi U Arab Emirates

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

The key challenge for few-shot semantic segmentation (FSS) is how to tailor a desirable interaction among support and query features and/or their prototypes, under the episodic training scenario. Most existing FSS methods implement such support/query interactions by solely leveraging plain operations - e.g., cosine similarity and feature concatenation - for segmenting the query objects. However, these interaction approaches usually cannot well capture the intrinsic object details in the query images that are widely encountered in FSS, e.g., if the query object to be segmented has holes and slots, inaccurate segmentation almost always happens. To this end, we propose a dynamic prototype convolution network (DPCN) to fully capture the aforementioned intrinsic details for accurate FSS. Specifically, in DPCN, a dynamic convolution module (DCM) is firstly proposed to generate dynamic kernels from support foreground, then information interaction is achieved by convolution operations over query features using these kernels. Moreover, we equip DPCN with a support activation module (SAM) and a feature filtering module (FFM) to generate pseudo mask and filter out background information for the query images, respectively. SAM and FFM together can mine enriched context information from the query features. Our DPCN is also flexible and efficient under the k-shot FSS setting. Extensive experiments on PASCAL-5(i) and COCO-20(i) show that DPCN yields superior performances under both 1-shot and 5-shot settings.

关键词： Training Image segmentation computer vision Convolution Semantics Prototypes Information filters

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HOI4D: A 4D Egocentric Dataset for Category-Level Human-Object Interaction

HOI4D: A 4D Egocentric Dataset for Category-Level Human-Obje...

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

作者： Liu, Yunze Liu, Yun Jiang, Che Lyu, Kangbo Wan, Weikang Shen, Hao Liang, Boqiang Fu, Zhoujie Wang, He Yi, Li Tsinghua Univ Beijing Peoples R China Peking Univ Beijing Peoples R China Shanghai Qi Zhi Inst Shanghai Peoples R China

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

We present HOI4D, a large-scale 4D egocentric dataset with rich annotations, to catalyze the research of category-level human-object interaction. HOI4D consists of 2.4M RGB-D egocentric video frames over 4000 sequences collected by 9 participants interacting with 800 different object instances from 16 categories over 610 different indoor rooms. Frame-wise annotations for panoptic segmentation, motion segmentation, 3D hand pose, category-level object pose and hand action have also been provided, together with reconstructed object meshes and scene point clouds. With HOI4D, we establish three benchmarking tasks to promote category-level HOI from 4D visual signals including semantic segmentation of 4D dynamic point cloud sequences, category-level object pose tracking, and egocentric action segmentation with diverse interaction targets. In-depth analysis shows HOI4D poses great challenges to existing methods and produces huge research opportunities.

关键词： Point cloud compression computer vision Visualization Target tracking Three-dimensional displays Annotations Shape

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ORDisCo: Effective and Efficient Usage of Incremental Unlabeled Data for Semi-supervised Continual Learning

ORDisCo: Effective and Efficient Usage of Incremental Unlabe...

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

作者： Wang, Liyuan Yang, Kuo Li, Chongxuan Hong, Lanqing Li, Zhenguo Zhu, Jun Tsinghua Univ BNRist Ctr Inst AI Dept Comp Sci & TechTHBI Lab Beijing Peoples R China Huawei Noahs Ark Lab Shenzhen Peoples R China

ISBN: (纸本)9781665445092

Continual learning usually assumes the incoming data are fully labeled, which might not be applicable in real applications. In this work, we consider semi-supervised continual learning (SSCL) that incrementally learns from partially labeled data. Observing that existing continual learning methods lack the ability to continually exploit the unlabeled data, we propose deep Online Replay with Discriminator Consistency (ORDisCo) to interdependently learn a classifier with a conditional generative adversarial network (GAN), which continually passes the learned data distribution to the classifier. In particular, ORDisCo replays data sampled from the conditional generator to the classifier in an online manner, exploiting unlabeled data in a time- and storage-efficient way. Further, to explicitly overcome the catastrophic forgetting of unlabeled data, we selectively stabilize parameters of the discriminator that are important for discriminating the pairs of old unlabeled data and their pseudo-labels predicted by the classifier. We extensively evaluate ORDisCo on various semi-supervised learning benchmark datasets for SSCL, and show that ORDisCo achieves significant performance improvement on SVHN, CIFAR10 and Tiny-ImageNet, compared to strong baselines.

关键词： Deep learning computer vision Systematics Semisupervised learning Benchmark testing Generative adversarial networks Generators

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Person30K: A Dual-Meta Generalization Network for Person Re-Identification

Person30K: A Dual-Meta Generalization Network for Person Re-...

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

作者： Bai, Yan Jiao, Jile Ce, Wang Liu, Jun Lou, Yihang Feng, Xuetao Duan, Ling-Yu Peking Univ Beijing Peoples R China Alibaba Grp Beijing Peoples R China Singapore Univ Technol & Design Singapore Singapore Peng Cheng Lab Shenzhen Peoples R China

ISBN: (纸本)9781665445092

Recently, person re-identification (ReID) has vastly benefited from the surging waves of data-driven methods. However, these methods are still not reliable enough for real-world deployments, due to the insufficient generalization capability of the models learned on existing benchmarks that have limitations in multiple aspects, including limited data scale, capture condition variations, and appearance diversities. To this end, we collect a new dataset named Person30K with the following distinct features: 1) a very large scale containing 1.38 million images of 30K identities, 2) a large capture system containing 6,497 cameras deployed at 89 different sites, 3) abundant sample diversities including varied backgrounds and diverse person poses. Furthermore, we propose a domain generalization ReID method, dual-meta generalization network (DMG-Net), to exploit the merits of meta-learning in both the training procedure and the metric space learning. Concretely, we design a "learning then generalization evaluation" meta-training procedure and a meta-discrimination loss to enhance model generalization and discrimination capabilities. Comprehensive experiments validate the effectiveness of our DMG-Net.

关键词： Training computer vision Computational modeling Benchmark testing Extraterrestrial measurements Cameras Data models

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Deep Safe Multi-view Clustering: Reducing the Risk of Clustering Performance Degradation Caused by View Increase

Deep Safe Multi-view Clustering: Reducing the Risk of Cluste...

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

作者： Tang, Huayi Liu, Yong Renmin Univ China Gaoling Sch Artificial Intelligence Beijing Peoples R China Beijing Key Lab Big Data Management & Anal Method Beijing Peoples R China

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Multi-view clustering has been shown to boost clustering performance by effectively mining the complementary information from multiple views. However, we observe that learning from data with more views is not guaranteed to achieve better clustering performance than from data with fewer views. To address this issue, we propose a general deep learning based framework that is guaranteed to reduce the risk of performance degradation caused by view increase. Concretely, the model is trained to simultaneously extract complementary information and discard the meaningless noise by automatically selecting features. These two learning procedures are incorporated into one unified framework by the proposed optimization objective. In theory, the empirical clustering risk of the model is no higher than learning from data before the view increase and data of the new increased single view. Also, the expected clustering risk of the model under divergence-based loss is no higher than that with high probability. Comprehensive experiments on benchmark datasets demonstrate the effectiveness and superiority of the proposed framework in achieving safe multi-view clustering.

关键词： Degradation Deep learning computer vision Computational modeling Feature extraction Robustness Data models

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