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ECKPN: Explicit Class Knowledge Propagation Network for Transductive Few-shot Learning

ECKPN: Explicit Class Knowledge Propagation Network for Tran...

引用

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

作者： Chen, Chaofan Yang, Xiaoshan Xu, Changsheng Huang, Xuhui Ma, Zhe Univ Sci & Technol China USTC Sch Informat Sci & Technol Hefei Anhui Peoples R China Chinese Acad Sci CASIA Inst Automat Natl Lab Pattern Recognit NLPR Beijing Peoples R China Univ Chinese Acad Sci UCAS Sch Artificial Intelligence Beijing Peoples R China Second Acad CASIC X Lab Beijing Peoples R China

ISBN: (数字)9781665445092

ISBN: (纸本)9781665445092

Recently, the transductive graph-based methods have achieved great success in the few-shot classification task. However, most existing methods ignore exploring the class-level knowledge that can be easily learned by humans from just a handful of samples. In this paper, we propose an Explicit Class Knowledge Propagation Network (ECKPN), which is composed of the comparison, squeeze and calibration modules, to address this problem. Specifically, we first employ the comparison module to explore the pairwise sample relations to learn rich sample representations in the instance-level graph. Then, we squeeze the instance-level graph to generate the class-level graph, which can help obtain the class-level visual knowledge and facilitate modeling the relations of different classes. Next, the calibration module is adopted to characterize the relations of the classes explicitly to obtain the more discriminative class-level knowledge representations. Finally, we combine the class-level knowledge with the instance-level sample representations to guide the inference of the query samples. We conduct extensive experiments on four few-shot classification benchmarks, and the experimental results show that the proposed ECKPN significantly outperforms the state-of-the art methods.

关键词： Visualization computer vision Art Computational modeling Knowledge representation Benchmark testing Calibration

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Entity-NeRF: Detecting and Removing Moving Entities in Urban Scenes

Entity-NeRF: Detecting and Removing Moving Entities in Urban...

引用

conference on computer vision and pattern recognition (cvpr)

作者： Takashi Otonari Satoshi Ikehata Kiyoharu Aizawa The University of Tokyo National Institute of Informatics (NII) Tokyo Institute of Technology

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

Recent advancements in the study of Neural Radiance Fields (NeRF) for dynamic scenes often involve explicit modeling of scene dynamics. However, this approach faces challenges in modeling scene dynamics in urban environments, where moving objects of various categories and scales are present. In such settings, it becomes crucial to effectively eliminate moving objects to accurately reconstruct static backgrounds. Our research introduces an innovative method, termed here as Entity-NeRF, which combines the strengths of knowledge-based and statistical strategies. This approach utilizes entity-wise statistics, leveraging entity segmentation and stationary entity classification through thing/stuff segmentation. To assess our methodology, we created an urban scene dataset masked with moving objects. Our comprehensive experiments demonstrate that Entity-NeRF notably outperforms existing techniques in removing moving objects and reconstructing static urban backgrounds, both quantitatively and qualitatively. 1 1 Our project page is available at https://***/entitynerf/

关键词： Training computer vision Computational modeling Urban areas Knowledge based systems Neural radiance field pattern recognition

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Self-Supervised Visibility Learning for Novel View Synthesis

Self-Supervised Visibility Learning for Novel View Synthesis

引用

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

作者： Shi, Yujiao Li, Hongdong Yu, Xin Australian Natl Univ Canberra ACT Australia ACRV Canberra ACT Australia Univ Technol Sydney Sydney NSW Australia

ISBN: (纸本)9781665445092

We address the problem of novel view synthesis (NVS) from a few sparse source view images. Conventional image-based rendering methods estimate scene geometry and synthesize novel views in two separate steps. However, erroneous geometry estimation will decrease NVS performance as view synthesis highly depends on the quality of estimated scene geometry. In this paper, we propose an end-to-end NVS framework to eliminate the error propagation issue. To be specific, we construct a volume under the target view and design a source-view visibility estimation (SVE) module to determine the visibility of the target-view voxels in each source view. Next, we aggregate the visibility of all source views to achieve a consensus volume. Each voxel in the consensus volume indicates a surface existence probability. Then, we present a soft ray-casting (SRC) mechanism to find the most front surface in the target view (i.e., depth). Specifically, our SRC traverses the consensus volume along viewing rays and then estimates a depth probability distribution. We then warp and aggregate source view pixels to synthesize a novel view based on the estimated source-view visibility and target-view depth. At last, our network is trained in an end-to-end self-supervised fashion, thus significantly alleviating error accumulation in view synthesis. Experimental results demonstrate that our method generates novel views in higher quality compared to the state-of-the-art.

关键词： Geometry Training Technological innovation computer vision Image synthesis Aggregates Estimation

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Peekaboo: Interactive Video Generation via Masked-Diffusion

Peekaboo: Interactive Video Generation via Masked-Diffusion

引用

conference on computer vision and pattern recognition (cvpr)

作者： Yash Jain Anshul Nasery Vibhav Vineet Harkirat Behl Microsoft University of Washington Project Webpage

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

Modern video generation models like Sora have achieved remarkable success in producing high-quality videos. However, a significant limitation is their inability to offer interactive control to users, a feature that promises to open up unprecedented applications and creativity. In this work, we introduce the first solution to equip diffusion-based video generation models with spatio-temporal control. We present PEEKABOO, a novel masked attention module, which seamlessly integrates with current video generation models offering control without the need for additional training or inference overhead. To facilitate future research, we also introduce a comprehensive benchmark for interactive video generation. This benchmark offers a standardized framework for the community to assess the efficacy of emerging interactive video generation models. Our extensive qualitative and quantitative assessments reveal that PEEKABOO achieves up to a 3.8x improvement in mIoU over baseline models, all while maintaining the same latency. Code and benchmark are available on the webpage.

关键词： Training computer vision Codes Computational modeling Benchmark testing pattern recognition Creativity

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Fuse-PN: A Novel Architecture for Anomaly pattern Segmentation in Aerial Agricultural Images

Fuse-PN: A Novel Architecture for Anomaly Pattern Segmentati...

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

作者： Innani, Shubham Dutande, Prasad Baheti, Bhakti Talbar, Sanjay Baid, Ujjwal SGGS Inst Engn & Technol Ctr Excellence Signal & Image Proc Nanded 431606 India

ISBN: (纸本)9781665448994

Deep learning and pattern recognition in smart farming has seen rapid growth as a building bridge between crop science and computer vision. One of the important application is anomaly segmentation in agriculture like weed, standing water, cloud shadow, etc. Our research work focuses on aerial farmland image dataset known as Agriculture vision. We propose to have data fusion of R, G, B, and NIR modalities that enhances the feature extraction and also propose Efficient Fused Pyramid Network (Fuse-PN) for anomaly pattern segmentation. The proposed encoder module is a bottom-up pathway having a compound scaled network and decoder module is a top-down pyramid network enhancing features at different scales having rich semantic features with lateral connections of low-level features. This proposed approach achieved a mean dice similarity score of 0.8271 for six agricultural anomaly patterns of Agriculture vision dataset and outperforms various approaches in literature.

关键词： Deep learning Training Image segmentation computer vision Semantics Data integration computer architecture

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Learning to Filter: Siamese Relation Network for Robust Tracking

Learning to Filter: Siamese Relation Network for Robust Trac...

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

作者： Cheng, Siyuan Zhong, Bineng Li, Guorong Liu, Xin Tang, Zhenjun Li, Xianxian Wang, Jing Guangxi Normal Univ Guangxi Key Lab Multisource Informat Min & Secur Guilin 541004 Peoples R China Huaqiao Univ Dept Comp Sci & Technol Xiamen Peoples R China Univ Chinese Acad Sci Sch Comp Sci & Technol Beijing Peoples R China Seetatech Technol Beijing Peoples R China

ISBN: (纸本)9781665445092

Despite the great success of Siamese-based trackers, their performance under complicated scenarios is still not satisfying, especially when there are distractors. To this end, we propose a novel Siamese relation network, which introduces two efficient modules, i.e. Relation Detector (RD) and Refinement Module (RM). RD performs in a meta-learning way to obtain a learning ability to filter the distractors from the background while RM aims to effectively integrate the proposed RD into the Siamese framework to generate accurate tracking result. Moreover, to further improve the discriminability and robustness of the tracker, we introduce a contrastive training strategy that attempts not only to learn matching the same target but also to learn how to distinguish the different objects. Therefore, our tracker can achieve accurate tracking results when facing background clutters, fast motion, and occlusion. Experimental results on five popular benchmarks, including VOT2018, VOT2019, OTB100, LaSOT, and UAV123, show that the proposed method is effective and can achieve stateof-the-art results.

关键词： Training Matched filters computer vision Target tracking Detectors Benchmark testing Gain measurement

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UniT: Unified Knowledge Transfer for Any-shot Object Detection and Segmentation

UniT: Unified Knowledge Transfer for Any-shot Object Detecti...

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

作者： Khandelwal, Siddhesh Goyal, Raghav Sigal, Leonid Univ British Columbia Dept Comp Sci Vancouver BC Canada Vector Inst AI Toronto ON Canada CIFAR AI Chair Montreal PQ Canada

ISBN: (纸本)9781665445092

Methods for object detection and segmentation rely on large scale instance-level annotations for training, which are difficult and time-consuming to collect. Efforts to alleviate this look at varying degrees and quality of supervision. Weakly-supervised approaches draw on image-level labels to build detectors/segmentors, while zero/few-shot methods assume abundant instance-level data for a set of base classes, and none to a few examples for novel classes. This taxonomy has largely saved algorithmic designs. In this work, we aim to bridge this divide by proposing an intuitive and unified semi-supervised model that is applicable to a range of supervision: from zero to a few instance-level samples per novel class. For base classes, our model learns a mapping from weakly-supervised to fully-supervised detectors/segmentors. By learning and leveraging visual and lingual similarities between the novel and base classes, we transfer those mappings to obtain detectors/segmentors for novel classes;refining them with a few novel class instance-level annotated samples, if available. The overall model is end-to-end trainable and highly flexible(1). Through extensive experiments on MS-COCO [32] and Pascal VOC [14] benchmark datasets we show improved performance in a variety of settings.

关键词： Training Visualization Image segmentation computer vision Computational modeling Taxonomy Refining

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Bilateral Grid Learning for Stereo Matching Networks

Bilateral Grid Learning for Stereo Matching Networks

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

作者： Xu, Bin Xu, Yuhua Yang, Xiaoli Jia, Wei Guo, Yulan Orbbec Troy MI 48083 USA Hefei Univ Technol Hefei Peoples R China Sun Yat Sen Univ Guangzhou Peoples R China

ISBN: (纸本)9781665445092

Real-time performance of stereo matching networks is important for many applications, such as automatic driving, robot navigation and augmented reality (AR). Although significant progress has been made in stereo matching networks in recent years, it is still challenging to balance realtime performance and accuracy. In this paper, we present a novel edge-preserving cost volume upsampling module based on the slicing operation in the learned bilateral grid. The slicing layer is parameter-free, which allows us to obtain a high quality cost volume of high resolution from a low-resolution cost volume under the guide of the learned guidance map efficiently. The proposed cost volume upsampling module can be seamlessly embedded into many existing stereo matching networks, such as GCNet, PSMNet, and GANet. The resulting networks are accelerated several times while maintaining comparable accuracy. Furthermore, we design a real-time network (named BGNet) based on this module, which outperforms existing published real-time deep stereo matching networks, as well as some complex networks on the KITTI stereo datasets. The code is available at https://***/YuhuaXu/BGNet.

关键词： computer vision Costs Codes Navigation Estimation Complex networks Network architecture

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Visually Informed Binaural Audio Generation without Binaural Audios

Visually Informed Binaural Audio Generation without Binaural...

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

作者： Xu, Xudong Zhou, Hang Liu, Ziwei Dai, Bo Wang, Xiaogang Lin, Dahua Chinese Univ Hong Kong CUHK SenseTime Joint Lab Hong Kong Peoples R China Nanyang Technol Univ S Lab Singapore Singapore

ISBN: (纸本)9781665445092

Stereophonic audio, especially binaural audio, plays an essential role in immersive viewing environments. Recent research has explored generating visually guided stereophonic audios supervised by multi-channel audio collections. However, due to the requirement of professional recording devices, existing datasets are limited in scale and variety, which impedes the generalization of supervised methods in real-world scenarios. In this work, we propose PseudoBinaural, an effective pipeline that is free of binaural recordings. The key insight is to carefully build pseudo visual-stereo pairs with mono data for training. Specifically, we leverage spherical harmonic decomposition and head-related impulse response (HRIR) to identify the relationship between spatial locations and received binaural audios. Then in the visual modality, corresponding visual cues of the mono data are manually placed at sound source positions to form the pairs. Compared to fully-supervised paradigms, our binaural-recording-free pipeline shows great stability in cross-dataset evaluation and achieves comparable performance under subjective preference. Moreover, combined with binaural recordings, our method is able to further boost the performance of binaural audio generation under supervised settings(1).

关键词： Training Visualization computer vision Computational modeling Pipelines Audio recording Harmonic analysis

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Unbiased Mean Teacher for Cross-domain Object Detection

Unbiased Mean Teacher for Cross-domain Object Detection

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

作者： Deng, Jinhong Li, Wen Chen, Yuhua Duan, Lixin Univ Elect Sci & Technol China Chengdu Sichuan Peoples R China Swiss Fed Inst Technol Comp Vis Lab Zurich Switzerland

ISBN: (纸本)9781665445092

Cross-domain object detection is challenging, because object detection model is often vulnerable to data variance, especially to the considerable domain shift between two distinctive domains. In this paper, we propose a new Unbiased Mean Teacher (UMT) model for cross-domain object detection. We reveal that there often exists a considerable model bias for the simple mean teacher (MT) model in cross-domain scenarios, and eliminate the model bias with several simple yet highly effective strategies. In particular, for the teacher model, we propose a cross-domain distillation method for MT to maximally exploit the expertise of the teacher model. Moreover, for the student model, we alleviate its bias by augmenting training samples with pixel-level adaptation. Finally, for the teaching process, we employ an out-of-distribution estimation strategy to select samples that most fit the current model to further enhance the cross-domain distillation process. By tackling the model bias issue with these strategies, our UMT model achieves mAPs of 44.1%, 58.1%, 41.7%, and 43.1% on benchmark datasets Clipart1k, Watercolor2k, Foggy Cityscapes, and Cityscapes, respectively, which outperforms the (listing state-of-the-art results in notable margins. Our implementation is available at hups://***/kinredon/umt.

关键词： Training Adaptation models computer vision Computational modeling 3G mobile communication Estimation Object detection

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