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SIPSA-Net: Shift-Invariant Pan Sharpening with Moving Object Alignment for Satellite Imagery

SIPSA-Net: Shift-Invariant Pan Sharpening with Moving Object...

引用

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

作者： Lee, Jaehyup Seo, Soomin Kim, Munchurl Korea Adv Institue Sci & Technol KAIST Seoul South Korea

ISBN: (纸本)9781665445092

Pan-sharpening is a process of merging a high-resolution (HR) panchromatic (PAN) image and its corresponding low-resolution (LR) multi-spectral (MS) image to create an HR-MS and pan-sharpened image. However, due to the different sensors' locations, characteristics and acquisition time, PAN and MS image pairs often tend to have various amounts of misalignment. Conventional deep-learning-based methods that were trained with such misaligned PAN-MS image pairs suffer from diverse artifacts such as double-edge and blur artifacts in the resultant PAN-sharpened images. In this paper, we propose a novel framework called shift-invariant pan-sharpening with moving object alignment (SIPSA-Net) which is the first method to take into account such large misalignment of moving object regions for PAN sharpening. The SISPA-Net has a feature alignment module (FAM) that can adjust one feature to be aligned to another feature, even between the two different PAN and MS domains. For better alignment in pan-sharpened images, a shift-invariant spectral loss is newly designed, which ignores the inherent misalignment in the original MS input, thereby having the same effect as optimizing the spectral loss with a well-aligned MS image. Extensive experimental results show that our SIPSA-Net can generate pan-sharpened images with remarkable improvements in terms of visual quality and alignment, compared to the state-of-the-art methods.

关键词： Visualization computer vision Satellites Image edge detection Merging Feature extraction Probabilistic logic

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Context Modeling in 3D Human Pose Estimation: A Unified Perspective

Context Modeling in 3D Human Pose Estimation: A Unified Pers...

引用

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

作者： Ma, Xiaoxuan Su, Jiajun Wang, Chunyu Ci, Hai Wang, Yizhou Peking Univ Ctr Frontiers Comp Studies Dept Comp Sci Beijing Peoples R China Peking Univ Ctr Data Sci Adv Inst Info Tech Beijing Peoples R China Beijing Film Acad Adv Innovat Ctr Future Visual Entertainment AICFV Beijing Peoples R China Microsoft Res Asia Beijing Peoples R China Deepwise AI Lab Beijing Peoples R China

ISBN: (纸本)9781665445092

Estimating 3D human pose from a single image suffers from severe ambiguity since multiple 3D joint configurations may have the same 2D projection. The state-of-the-art methods often rely on context modeling methods such as pictorial structure model (PSM) or graph neural network (GNN) to reduce ambiguity. However, there is no study that rigorously compares them side by side. So we first present a general formula for context modeling in which both PSM and GNN are its special cases. By comparing the two methods, we found that the end-to-end training scheme in GNN and the limb length constraints in PSM are two complementary factors to improve results. To combine their advantages, we propose ContextPose based on attention mechanism that allows enforcing soft limb length constraints in a deep network. The approach effectively reduces the chance of getting absurd 3D pose estimates with incorrect limb lengths and achieves state-of-the-art results on two benchmark datasets. More importantly, the introduction of limb length constraints into deep networks enables the approach to achieve much better generalization performance.

关键词： Training computer vision Three-dimensional displays Pose estimation Benchmark testing Graph neural networks pattern recognition

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DCNAS: Densely Connected Neural Architecture Search for Semantic Image Segmentation

DCNAS: Densely Connected Neural Architecture Search for Sema...

引用

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

作者： Zhang, Xiong Xu, Hongmin Mo, Hong Tan, Jianchao Yang, Cheng Wang, Lei Ren, Wenqi Joyy Inc Singapore Singapore Kwai Inc AI Platform Bengaluru India Beihang Univ State Key Lab VR Beijing Peoples R China Chinese Acad Sci IIE SKLOIS Beijing Peoples R China

ISBN: (纸本)9781665445092

Existing NAS methods for dense image prediction tasks usually compromise on restricted search space or search on proxy task to meet the achievable computational demands. To allow as wide as possible network architectures and avoid the gap between realistic and proxy setting, we propose a novel Densely Connected NAS (DCNAS) framework, which directly searches the optimal network structures for the multi-scale representations of visual information, over a large-scale target dataset without proxy. Specifically, by connecting cells with each other using learnable weights, we introduce a densely connected search space to cover an abundance of mainstream network designs. Moreover, by combining both path-level and channel-level sampling strategies, we design a fusion module and mixture layer to reduce the memory consumption of ample search space, hence favoring the proxyless searching. Compared with contemporary works, experiments reveal that the proxyless searching scheme is capable of bridging the gap between searching and training environments. Further, DCNAS achieves new state-of-the-art performances on public semantic image segmentation benchmarks, including 84.3% on Cityscapes, and 86.9% on PASCAL VOC 2012. We also retain leading performances when evaluating the architecture on the more challenging ADE20K and PASCAL-Context dataset.

关键词： Training Image segmentation Visualization computer vision Semantics Memory management 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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Learning to Name Classes for vision and Language Models

Learning to Name Classes for Vision and Language Models

引用

conference on computer vision and pattern recognition (cvpr)

作者： Sarah Parisot Yongxin Yang Steven McDonagh Huawei Noah's Ark Lab

Large scale vision and language models can achieve impressive zero-shot recognition performance by mapping class specific text queries to image content. Two distinct challenges that remain however, are high sensitivity to the choice of handcrafted class names that define queries, and the difficulty of adaptation to new, smaller datasets. Towards addressing these problems, we propose to leverage available data to learn, for each class, an optimal word embedding as a function of the visual content. By learning new word embeddings on an otherwise frozen model, we are able to retain zero-shot capabilities for new classes, easily adapt models to new datasets, and adjust potentially erroneous, non-descriptive or ambiguous class names. We show that our solution can easily be integrated in image classification and object detection pipelines, yields significant performance gains in multiple scenarios and provides insights into model biases and labelling errors.

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Regressive Domain Adaptation for Unsupervised Keypoint Detection

Regressive Domain Adaptation for Unsupervised Keypoint Detec...

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

作者： Jiang, Junguang Ji, Yifei Wang, Ximei Liu, Yufeng Wang, Jianmin Long, Mingsheng Tsinghua Univ Sch Software BNRist Beijing Peoples R China Kuaishou Technol Y Tech Beijing Peoples R China

ISBN: (纸本)9781665445092

Domain adaptation (DA) aims at transferring knowledge from a labeled source domain to an unlabeled target domain. Though many DA theories and algorithms have been proposed, most of them are tailored into classification settings and may fail in regression tasks, especially in the practical keypoint detection task. To tackle this difficult but significant task, we present a method of regressive domain adaptation (RegDA) for unsupervised keypoint detection. Inspired by the latest theoretical work, we first utilize an adversarial regressor to maximize the disparity on the target domain and train a feature generator to minimize this disparity. However, due to the high dimension of the output space, this regressor fails to detect samples that deviate from the support of the source. To overcome this problem, we propose two important ideas. First, based on our observation that the probability density of the output space is sparse, we introduce a spatial probability distribution to describe this sparsity and then use it to guide the learning of the adversarial regressor. Second, to alleviate the optimization difficulty in the high-dimensional space, we innovatively convert the minimax game in the adversarial training to the minimization of two opposite goals. Extensive experiments show that our method brings large improvement by 8% to 11% in terms of PCK on different datasets.

关键词： Training computer vision Games Superluminescent diodes Minimization Probability distribution Generators

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Metadata Normalization

Metadata Normalization

引用

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

作者： Lu, Mandy Zhao, Qingyu Zhang, Jiequan Pohl, Kilian M. Li Fei-Fei Niebles, Juan Carlos Adeli, Ehsan Stanford Univ Stanford CA 94305 USA

ISBN: (纸本)9781665445092

Batch Normalization (BN) and its variants have delivered tremendous success in combating the covariate shift induced by the training step of deep learning methods. While these techniques normalize the feature distribution by standardizing with batch statistics, they do not correct the influence on features from extraneous variables or multiple distributions. Such extra variables, referred to as meta-data here, may create bias or confounding effects (e.g., race when classifying gender from face images). We introduce the Metadata Normalization (MDN) layer, a new batch-level operation which can be used end-to-end within the training framework, to correct the influence of meta-data on the feature distribution. MDN adopts a regression analysis technique traditionally used for preprocessing to remove (regress out) the metadata effects on model features during training. We utilize a metric based on distance correlation to quantify the distribution bias from the meta-data and demonstrate that our method successfully removes metadata effects on four diverse settings: one synthetic, one 2D image, one video, and one 3D medical image dataset.

关键词： Deep learning Training Measurement Three-dimensional displays Computational modeling Face recognition computer architecture

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Understanding the Robustness of 3D Object Detection with Bird'View Representations in Autonomous Driving

Understanding the Robustness of 3D Object Detection with Bir...

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2023 ieee/cvf conference on computer vision and pattern recognition, cvpr 2023

作者： Zhu, Zijian Zhang, Yichi Chen, Hai Dong, Yinpeng Zhao, Shu Ding, Wenbo Zhong, Jiachen Zheng, Shibao Institute of Image Communication and Network Engineering Shanghai Jiao Tong University China Institute for Ai Tsinghua University BNRist Center Thbi Lab Dept. of Comp. Sci. and Tech. China School of Computer Science and Technology Anhui University Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Information Materials and Intelligent Sensing Laboratory of Anhui Province China Saic Motor Ai Lab Zhongguancun Laboratory China

ISBN: (纸本)9798350301298

3D object detection is an essential perception task in autonomous driving to understand the environments. The Bird's-Eye-View (BEV) representations have significantly improved the performance of 3D detectors with camera inputs on popular benchmarks. However, there still lacks a systematic understanding of the robustness of these vision-dependent BEV models, which is closely related to the safety of autonomous driving systems. In this paper, we evaluate the natural and adversarial robustness of various representative models under extensive settings, to fully understand their behaviors influenced by explicit BEV features compared with those without BEV. In addition to the classic settings, we propose a 3D consistent patch attack by applying adversarial patches in the 3D space to guarantee the spatiotemporal consistency, which is more realistic for the scenario of autonomous driving. With substantial experiments, we draw several findings: 1) BEV models tend to be more stable than previous methods under different natural conditions and common corruptions due to the expressive spatial representations;2) BEV models are more vulnerable to adversarial noises, mainly caused by the redundant BEV features;3) Camera-LiDARfusion models have superior performance under different settings with multi-modal inputs, but BEV fusion model is still vulnerable to adversarial noises of both point cloud and image. These findings alert the safety issue in the applications of BEV detectors and could facilitate the development of more robust models. © 2023 ieee.

关键词： Autonomous driving

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Sewer-ML: A Multi-Label Sewer Defect Classification Dataset and Benchmark

Sewer-ML: A Multi-Label Sewer Defect Classification Dataset ...

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

作者： Haurum, Joakim Bruslund Moeslund, Thomas B. Aalborg Univ Visual Anal & Percept VAP Lab Aalborg Denmark

ISBN: (纸本)9781665445092

Perhaps surprisingly sewerage infrastructure is one of the most costly infrastructures in modern society. Sewer pipes are manually inspected to determine whether the pipes are defective. However, this process is limited by the number of qualified inspectors and the time it takes to inspect a pipe. Automatization of this process is therefore of high interest. So far, the success of computer vision approaches for sewer defect classification has been limited when compared to the success in other fields mainly due to the lack of public datasets. To this end, in this work we present a large novel and publicly available multi-label classification dataset for image-based sewer defect classification called Sewer-ML. The Sewer-ML dataset consists of 1.3 million images annotated by professional sewer inspectors from three different utility companies across nine years. Together with the dataset, we also present a benchmark algorithm and a novel metric for assessing performance. The benchmark algorithm is a result of evaluating 12 state-of-the-art algorithms, six from the sewer defect classification domain and six from the multi-label classification domain, and combining the best performing algorithms. The novel metric is a class-importance weighted F2 score, F2CIW, reflecting the economic impact of each class, used together with the normal pipe F1 score, F1Normal. The benchmark algorithm achieves an F2CIW score of 55.11% and F1Normal score of 90.94%, leaving ample room for improvement on the SewerML dataset. The code, models, and dataset are available at the project page http://***/sewer-ml

关键词： Measurement Economics computer vision Codes Biological system modeling Benchmark testing Inspection

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Self-Supervised Visibility Learning for Novel View Synthesis

Self-Supervised Visibility Learning for Novel View Synthesis

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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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