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MFP: Making Full Use of Probability Maps for Interactive Image Segmentation

MFP: Making Full Use of Probability Maps for Interactive Ima...

引用

conference on computer vision and pattern recognition (cvpr)

作者： Chaewon Lee Seon-Ho Lee Chang-Su Kim Korea University

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

In recent interactive segmentation algorithms, previous probability maps are used as network input to help predictions in the current segmentation round. However, despite the utilization of previous masks, useful information contained in the probability maps is not well propagated to the current predictions. In this paper, to overcome this limitation, we propose a novel and effective algorithm for click-based interactive image segmentation, called MFP, which attempts to make full use of probability maps. We first modulate previous probability maps to enhance their represen-tations of user-specified objects. Then, we feed the modulated probability maps as additional input to the segmentation network. We implement the proposed MFP algorithm based on the ResNet-34, HRNet-18, and ViT-B backbones and assess the performance extensively on various datasets. It is demonstrated that MFP meaningfully outperforms the existing algorithms using identical backbones. The source codes are available at hups.//***/cwleetul/Ml-P.

关键词： Image segmentation computer vision Source coding Prediction algorithms pattern recognition Feeds

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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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Enhancing Quality of Compressed Images by Mitigating Enhancement Bias Towards Compression Domain

Enhancing Quality of Compressed Images by Mitigating Enhance...

引用

conference on computer vision and pattern recognition (cvpr)

作者： Qunliang Xing Mai Xu Shengxi Li Xin Deng Meisong Zheng Huaida Liu Ying Chen Beihang University

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

Existing quality enhancement methods for compressed images focus on aligning the enhancement domain with the raw domain to yield realistic images. However, these methods exhibit a pervasive enhancement bias towards the compression domain, inadvertently regarding it as more realistic than the raw domain. This bias makes enhanced images closely resemble their compressed counterparts, thus degrading their perceptual quality. In this paper, we propose a simple yet effective method to mitigate this bias and enhance the quality of compressed images. Our method employs a conditional discriminator with the compressed image as a key condition, and then incorporates a domain-divergence regularization to actively distance the enhancement domain from the compression domain. Through this dual strategy, our method enables the discrimination against the compression domain, and brings the enhancement domain closer to the raw domain. Comprehensive quality evaluations confirm the superiority of our method over other state-of-the-art methods without incurring inference overheads.

关键词： Image quality computer vision Image coding pattern recognition

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Efficient deformable shape correspondence via multiscale spectral manifold wavelets preservation

Efficient deformable shape correspondence via multiscale spe...

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

作者： Hu, Ling Li, Qinsong Liu, Shengjun Liu, Xinru Cent South Univ Inst Engn Modeling & Sci Comp Changsha Peoples R China Hunan First Normal Univ Sch Math & Computat Sci Changsha Peoples R China Cent South Univ State Key Lab High Performance Mfg Complex Changsha Peoples R China

ISBN: (纸本)9781665445092

The functional map framework has proven to be extremely effective for representing dense correspondences between deformable shapes. A key step in this framework is to formulate suitable preservation constraints to encode the geometric information that must be preserved by the unknown map. For this issue, we construct novel and powerful constraints to determine the functional map, where multiscale spectral manifold wavelets are required to be preserved at each scale correspondingly. Such constraints allow us to extract significantly more information than previous methods, especially those based on descriptor preservation constraints, and strongly ensure the isometric property of the map. In addition, we also propose a remarkable efficient iterative method to alternatively update the functional maps and pointwise maps. Moreover, when we use the tight wavelet frames in iterations, the computation of the functional maps boils down to a simple filtering procedure with low-pass and various band-pass filters, which avoids time-consuming solving large systems of linear equations commonly presented in functional maps. We demonstrate on a wide variety of experiments with different datasets that our approach achieves significant improvements both in the shape correspondence quality and the computing efficiency.

关键词： Manifolds Band-pass filters computer vision Shape Filtering Color Iterative methods

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Hierarchical Temporal Transformer for 3D Hand Pose Estimation and Action recognition from Egocentric RGB Videos

Hierarchical Temporal Transformer for 3D Hand Pose Estimatio...

引用

conference on computer vision and pattern recognition (cvpr)

作者： Yilin Wen Hao Pan Lei Yang Jia Pan Taku Komura Wenping Wang The University of Hong Kong Microsoft Research Asia TransGP Texas A&M University

Understanding dynamic hand motions and actions from egocentric RGB videos is a fundamental yet challenging task due to self-occlusion and ambiguity. To address occlusion and ambiguity, we develop a transformer-based framework to exploit temporal information for robust estimation. Noticing the different temporal granularity of and the semantic correlation between hand pose estimation and action recognition, we build a network hierarchy with two cascaded transformer encoders, where the first one exploits the short-term temporal cue for hand pose estimation, and the latter aggregates per-frame pose and object information over a longer time span to recognize the action. Our approach achieves competitive results on two first-person hand action benchmarks, namely FPHA and H2O. Extensive ablation studies verify our design choices.

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Combining Semantic Guidance and Deep Reinforcement Learning For Generating Human Level Paintings

Combining Semantic Guidance and Deep Reinforcement Learning ...

引用

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

作者： Singh, Jaskirat Zheng, Liang Australian Natl Univ Canberra ACT Australia

ISBN: (纸本)9781665445092

Generation of stroke-based non-photorealistic imagery, is an important problem in the computer vision community. As an endeavor in this direction, substantial recent research efforts have been focused on teaching machines "how to paint", in a manner similar to a human painter. However, the applicability of previous methods has been limited to datasets with little variation in position, scale and saliency of the foreground object. As a consequence, we find that these methods struggle to cover the granularity and diversity possessed by real world images. To this end, we propose a Semantic Guidance pipeline with 1) a bi-level painting procedure for learning the distinction between foreground and background brush strokes at training time. 2) We also introduce invariance to the position and scale of the foreground object through a neural alignment model, which combines object localization and spatial transformer networks in an end to end manner, to zoom into a particular semantic instance. 3) The distinguishing features of the in-focus object are then amplified by maximizing a novel guided backpropagation based focus reward. The proposed agent does not require any supervision on human stroke-data and successfully handles variations in foreground object attributes, thus, producing much higher quality canvases for the CUB-200 Birds [29] and Stanford Cars-196 [17] datasets. Finally, we demonstrate the further efficacy of our method on complex datasets with multiple foreground object instances by evaluating an extension of our method on the challenging Virtual-KITTI [2] dataset. Source code and models are available at https://***/1jsingh/semantic-guidance.

关键词： Backpropagation Training computer vision Semantics Pipelines Reinforcement learning Stroke (medical condition)

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Semantic Segmentation with Generative Models: Semi-Supervised Learning and Strong Out-of-Domain Generalization

Semantic Segmentation with Generative Models: Semi-Supervise...

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

作者： Li, Daiqing Yang, Junlin Kreis, Karsten Torralba, Antonio Fidler, Sanja NVIDIA Santa Clara CA 95051 USA Univ Toronto Toronto ON Canada Yale Univ New Haven CT 06520 USA MIT 77 Massachusetts Ave Cambridge MA 02139 USA Vector Inst Toronto ON Canada

ISBN: (纸本)9781665445092

Training deep networks with limited labeled data while achieving a strong generalization ability is key in the quest to reduce human annotation efforts. This is the goal of semi-supervised learning, which exploits more widely available unlabeled data to complement small labeled data sets. In this paper, we propose a novel framework for discriminative pixel-level tasks using a generative model of both images and labels. Concretely, we learn a generative adversarial network that captures the joint image-label distribution and is trained efficiently using a large set of unlabeled images supplemented with only few labeled ones. We build our architecture on top of StyleGAN2 [45], augmented with a label synthesis branch. Image labeling at test time is achieved by first embedding the target image into the joint latent space via an encoder network and test-time optimization, and then generating the label from the inferred embedding. We evaluate our approach in two important domains: medical image segmentation and part-based face segmentation. We demonstrate strong in-domain performance compared to several baselines, and are the first to showcase extreme out-of-domain generalization, such as transferring from CT to MRI in medical imaging, and photographs of real faces to paintings, sculptures, and even cartoons and animal faces. Project Page: http://***/semanticGAN/

关键词： Training Image segmentation Annotations Animals Face recognition Computational modeling Semantics

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CORES: Convolutional Response-based Score for Out-of-distribution Detection

CORES: Convolutional Response-based Score for Out-of-distrib...

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conference on computer vision and pattern recognition (cvpr)

作者： Keke Tang Chao Hou Weilong Peng Runnan Chen Peican Zhu Wenping Wang Zhihong Tian Guangzhou University University of Hong Kong Northwestern Polytechnical University Texas A&M University

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

Deep neural networks (DNNs) often display overconfidence when encountering out-of-distribution (OOD) samples, posing significant challenges in real-world applications. Capitalizing on the observation that responses on convolutional kernels are generally more pronounced for in-distribution (ID) samples than for OOD ones, this paper proposes the COnvolutional REsponse-based Score (CORES) to exploit these discrepancies for OOD detection. Initially, CORES delves into the extremities of convolutional responses by considering both their magnitude and the frequency of significant values. Moreover, through backtracking from the most prominent predictions, CORES effectively pinpoints sample-relevant kernels across different layers. These kernels, which exhibit a strong correlation to input samples, are integral to CORES's OOD detection capability. Comprehensive experiments across various ID and OOD settings demonstrate CORES's effectiveness in OOD detection and its superiority to the state-of-the-art methods.

关键词： computer vision Backtracking Correlation Fitting Artificial neural networks pattern recognition Security

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X-MIC: Cross-Modal Instance Conditioning for Egocentric Action Generalization

X-MIC: Cross-Modal Instance Conditioning for Egocentric Acti...

引用

conference on computer vision and pattern recognition (cvpr)

作者： Anna Kukleva Fadime Sener Edoardo Remelli Bugra Tekin Eric Sauser Bernt Schiele Shugao Ma Meta Reality Labs Saarland Informatics Campus Max Planck Institute for Informatics

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

Lately, there has been growing interest in adapting vision-language models (VLMs) to image and third-person video classification due to their success in zero-shot recog-nition. However, the adaptation of these models to egocentric videos has been largely unexplored. To address this gap, we propose a simple yet effective cross-modal adaptation framework, which we call X-MIC. Using a video adapter, our pipeline learns to align frozen text embeddings to each egocentric video directly in the shared embedding space. Our novel adapter architecture retains and improves generalization of the pre-trained VLMs by disentangling learnable temporal modeling and frozen visual en-coder. This results in an enhanced alignment of text embeddings to each egocentric video, leading to a significant improvement in cross-dataset generalization. We evaluate our approach on the Epic-Kitchens, Ego4D, and EGTEA datasets for fine-grained cross-dataset action generalization, demonstrating the effectiveness of our method. 1 1 https://***/annusha/xmic

关键词： Adaptation models Visualization computer vision Pipelines computer architecture pattern recognition

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TediGAN: Text-Guided Diverse Face Image Generation and Manipulation

TediGAN: Text-Guided Diverse Face Image Generation and Manip...

引用

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

作者： Xia, Weihao Yang, Yujiu Xue, Jing-Hao Wu, Baoyuan Tsinghua Univ Tsinghua Shenzhen Int Grad Sch Beijing Peoples R China UCL Dept Stat Sci London England Chinese Univ Hongkong Sch Data Sci Shenzhen Peoples R China Shenzhen Res Inst Big Data Secure Comp Lab Big Data Shenzhen Peoples R China

ISBN: (纸本)9781665445092

In this work, we propose TediGAN, a novel framework for multi-modal image generation and manipulation with textual descriptions. The proposed method consists of three components: StyleGAN inversion module, visual-linguistic similarity learning, and instance-level optimization. The inversion module maps real images to the latent space of a well-trained StyleGAN. The visual-linguistic similarity learns the text-image matching by mapping the image and text into a common embedding space. The instance-level optimization is for identity preservation in manipulation. Our model can produce diverse and high-quality images with an unprecedented resolution at 1024(2). Using a control mechanism based on style-mixing, our TediGAN inherently supports image synthesis with multi-modal inputs, such as sketches or semantic labels, with or without instance guidance. To facilitate text-guided multi-modal synthesis, we propose the Multi-Modal CelebA-HQ, a large-scale dataset consisting of real face images and corresponding semantic segmentation map, sketch, and textual descriptions. Extensive experiments on the introduced dataset demonstrate the superior performance of our proposed method.

关键词： Image segmentation computer vision Image resolution Image synthesis Face recognition Semantics Aerospace electronics

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