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RepMode: Learning to Re-Parameterize Diverse Experts for Subcellular Structure Prediction

RepMode: Learning to Re-Parameterize Diverse Experts for Sub...

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conference on computer vision and pattern recognition (CVPR)

作者： Donghao Zhou Chunbin Gu Junde Xu Furui Liu Qiong Wang Guangyong Chen Pheng-Ann Heng Guangdong Provincial Key Laboratory of Computer Vision and Virtual Reality Technology Shenzhen Institute of Advanced Technology Chinese Academy of Sciences University of Chinese Academy of Sciences The Chinese University of Hong Kong Zhejiang Lab

In biological research, fluorescence staining is a key technique to reveal the locations and morphology of subcellular structures. However, it is slow, expensive, and harmful to cells. In this paper, we model it as a deep learning task termed subcellular structure prediction (SSP), aiming to predict the 3D fluorescent images of multiple subcellular structures from a 3D transmitted-light image. Unfortunately, due to the limitations of current biotechnology, each image is partially labeled in SSP. Besides, naturally, subcellular structures vary considerably in size, which causes the multi-scale issue of SSP. To overcome these challenges, we propose Re-parameterizing Mixture-of-Diverse-Experts (RepMode), a network that dynamically organizes its parameters with task-aware priors to handle specified single-label prediction tasks. In RepMode, the Mixture-of-Diverse-Experts (MoDE) block is designed to learn the generalized parameters for all tasks, and gating re-parameterization (GatRep) is performed to generate the specialized parameters for each task, by which RepMode can maintain a compact practical topology exactly like a plain network, and meanwhile achieves a powerful theoretical topology. Comprehensive experiments show that RepMode can achieve state-of-the-art overall performance in SSP.

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Weakly Supervised Segmentation with Point Annotations for Histopathology Images via Contrast-Based Variational Model

Weakly Supervised Segmentation with Point Annotations for Hi...

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conference on computer vision and pattern recognition (CVPR)

作者： Hongrun Zhang Liam Burrows Yanda Meng Declan Sculthorpe Abhik Mukherjee Sarah E Coupland Ke Chen Yalin Zheng Department of Eye and Vision Science University of Liverpool Liverpool UK Institute of Systems Molecular and Integrative Biology University of Liverpool Liverpool UK Department of Mathematical Sciences and Centre for Mathematical Imaging Techniques University of Liverpool Liverpool UK Biodiscovery Institute School of Medicine University of Nottingham Nottingham UK

Image segmentation is a fundamental task in the field of imaging and vision. Supervised deep learning for segmentation has achieved unparalleled success when sufficient training data with annotated labels are available. However, annotation is known to be expensive to obtain, especially for histopathology images where the target regions are usually with high morphology variations and irregular shapes. Thus, weakly supervised learning with sparse annotations of points is promising to reduce the annotation workload. In this work, we propose a contrast-based variational model to generate segmentation results, which serve as reliable complementary supervision to train a deep segmentation model for histopathology images. The proposed method considers the common characteristics of target regions in histopathology images and can be trained in an end-to-end manner. It can generate more regionally consistent and smoother boundary segmentation, and is more robust to unlabeled ‘novel’ regions. Experiments on two different histology datasets demonstrate its effectiveness and efficiency in comparison to previous models. Code is available at: https://***/hrzhang1123/CVM_WS_Segmentation.

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Delving into Shape-aware Zero-shot Semantic Segmentation

Delving into Shape-aware Zero-shot Semantic Segmentation

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conference on computer vision and pattern recognition (CVPR)

作者： Xinyu Liu Beiwen Tian Zhen Wang Rui Wang Kehua Sheng Bo Zhang Hao Zhao Guyue Zhou Xidian University Institute for AI Industry Research (AIR) Tsinghua University Department of Computer Science and Technology Tsinghua University Didi Chuxing

Thanks to the impressive progress of large-scale vision-language pretraining, recent recognition models can classify arbitrary objects in a zero-shot and open-set manner, with a surprisingly high accuracy. However, translating this success to semantic segmentation is not trivial, because this dense prediction task requires not only accurate semantic understanding but also fine shape delineation and existing vision-language models are trained with image-level language descriptions. To bridge this gap, we pursue shape-aware zero-shot semantic segmentation in this study. Inspired by classical spectral methods in the image segmentation literature, we propose to leverage the eigen vectors of Laplacian matrices constructed with self-supervised pixel-wise features to promote shape-awareness. Despite that this simple and effective technique does not make use of the masks of seen classes at all, we demonstrate that it out-performs a state-of-the-art shape-aware formulation that aligns ground truth and predicted edges during training. We also delve into the performance gains achieved on different datasets using different backbones and draw several interesting and conclusive observations: the benefits of promoting shape-awareness highly relates to mask compactness and language embedding locality. Finally, our method sets new state-of-the-art performance for zero-shot semantic segmentation on both Pascal and COCO, with significant margins. Code and models will be accessed at SAZS.

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Modality-invariant Visual Odometry for Embodied vision

Modality-invariant Visual Odometry for Embodied Vision

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conference on computer vision and pattern recognition (CVPR)

作者： Marius Memmel Roman Bachmann Amir Zamir University of Washington Swiss Federal Institute of Technology (EPFL)

Effectively localizing an agent in a realistic, noisy setting is crucial for many embodied vision tasks. Visual Odometry (VO) is a practical substitute for unreliable GPS and compass sensors, especially in indoor environments. While SLAM-based methods show a solid performance without large data requirements, they are less flexible and robust w.r.t. to noise and changes in the sensor suite compared to learning-based approaches. Recent deep VO models, however, limit themselves to a fixed set of input modalities, e.g., RGB and depth, while training on millions of samples. When sensors fail, sensor suites change, or modalities are intentionally looped out due to available resources, e.g., power consumption, the models fail catastrophically. Furthermore, training these models from scratch is even more expensive without simulator access or suitable existing models that can be fine-tuned. While such scenarios get mostly ignored in simulation, they commonly hinder a model's reusability in real-world applications. We propose a Transformer-based modality-invariant VO approach that can deal with diverse or changing sensor suites of navigation agents. Our model outperforms previous methods while training on only a fraction of the data. We hope this method opens the door to a broader range of real-world applications that can benefit from flexible and learned VO models.

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OSRT: Omnidirectional Image Super-Resolution with Distortion-aware Transformer

OSRT: Omnidirectional Image Super-Resolution with Distortion...

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conference on computer vision and pattern recognition (CVPR)

作者： Fanghua Yu Xintao Wang Mingdeng Cao Gen Li Ying Shan Chao Dong ShenZhen Key Lab of Computer Vision and Pattern Recognition Shenzhen Institute of Advanced Technology Chinese Academy of Sciences ARC Tencent PCG The University of Tokyo Platform Technologies Tencent Online Video Shanghai Artificial Intelligence Laboratory

Omnidirectional images (ODIs) have obtained lots of research interest for immersive experiences. Although ODIs require extremely high resolution to capture details of the entire scene, the resolutions of most ODIs are insufficient. Previous methods attempt to solve this issue by image super-resolution (SR) on equirectangular projection (ERP) images. However, they omit geometric properties of ERP in the degradation process, and their models can hardly generalize to real ERP images. In this paper, we propose Fisheye downsampling, which mimics the real-world imaging process and synthesizes more realistic low-resolution samples. Then we design a distortion-aware Transformer (OSRT) to modulate ERP distortions continuously and self-adaptively. Without a cumbersome process, OSRT outperforms previous methods by about 0.2dB on PSNR. Moreover, we propose a convenient data augmentation strategy, which synthesizes pseudo ERP images from plain images. This simple strategy can alleviate the over-fitting problem of large networks and significantly boost the performance of ODISR. Extensive experiments have demonstrated the state-of-the-art performance of our OSRT.

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iDisc: Internal Discretization for Monocular Depth Estimation

iDisc: Internal Discretization for Monocular Depth Estimatio...

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conference on computer vision and pattern recognition (CVPR)

作者： Luigi Piccinelli Christos Sakaridis Fisher Yu Computer Vision Lab ETH Zürich

Monocular depth estimation is fundamental for 3D scene understanding and downstream applications. However, even under the supervised setup, it is still challenging and ill-posed due to the lack of full geometric constraints. Although a scene can consist of millions of pixels, there are fewer high-level patterns. We propose iDisc to learn those patterns with internal discretized representations. The method implicitly partitions the scene into a set of high-level patterns. In particular, our new module, Internal Discretization (ID), implements a continuous-discrete-continuous bottleneck to learn those concepts without supervision. In contrast to state-of-the-art methods, the proposed model does not enforce any explicit constraints or priors on the depth output. The whole network with the ID module can be trained end-to-end, thanks to the bottleneck module based on attention. Our method sets the new state of the art with significant improvements on NYU-Depth v2 and KITTI, outperforming all published methods on the official KITTI benchmark. iDisc can also achieve state-of-the-art results on surface normal estimation. Further, we explore the model generalization capability via zero-shot testing. We observe the compelling need to promote diversification in the outdoor scenario. Hence, we introduce splits of two autonomous driving datasets, DDAD and Argoverse. Code is available at http://***/pub/idisc.

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Reconstruct, Rasterize and Backprop: Dense shape and pose estimation from a single image

Reconstruct, Rasterize and Backprop: Dense shape and pose es...

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

作者： Pokale, Aniket Aggarwal, Aditya Jatavallabhula, Krishna Murthy Krishna, K. Madhava IIIT Hyderabad Robot Res Ctr KCIS Hyderabad India Univ Montreal Mila Montreal PQ Canada

ISBN: (纸本)9781728193601

This paper presents a new system to obtain dense object reconstructions along with 6-DoF poses from a single image. Geared towards high fidelity reconstruction, several recent approaches leverage implicit surface representations and deep neural networks to estimate a 3D mesh of an object, given a single image. However, all such approaches recover only the shape of an object;the reconstruction is often in a canonical frame, unsuitable for downstream robotics tasks. To this end, we leverage recent advances in differentiable rendering (in particular, rasterization) to close the loop with 3D reconstruction in camera frame. We demonstrate that our approach-dubbed reconstruct, rasterize and backprop (RRB)-achieves significantly lower pose estimation errors compared to prior art, and is able to recover dense object shapes and poses from imagery. We further extend our results to an (offline) setup, where we demonstrate a dense monocular object-centric egomotion estimation system.

关键词： Three dimensional computer graphics

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Utilizing Mask R-CNN for Waterline Detection in Canoe Sprint Video Analysis

Utilizing Mask R-CNN for Waterline Detection in Canoe Sprint...

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

作者： Von Braun, Marie-Sophie Frenzel, Patrick Kaeding, Christian Fuchs, Mirco Leipzig Univ Appl Sci Lab Biosignal Proc Leipzig Germany Inst Appl Training Sci Leipzig Leipzig Germany

ISBN: (纸本)9781728193601

Determining a waterline in images recorded in canoe sprint training is an important component for the kinematic parameter analysis to assess an athlete's performance. Here, we propose an approach for the automated waterline detection. First, we utilized a pre-trained Mask R-CNN by means of transfer learning for canoe segmentation. Second, we developed a multi-stage approach to estimate a waterline from the outline of the segments. It consists of two linear regression stages and the systematic selection of canoe parts. We then introduced a parameterization of the waterline as a basis for further evaluations. Next, we conducted a study among several experts to estimate the ground truth waterlines. This not only included an average waterline drawn from the individual experts annotations but, more importantly, a measure for the uncertainty between individual results. Finally, we assessed our method with respect to the question whether the predicted waterlines are in accordance with the experts annotations. Our method demonstrated a high performance and provides opportunities for new applications in the field of automated video analysis in canoe sprint.

关键词： computer vision

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N-Gram in Swin Transformers for Efficient Lightweight Image Super-Resolution

N-Gram in Swin Transformers for Efficient Lightweight Image ...

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conference on computer vision and pattern recognition (CVPR)

作者： Haram Choi Jeongmin Lee Jihoon Yang Department of Computer Science & Engineering Sogang University LG Innotek

While some studies have proven that Swin Transformer (Swin) with window self-attention (WSA) is suitable for single image super-resolution (SR), the plain WSA ignores the broad regions when reconstructing high-resolution images due to a limited receptive field. In addition, many deep learning SR methods suffer from intensive computations. To address these problems, we introduce the N-Gram context to the low-level vision with Transformers for the first time. We define N-Gram as neighboring local windows in Swin, which differs from text analysis that views N-Gram as consecutive characters or words. N-Grams interact with each other by sliding-WSA, expanding the regions seen to restore degraded pixels. Using the N-Gram context, we propose NGswin, an efficient SR network with SCDP bottleneck taking multi-scale outputs of the hierarchical encoder. Experimental results show that NGswin achieves competitive performance while maintaining an efficient structure when compared with previous leading methods. Moreover, we also improve other Swin-based SR methods with the N-Gram context, thereby building an enhanced model: SwinIR-NG. Our improved SwinIR-NG out-performs the current best lightweight SR approaches and establishes state-of-the-art results. Codes are available at https://***/rami0205/NGramSwin.

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Understanding Knowledge Gaps in Visual Question Answering: Implications for Gap Identification and Testing

Understanding Knowledge Gaps in Visual Question Answering: I...

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

作者： Bajaj, Goonmeet Bandyopadhyay, Bortik Schmidt, Daniel Maneriker, Pranav Myers, Christopher Parthasarathy, Srinivasan Ohio State Univ OSU Columbus OH 43210 USA Wright State Univ Dayton OH 45435 USA Air Force Res Lab AFRL Wright Patterson AFB OH USA

ISBN: (纸本)9781728193601

Traditional Visual Question Answering (VQA) datasets typically contain questions related to the spatial information of objects, object attributes, or general scene questions. Recently, researchers have recognized the need to improve the balance of such datasets to reduce the system's dependency on memorized linguistic features and statistical biases, while aiming for enhanced visual understanding. However, it is unclear whether any latent patterns exist to quantify and explain these failures. As an initial step towards better quantifying our understanding of the performance of VQA models, we use a taxonomy of Knowledge Gaps (KGs) to tag questions with one or more types of KGs. Each KG describes the reasoning abilities needed to arrive at a resolution, and failure to resolve gaps indicates an absence of the required reasoning ability. After identifying KGs for each question, we examine the skew in the distribution of questions for each KG. We then introduce a targeted question generation model to reduce this skew, which allows us to generate new types of questions for an image.

关键词： computer vision

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