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检索条件"任意字段=Conference on Computer Vision and Pattern Recognition"

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clDice - a Novel Topology-Preserving Loss Function for Tubular Structure Segmentation

clDice - a Novel Topology-Preserving Loss Function for Tubul...

引用

IEEE/CVF conference on computer vision and pattern recognition (CVPR)

作者： Shit, Suprosanna Paetzold, Johannes C. Sekuboyina, Anjany Ezhov, Ivan Unger, Alexander Zhylka, Andrey Pluim, Josien P. W. Bauer, Ulrich Menze, Bjoern H. Tech Univ Munich Munich Germany Eindhoven Univ Technol Eindhoven Netherlands

ISBN: (纸本)9781665445092

Accurate segmentation of tubular, network-like structures, such as vessels, neurons, or roads, is relevant to many fields of research. For such structures, the topology is their most important characteristic;particularly preserving connectedness: in the case of vascular networks, missing a connected vessel entirely alters the blood-flow dynamics. We introduce a novel similarity measure termed centerline-Dice (short clDice), which is calculated on the intersection of the segmentation masks and their (morphological) skeleta. We theoretically prove that clDice guarantees topology preservation up to homotopy equivalence for binary 2D and 3D segmentation. Extending this, we propose a computationally efficient, differentiable loss function (soft-clDice) for training arbitrary neural segmentation networks. We benchmark the soft-clDice loss on five public datasets, including vessels, roads and neurons (2D and 3D). Training on soft-clDice leads to segmentation with more accurate connectivity information, higher graph similarity, and better volumetric scores.

关键词： Training computer vision Three-dimensional displays Network topology Roads Neurons Neural networks

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YOLO-World: Real-Time Open-Vocabulary Object Detection

YOLO-World: Real-Time Open-Vocabulary Object Detection

引用

IEEE/CVF conference on computer vision and pattern recognition (CVPR)

作者： Cheng, Tianheng Sone, Lin Ge, Yixiao Liu, Wenyu Wang, Xinggang Shan, Yong Tencent AI Lab Shenzhen Guangdong Peoples R China Tencent PCG ARC Lab Shenzhen Guangdong Peoples R China Huazhong Univ Sci & Technol Sch EIC Wuhan Hubei Peoples R China

ISBN: (纸本)9798350353006

The You Only Look Once (YOLO) series of detectors have established themselves as efficient and practical tools. However, their reliance on predefined and trained object categories limits their applicability in open scenarios. Addressing this limitation, we introduce YOLO-World, an innovative approach that enhances YOLO with open-vocabulary detection capabilities through vision-language modeling and pre-training on large-scale datasets. Specifically, we propose a new Re-parameterizable vision-Language Path Aggregation Network (RepVL-PAN) and region-text contrastive loss to facilitate the interaction between visual and linguistic information. Our method excels in detecting a wide range of objects in a zero-shot manner with high efficiency. On the challenging LVIS dataset, YOLO- World achieves 35.4 AP with 52.0 FPS on V100, which outperforms many state-of-the-art methods in terms of both accuracy and speed. Furthermore, the fine-tuned YOLO-World achieves remarkable performance on several downstream tasks, including object detection and open-vocabulary instance segmentation. Code and models are available at: https://***/AILab-CVC/YOLO-World.

关键词： Object detection open-vocabulary object detection real-time object detection vision-language modeling vision-language pre-training YOLO

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Self-supervised Learning of Depth Inference for Multi-view Stereo

Self-supervised Learning of Depth Inference for Multi-view S...

引用

IEEE/CVF conference on computer vision and pattern recognition (CVPR)

作者： Yang, Jiayu Alvarez, Jose M. Liu, Miaomiao Australian Natl Univ Canberra ACT Australia NVIDIA Santa Clara CA USA

ISBN: (纸本)9781665445092

Recent supervised multi-view depth estimation networks have achieved promising results. Similar to all supervised approaches, these networks require ground-truth data during training. However, collecting a large amount of multi-view depth data is very challenging. Here, we propose a self-supervised learning framework for multi-view stereo that exploit pseudo labels from the input data. We start by learning to estimate depth maps as initial pseudo labels under an unsupervised learning framework relying on image reconstruction loss as supervision. We then refine the initial pseudo labels using a carefully designed pipeline leveraging depth information inferred from a higher resolution image and neighboring views. We use these high-quality pseudo labels as the supervision signal to train the network and improve, iteratively, its performance by self-training. Extensive experiments on the DTU dataset show that our proposed self-supervised learning framework outperforms existing unsupervised multi-view stereo networks by a large margin and performs on par compared to the supervised counterpart. Code is available at https://***/JiayuYANG/Self-supervised-CVP-MVSNet.

关键词： Training computer vision Image resolution Pipelines Supervised learning Estimation Data mining

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iCoseg: Interactive Co-segmentation with Intelligent Scribble Guidance

iCoseg: Interactive Co-segmentation with Intelligent Scribbl...

引用

23rd IEEE conference on computer vision and pattern recognition (CVPR)

作者： Batra, Dhruv Kowdle, Adarsh Parikh, Devi Luo, Jiebo Chen, Tsuhan Carnegie Mellon Univ Pittsburgh PA 15213 USA Cornell Univ Ithaca NY 14853 USA TTIC Chicago IL USA Eastman Kodak Co Rochester NY 14650 USA

ISBN: (纸本)9781424469840

This paper presents an algorithm for Interactive Co-segmentation of a foreground object from a group of related images. While previous approaches focus on unsupervised co-segmentation, we use successful ideas from the interactive object-cutout literature. We develop an algorithm that allows users to decide what foreground is, and then guide the output of the co-segmentation algorithm towards it via scribbles. Interestingly, keeping a user in the loop leads to simpler and highly parallelizable energy functions, allowing us to work with significantly more images per group. However, unlike the interactive single image counterpart, a user cannot be expected to exhaustively examine all cutouts (from tens of images) returned by the system to make corrections. Hence, we propose iCoseg, an automatic recommendation system that intelligently recommends where the user should scribble next. We introduce and make publicly available the largest co-segmentation dataset yet, the CMU-Cornell iCoseg Dataset, with 3 8 groups, 6 4 3 images, and pixelwise hand-annotated groundtruth. Through machine experiments and real user studies with our developed interface, we show that iCoseg can intelligently recommend regions to scribble on, and users following these recommendations can achieve good quality cutouts with significantly lower time and effort than exhaustively examining all cutouts.

关键词： computer vision

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Causally-Aware Intraoperative Imputation for Overall Survival Time Prediction

Causally-Aware Intraoperative Imputation for Overall Surviva...

引用

IEEE/CVF conference on computer vision and pattern recognition (CVPR)

作者： Li, Xiang Qian, Xuelin Liang, Litian Kong, Lingjie Dong, Qiaole Chen, Jiejun Liu, Dingxia Yao, Xiuzhong Fu, Yanwei Fudan Univ Sch Data Sci Shanghai Peoples R China Fudan Univ Zhongshan Hosp Dept Radiol Shanghai Peoples R China

ISBN: (纸本)9798350301298

Previous efforts in vision community are mostly made on learning good representations from visual patterns. Beyond this, this paper emphasizes the high-level ability of causal reasoning. We thus present a case study of solving the challenging task of Overall Survival (OS) time in primary liver cancers. Critically, the prediction of OS time at the early stage remains challenging, due to the unobvious image patterns of reflecting the OS. To this end, we propose a causal inference system by leveraging the intraoperative attributes and the correlation among them, as an intermediate supervision to bridge the gap between the images and the final OS. Particularly, we build a causal graph, and train the images to estimate the intraoperative attributes for final OS prediction. We present a novel Causally-aware Intraoperative Imputation Model (CAWIM) that can sequentially predict each attribute using its parent nodes in the estimated causal graph. To determine the causal directions, we propose a splitting-voting mechanism, which votes for the direction for each pair of adjacent nodes among multiple predictions obtained via causal discovery from heterogeneity. The practicability and effectiveness of our method are demonstrated by the promising results on liver cancer dataset of 361 patients with long-term observations.

关键词： cell microscopy Medical and biological vision

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Local Attention Pyramid for Scene Image Generation

Local Attention Pyramid for Scene Image Generation

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IEEE/CVF conference on computer vision and pattern recognition (CVPR)

作者： Shim, Sang-Heon Hyun, Sangeek Bae, DaeHyun Heo, Jae-Pil Sungkyunkwan Univ Seoul South Korea

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

In this paper, we first investigate the class-wise visual quality imbalance problem of scene images generated by GANs. The tendency is empirically found that the class-wise visual qualities are highly correlated with the dominance of object classes in the training data in terms of their scales and appearance frequencies. Specifically, the synthesized qualities of small and less frequent object classes tend to be low. To address this, we propose a novel attention module, Local Attention Pyramid (LAP) module tailored for scene image synthesis, that encourages GANs to generate diverse object classes in a high quality by explicit spread of high attention scores to local regions, since objects in scene images are scattered over the entire images. Moreover, our LAP assigns attention scores in a multiple scale to reflect the scale diversity of various objects. The experimental evaluations on three different datasets show consistent improvements in Frechet Inception Distance (FID) and Frechet Segmentation Distance (FSD) over the state-of-the-art baselines. Furthermore, we apply our LAP module to various GANs methods to demonstrate a wide applicability of our LAP module.

关键词： Measurement Deep learning Visualization Image segmentation computer vision Image analysis Image synthesis

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Accurate Few-shot Object Detection with Support-Query Mutual Guidance and Hybrid Loss

Accurate Few-shot Object Detection with Support-Query Mutual...

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IEEE/CVF conference on computer vision and pattern recognition (CVPR)

作者： Zhang, Lu Zhou, Shuigeng Guan, Jihong Zhang, Ji Fudan Univ Shanghai Key Lab Intelligent Informat Proc Shanghai Peoples R China Fudan Univ Sch Comp Sci Shanghai Peoples R China Tongji Univ Dept Comp Sci & Technol Shanghai Peoples R China Zhejiang Lab Hangzhou Peoples R China

ISBN: (纸本)9781665445092

Most object detection methods require huge amounts of annotated data and can detect only the categories that appear in the training set. However, in reality acquiring massive annotated training data is both expensive and time-consuming. In this paper, we propose a novel two-stage detector for accurate few-shot object detection. In the first stage, we employ a support-query mutual guidance mechanism to generate more support-relevant proposals. Concretely, on the one hand, a query-guided support weighting module is developed for aggregating different supports to generate the support feature. On the other hand, a support-guided query enhancement module is designed by dynamic kernels. In the second stage, we score and filter proposals via multi-level feature comparison between each proposal and the aggregated support feature based on a distance metric learnt by an effective hybrid loss, which makes the embedding space of distance metric more discriminative. Extensive experiments on benchmark datasets show that our method substantially outperforms the existing methods and lifts the SOTA of FSOD task to a higher level.

关键词： Measurement Training computer vision Training data Object detection Detectors pattern recognition

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Generalized Zero-Shot Learning via Synthesized Examples 31

Generalized Zero-Shot Learning via Synthesized Examples

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31st IEEE/CVF conference on computer vision and pattern recognition (CVPR)

作者： Verma, Vinay Kumar Arora, Gundeep Mishra, Ashish Rai, Piyush Indian Inst Technol Kanpur Kanpur Uttar Pradesh India Indian Inst Technol Madras Chennai India

ISBN: (纸本)9781538664209

We present a generative framework for generalized zero shot learning where the training and test classes are not necessarily disjoint. Built upon a variational autoencoder based architecture, consisting of a probabilistic encoder and a probabilistic conditional decoder, our model can generate novel exemplars from seen/unseen classes, given their respective class attributes. These exemplars can subsequently be used to train any off-the-shelf classification model. One of the key aspects of our encoder-decoder architecture is a feedback-driven mechanism in which a discriminator (a multivariate regressor) learns to map the generated exemplars to the corresponding class attribute vectors, leading to an improved generator. Our model's ability to generate and leverage examples from unseen classes to train the classification model naturally helps to mitigate the bias towards predicting seen classes in generalized zero shot learning settings. Through a comprehensive set of experiments, we show that our model outperforms several state-of-the-art methods, on several benchmark datasets, for both standard as well as generalized zero-shot learning.

关键词： Generators Training Predictive models computer architecture Probabilistic logic Decoding Image reconstruction

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Learning to Ask Informative Sub-Questions for Visual Question Answering

Learning to Ask Informative Sub-Questions for Visual Questio...

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IEEE/CVF conference on computer vision and pattern recognition (CVPR)

作者： Uehara, Kohei Duan, Nan Harada, Tatsuya Univ Tokyo Tokyo Japan Microsoft Res Asia Beijing Peoples R China Univ Tokyo RIKEN Tokyo Japan

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

VQA (Visual Question Answering) model tends to make incorrect inferences for questions that require reasoning over world knowledge. Recent study has shown that training VQA models with questions that provide lower-level perceptual information along with reasoning questions improves performance. Inspired by this, we propose a novel VQA model that generates questions to actively obtain auxiliary perceptual information useful for correct reasoning. Our model consists of a VQA model for answering questions, a Visual Question Generation (VQG) model for generating questions, and an Info-score model for estimating the amount of information the generated questions contain, which is useful in answering the original question. We train the VQG model to maximize the "informativeness" provided by the Info-score model to generate questions that contain as much information as possible, about the answer to the original question. Our experiments show that by in-putting the generated questions and their answers as additional information to the VQA model, it can indeed predict the answer more correctly than the baseline model.

关键词： Training Visualization computer vision Computational modeling conferences Reinforcement learning Predictive models

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Does Federated Dropout actually work?

Does Federated Dropout actually work?

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IEEE/CVF conference on computer vision and pattern recognition (CVPR)

作者： Cheng, Gary Charles, Zachary Garrett, Zachary Rush, Keith Stanford Univ Stanford CA 94305 USA Google Res Mountain View CA USA

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Model sizes are limited in Federated Learning due to network bandwidth and on-device memory constraints. The success of increasing model sizes in other machine learning domains motivates the development of methods for training large-scale models in Federated Learning. To this end, [3] draws inspiration from dropout and proposes Federated Dropout: an algorithm where clients train randomly selected subsets of a larger server model. Despite the promising empirical results and the many other works that build on it [1, 8, 13], we argue in this paper that the metrics used to measure the performance of Federated Dropout and its variants are misleading. We propose and perform new experiments which suggest that Federated Dropout is actually detrimental to scaling efforts. We show how a simple ensembling technique outperforms Federated Dropout and other baselines. We perform ablations that suggest that the best performing variations of Federated Dropout approximate ensembling. The simplicity of ensembling allows for easy, practical implementations. Furthermore, ensembling naturally leverages the parallelizable nature of Federated Learning-recall that it is easy to train several models independently because there are a lot of clients and server-compute is not the bottleneck. Ensembling's strong performance against our baselines suggests that Federated Learning models may be more easily scaled than previously thought with more sophisticated ensembling strategies e.g., via boosting.

关键词： Training Measurement computer vision Machine learning algorithms conferences Computational modeling Memory management

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