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Bootstrapping SparseFormers from vision Foundation Models

Bootstrapping SparseFormers from Vision Foundation Models

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

作者： Gao, Ziteng Tong, Zhan Lin, Kevin Qinghong Chen, Joya Shou, Mike Zheng Natl Univ Singapore Show Lab Singapore Singapore

ISBN: (纸本)9798350353006

The recently proposed SparseFormer architecture provides an alternative approach to visual understanding by utilizing a significantly lower number of visual tokens via adjusting RoIs, greatly reducing computational costs while still achieving promising performance. However, training SparseFormers from scratch is still expensive, and scal-ing up the number of parameters can be challenging. In this paper, we propose to bootstrap SparseFormers from ViT-based vision foundation models in a simple and efficient way. Since the majority of SparseFormer blocks are the standard transformer ones, we can inherit weights from large-scale pre-trained vision transformers and freeze them as much as possible. Therefore, we only need to train the SparseFormer-specific lightweight focusing transformer to adjust token RoIs and fine-tune a few early pre-trained blocks to align the final token representation. In such a way, we can bootstrap SparseFormer architectures from various large-scale pre-trained models (e.g., IN-21K pre-trained AugRegs or CLIPs) using a rather smaller amount of training samples (e.g., IN-1K) and without labels or captions within just a few hours. As a result, the bootstrapped unimodal SparseFormer (from AugReg-ViT-L/16-384) can reach 84.9% accuracy on IN-1K with only 49 tokens, and the multimodal SparseFormer from CLIPs also demonstrates notable zero-shot performance with highly reduced computational cost without seeing any caption during the bootstrapping procedure. In addition, CLIP-bootstrapped SparseFormers, which align the output space with language without seeing a word, can serve as efficient vision encoders in multimodal large language models. Code and models are available at https://***/showlab/sparseformer

关键词： Cost reduction

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Learning Compositional Representation for 4D Captures with Neural ODE

Learning Compositional Representation for 4D Captures with N...

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

作者： Jiang, Boyan Zhang, Yinda Wei, Xingkui Xue, Xiangyang Fu, Yanwei Fudan Univ Sch Comp Sci Shanghai Peoples R China Fudan Univ Shanghai Peoples R China Google Mountain View CA 94043 USA Fudan Univ Sch Data Sci MOE Frontiers Ctr Brain Sci Shanghai Peoples R China Fudan Univ Shanghai Key Lab Intelligent Informat Proc Shanghai Peoples R China

ISBN: (纸本)9781665445092

Learning based representation has become the key to the success of many computer vision systems. While many 3D representations have been proposed, it is still an unaddressed problem how to represent a dynamically changing 3D object. In this paper, we introduce a compositional representation for 4D captures, i.e. a deforming 3D object over a temporal span, that disentangles shape, initial state, and motion respectively. Each component is represented by a latent code via a trained encoder. To model the motion, a neural Ordinary Differential Equation (ODE) is trained to update the initial state conditioned on the learned motion code, and a decoder takes the shape code and the updated state code to reconstruct the 3D model at each time stamp. lb this end, we propose an Identity Exchange Training (IET) strategy to encourage the network to learn effectively decoupling each component. Extensive experiments demonstrate that the proposed method outperforms existing state-of-the-art deep learning based methods on 4D reconstruction, and significantly improves on various tasks, including motion transfer and completion.

关键词： Training Geometry Solid modeling computer vision Three-dimensional displays Codes Shape

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Pseudo-IoU: Improving Label Assignment in Anchor-Free Object Detection

Pseudo-IoU: Improving Label Assignment in Anchor-Free Object...

引用

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

作者： Li, Jiachen Cheng, Bowen Feris, Rogerio Xiong, Jinjun Huang, Thomas S. Hwu, Wen-Mei Shi, Humphrey UIUC Champaign IL 61820 USA MIT IBM Watson AI Lab Cambridge MA USA IBM TJ Watson Res Ctr Ossining NY USA NVIDIA Santa Clara CA USA Univ Oregon Eugene OR 97403 USA Picsart AI Res PAIR Champaign IL USA

ISBN: (纸本)9781665448994

Current anchor-free object detectors are quite simple and effective yet lack accurate label assignment methods, which limits their potential in competing with classic anchor-based models that are supported by well-designed assignment methods based on the Intersection-over-Union (IoU) metric. In this paper, we present Pseudo-Intersection-over-Union (Pseudo-IoU): a simple metric that brings more standardized and accurate assignment rule into anchor-free object detection frameworks without any additional computational cost or extra parameters for training and testing, making it possible to further improve anchor-free object detection by utilizing training samples of good quality under effective assignment rules that have been previously applied in anchor-based methods. By incorporating Pseudo-IoU metric into an end-to-end single-stage anchor-free object detection framework, we observe consistent improvements in their performance on general object detection benchmarks such as PASCAL VOC and MSCOCO. Our method (single-model and single-scale) also achieves comparable performance to other recent state-of-the-art anchor-free methods without bells and whistles. Our code is based on mmdetection toolbox and will be made publicly available at https://***/SHILabs/Pseudo-IoU-for-Anchor-Free-Object-Detection.

关键词： Measurement Training Location awareness computer vision Computational modeling conferences Object detection

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

作者： Zhang, Hongrun Burrows, Liam Meng, Yanda Sculthorpe, Declan Mukherjee, Abhik Coupland, Sarah E. Chen, Ke Zheng, Yalin Univ Liverpool Dept Eye & Vis Sci Liverpool Merseyside England Univ Liverpool Dept Math Sci Liverpool Merseyside England Univ Liverpool Ctr Math Imaging Techn Liverpool Merseyside England Univ Nottingham Biodiscovery Inst Sch Med Nottingham England Univ Liverpool Inst Syst Mol & Integrat Biol Liverpool Merseyside England

ISBN: (纸本)9798350301298

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.

关键词： cell microscopy Medical and biological vision

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Deep Normalized Cross-Modal Hashing with Bi-Direction Relation Reasoning

Deep Normalized Cross-Modal Hashing with Bi-Direction Relati...

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

作者： Sun, Changchang Latapie, Hugo Liu, Gaowen Yan, Yan IIT Dept Comp Sci Chicago IL 60616 USA Cisco Res Emerging Technol & Incubat San Jose CA USA

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Due to the continuous growth of large-scale multi-modal data and increasing requirements for retrieval speed, deep cross-modal hashing has gained increasing attention recently. Most of existing studies take a similarity matrix as supervision to optimize their models, and the inner product between continuous surrogates of hash codes is utilized to depict the similarity in the Hamming space. However, all of them merely consider the relevant information to build the similarity matrix, ignoring the contribution of the irrelevant one, i.e., the categories that samples do not belong to. Therefore, they cannot effectively alleviate the effect of dissimilar samples. Moreover, due to the modality distribution difference, directly utilizing continuous surrogates of hash codes to calculate similarity may induce suboptimal retrieval performance. To tackle these issues, in this paper, we propose a novel deep normalized cross-modal hashing scheme with bi-direction relation reasoning, named Bi NCMH. Specifically, we build the multi-level semantic similarity matrix by considering bi-direction relation, i.e., consistent and inconsistent relation. It hence can holistically characterize relations among instances. Besides, we execute feature normalization on continuous surrogates of hash codes to eliminate the deviation caused by modality gap, which further reduces the negative impact of binarization on retrieval performance. Extensive experiments on two cross-modal benchmark datasets demonstrate the superiority of our model over several state-of-the-art baselines.

关键词： computer vision Codes conferences Computational modeling Semantics Bidirectional control Benchmark testing

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Memory Oriented Transfer Learning for Semi-Supervised Image Deraining

Memory Oriented Transfer Learning for Semi-Supervised Image ...

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

作者： Huang, Huaibo Yu, Aijing He, Ran Univ Chinese Acad Sci Ctr Excellence Brain Sci & Intelligence Technol Ctr Res Intelligent Percept & Comp Natl Lab Pattern RecognitCASIACAS Beijing Peoples R China

ISBN: (纸本)9781665445092

Deep learning based methods have shown dramatic improvements in image rain removal by using large-scale paired data of synthetic datasets. However, due to the various appearances of real rain streaks that may differ from those in the synthetic training data, it is challenging to directly extend existing methods to the real-world scenes. To address this issue, we propose a memory-oriented semi-supervised (MOSS) method which enables the network to explore and exploit the properties of rain streaks from both synthetic and real data. The key aspect of our method is designing an encoder-decoder neural network that is augmented with a self-supervised memory module, where items in the memory record the prototypical patterns of rain degradations and are updated in a self-supervised way. Consequently, the rainy styles can be comprehensively derived from synthetic or real-world degraded images with- out the need for clean labels. Furthermore, we present a self-training mechanism that attempts to transfer deraining knowledge from supervised rain removal to unsupervised cases. An additional target network, which is updated with an exponential moving average of the online deraining network, is utilized to produce pseudo-labels for unlabeled rainy images. Meanwhile, the deraining network is optimized with supervised objectives on both synthetic paired data and pseudo-paired noisy data. Extensive experiments show that the proposed method achieves more appealing results not only on limited labeled data but also on unlabeled real-world images than recent state-of-the-art methods.

关键词： Degradation Rain Transfer learning Neural networks Training data Memory modules pattern recognition

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Towards Noiseless Object Contours for Weakly Supervised Semantic Segmentation

Towards Noiseless Object Contours for Weakly Supervised Sema...

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

作者： Li, Jing Fan, Junsong Zhang, Zhaoxiang Chinese Acad Sci CASIA Inst Automat Beijing Peoples R China Univ Chinese Acad Sci UCAS Beijing Peoples R China HKISI CAS Ctr Artificial Intelligence & Robot Beijing Peoples R China Natl Lab Pattern Recognit NLPR Beijing Peoples R China

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Image-level label based weakly supervised semantic segmentation has attracted much attention since image labels are very easy to obtain. Existing methods usually generate pseudo labels from class activation map (CAM) and then train a segmentation model. CAM usually highlights partial objects and produce incomplete pseudo labels. Some methods explore object contour by training a contour model with CAM seed label supervision and then propagate CAM score from discriminative regions to non-discriminative regions with contour guidance. The propagation process suffers from the noisy intra-object contours, and inadequate propagation results produce incomplete pseudo labels. This is because the coarse CAM seed label lacks sufficient precise semantic information to suppress contour noise. In this paper, we train a SANCE model which utilizes an auxiliary segmentation module to supplement high-level semantic information for contour training by backbone feature sharing and online label supervision. The auxiliary segmentation module also provides more accurate localization map than CAM for pseudo label generation. We evaluate our approach on Pascal VOC 2012 and MS COCO 2014 benchmarks and achieve state-of-the-art performance, demonstrating the effectiveness of our method. The source code can be found at https://***/BraveGroup/SANCE

关键词： Training Location awareness Image segmentation computer vision Semantics Refining Predictive models

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Robust and Accurate Superquadric Recovery: a Probabilistic Approach

Robust and Accurate Superquadric Recovery: a Probabilistic A...

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

作者： Liu, Weixiao Wu, Yuwei Ruan, Sipu Chirikjian, Gregory S. Natl Univ Singapore Singapore Singapore Johns Hopkins Univ Baltimore MD 21218 USA

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Interpreting objects with basic geometric primitives has long been studied in computer vision. Among geometric primitives, superquadrics are well known for their ability to represent a wide range of shapes with few parameters. However, as the first and foremost step, recovering superquadrics accurately and robustly from 3D data still remains challenging. The existing methods are subject to local optima and sensitive to noise and outliers in real-world scenarios, resulting in frequent failure in capturing geometric shapes. In this paper, we propose the first probabilistic method to recover superquadrics from point clouds. Our method builds a Gaussian-uniform mixture model (GUM) on the parametric surface of a superquadric, which explicitly models the generation of outliers and noise. The superquadric recovery is formulated as a Maximum Likelihood Estimation (MLE) problem. We propose an algorithm, Expectation, Maximization, and Switching (EMS), to solve this problem, where: (1) outliers are predicted from the posterior perspective;(2) the superquadric parameter is optimized by the trust-region reflective algorithm;and (3) local optima are avoided by globally searching and switching among parameters encoding similar superquadrics. We show that our method can be extended to the multi-superquadrics recovery for complex objects. The proposed method outperforms the state-of-the-art in terms of accuracy, efficiency, and robustness on both synthetic and real world datasets. The code is at http://***/bmlklwx/EMS-superquadric_***.

关键词： Point cloud compression Maximum likelihood estimation computer vision Three-dimensional displays Shape Switches Probabilistic logic

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Label Relation Graphs Enhanced Hierarchical Residual Network for Hierarchical Multi-Granularity Classification

Label Relation Graphs Enhanced Hierarchical Residual Network...

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

作者： Chen, Jingzhou Wang, Peng Liu, Jian Qian, Yuntao Zhejiang Univ Coll Comp Sci Hangzhou Zhejiang Peoples R China Ant Financial Serv Grp Hangzhou Zhejiang Peoples R China

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Hierarchical multi-granularity classification (HMC) assigns hierarchical multi-granularity labels to each object and focuses on encoding the label hierarchy, e.g., ["Albatross", "Laysan Albatross"] from coarse-to-fine levels. However, the definition of what is fine-grained is subjective, and the image quality may affect the identification. Thus, samples could be observed at any level of the hierarchy, e.g., ["Albatross"] or ["Albatross", "Laysan Albatross"], and examples discerned at coarse categories are often neglected in the conventional setting of HMC. In this paper, we study the HMC problem in which objects are labeled at any level of the hierarchy. The essential designs of the proposed method are derived from two motivations: (1) learning with objects labeled at various levels should transfer hierarchical knowledge between levels;(2) lower-level classes should inherit attributes related to upper-level superclasses. The proposed combinatorial loss maximizes the marginal probability of the observed ground truth label by aggregating information from related labels defined in the tree hierarchy. If the observed label is at the leaf level, the combinatorial loss further imposes the multi-class cross-entropy loss to increase the weight of fine-grained classification loss. Considering the hierarchical feature interaction, we propose a hierarchical residual network (HRN), in which granularity-specific features from parent levels acting as residual connections are added to features of children levels. Experiments on three commonly used datasets demonstrate the effectiveness of our approach compared to the state-of-the-art HMC approaches. The code will be available at https://***/MonsterZhZh/HRN.

关键词： Image quality Representation learning Knowledge engineering computer vision Machine vision Semantics Probabilistic logic

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One-Shot Neural Ensemble Architecture Search by Diversity-Guided Search Space Shrinking

One-Shot Neural Ensemble Architecture Search by Diversity-Gu...

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

作者： Chen, Minghao Fu, Jianlong Ling, Haibin SUNY Stony Brook Stony Brook NY 11794 USA Microsoft Res Asia Beijing Peoples R China Microsoft Beijing Peoples R China

ISBN: (纸本)9781665445092

Despite remarkable progress achieved, most neural architecture search (NAS) methods focus on searching for one single accurate and robust architecture. To further build models with better generalization capability and performance, model ensemble is usually adopted and performs better than stand-alone models. Inspired by the merits of model ensemble, we propose to search for multiple diverse models simultaneously as an alternative way to find powerful models. Searching for ensembles is non-trivial and has two key challenges: enlarged search space and potentially more complexity for the searched model. In this paper, we propose a one-shot neural ensemble architecture search (NEAS) solution that addresses the two challenges. For the first challenge, we introduce a novel diversity-based metric to guide search space shrinking, considering both the potentiality and diversity of candidate operators. For the second challenge, we enable a new search dimension to learn layer sharing among different models for efficiency purposes. The experiments on ImageNet clearly demonstrate that our solution can improve the supernet's capacity of ranking ensemble architectures, and further lead to better search results. The discovered architectures achieve superior performance compared with state-of-the-arts such as MobileNetV3 and EfficientNet families under aligned settings. Moreover, we evaluate the generalization ability and robustness of our searched architecture on the COCO detection benchmark and achieve a 3.1% improvement on AP compared with MobileNetV3. Codes and models are available here.

关键词： computer vision Codes computer architecture Benchmark testing Extraterrestrial measurements Robustness Complexity theory

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