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检索条件"任意字段=2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2020"

共 11281 条记录，以下是691-700 订阅

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CLIP-Sculptor: Zero-Shot Generation of High-Fidelity and Diverse Shapes from Natural Language

CLIP-Sculptor: Zero-Shot Generation of High-Fidelity and Div...

引用

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

作者： Sanghi, Aditya Fu, Rao Liu, Vivian Willis, Karl D. D. Shayani, Hooman Khasahmadi, Amir H. Sridhar, Srinath Ritchie, Daniel Autodesk Res San Francisco CA 94105 USA Brown Univ Providence RI USA Columbia Univ New York NY USA

ISBN: (纸本)9798350301298

Recent works have demonstrated that natural language can be used to generate and edit 3D shapes. However, these methods generate shapes with limited fidelity and diversity. We introduce CLIP-Sculptor, a method to address these constraints by producing high-fidelity and diverse 3D shapes without the need for (text, shape) pairs during training. CLIP-Sculptor achieves this in a multi-resolution approach that first generates in a low-dimensional latent space and then upscales to a higher resolution for improved shape fidelity. For improved shape diversity, we use a discrete latent space which is modeled using a transformer conditioned on CLIP's image-text embedding space. We also present a novel variant of classifier-free guidance, which improves the accuracy-diversity trade-off. Finally, we perform extensive experiments demonstrating that CLIP-Sculptor outperforms state-of-the-art baselines.

关键词： vision + graphics

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Exploring the Benefits of vision Foundation Models for Unsupervised Domain Adaptation

Exploring the Benefits of Vision Foundation Models for Unsup...

引用

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

作者： Englert, Bruno B. Piva, Fabrizio J. Kerssies, Tommie de Geus, Daan Dubbelman, Gijs Eindhoven Univ Technol Eindhoven Netherlands

ISBN: (纸本)9798350365474

Achieving robust generalization across diverse data domains remains a significant challenge in computer vision. This challenge is important in safety-critical applications, where deep-neural-network-based systems must perform reliably under various environmental conditions not seen during training. Our study investigates whether the generalization capabilities of vision Foundation Models (VFMs) and Unsupervised Domain Adaptation (UDA) methods for the semantic segmentation task are complementary. Results show that combining VFMs with UDA has two main benefits: (a) it allows for better UDA performance while maintaining the out-of-distribution performance of VFMs, and (b) it makes certain time-consuming UDA components redundant, thus enabling significant inference speedups. Specifically, with equivalent model sizes, the resulting VFM-UDA method achieves an 8.4x speed increase over the prior non-VFM state of the art, while also improving performance by +1.2 mIoU in the UDA setting and by +6.1 mIoU in terms of out-of-distribution generalization. Moreover, when we use a VFM with 3.6x more parameters, the VFM-UDA approach maintains a 3.3x speed up, while improving the UDA performance by +3.1 mIoU and the out-of-distribution performance by +10.3 mIoU. These results underscore the significant benefits of combining VFMs with UDA, setting new standards and baselines for Unsupervised Domain Adaptation in semantic segmentation. The implementation is available at https://***/tue-mps/vfmuda.

关键词： foundation model generalization semantic segmentation unsupervised domain adaptation vision foundation model

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Iterative vision-and-Language Navigation

Iterative Vision-and-Language Navigation

引用

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

作者： Krantz, Jacob Banerjee, Shurjo Zhu, Wang Corso, Jason Anderson, Peter Lee, Stefan Thomason, Jesse Oregon State Univ Corvallis OR 97331 USA Univ Michigan Ann Arbor MI USA Univ Southern Calif Los Angeles CA USA Google Res Sunnyvale CA USA

ISBN: (纸本)9798350301298

We present Iterative vision-and-Language Navigation (IVLN), a paradigm for evaluating language-guided agents navigating in a persistent environment over time. Existing vision-and-Language Navigation (VLN) benchmarks erase the agent's memory at the beginning of every episode, testing the ability to perform cold-start navigation with no prior information. However, deployed robots occupy the same environment for long periods of time. The IVLN paradigm addresses this disparity by training and evaluating VLN agents that maintain memory across tours of scenes that consist of up to 100 ordered instruction-following Room-to-Room (R2R) episodes, each defined by an individual language instruction and a target path. We present discrete and continuous Iterative Room-to-Room (IR2R) benchmarks comprising about 400 tours each in 80 indoor scenes. We find that extending the implicit memory of high-performing transformer VLN agents is not sufficient for IVLN, but agents that build maps can benefit from environment persistence, motivating a renewed focus on map-building agents in VLN.

关键词： language reasoning vision

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PolyFormer: Referring Image Segmentation as Sequential Polygon Generation

PolyFormer: Referring Image Segmentation as Sequential Polyg...

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

作者： Liu, Jiang Ding, Hui Cai, Zhaowei Zhang, Yuting Satzoda, Ravi Kumar Mahadevan, Vijay Manmatha, R. Johns Hopkins Univ Baltimore MD 21218 USA AWS AI Labs Pasadena CA USA

ISBN: (纸本)9798350301298

In this work, instead of directly predicting the pixel-level segmentation masks, the problem of referring image segmentation is formulated as sequential polygon generation, and the predicted polygons can be later converted into segmentation masks. This is enabled by a new sequence-to-sequence framework, Polygon Transformer (PolyFormer), which takes a sequence of image patches and text query tokens as input, and outputs a sequence of polygon vertices autoregressively. For more accurate geometric localization, we propose a regression-based decoder, which predicts the precise floating-point coordinates directly, without any coordinate quantization error. In the experiments, PolyFormer outperforms the prior art by a clear margin, e.g., 5.40% and 4.52% absolute improvements on the challenging RefCOCO+ and RefCOCOg datasets. It also shows strong generalization ability when evaluated on the referring video segmentation task without fine-tuning, e.g., achieving competitive 61.5% J&F on the Ref-DAVIS17 dataset.

关键词： and reasoning language vision

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AnyFlow: Arbitrary Scale Optical Flow with Implicit Neural Representation

AnyFlow: Arbitrary Scale Optical Flow with Implicit Neural R...

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

作者： Jung, Hyunyoung Hui, Zhuo Luo, Lei Yang, Haitao Liu, Feng Yoo, Sungjoo Ranjan, Rakesh Demandolx, Denis Seoul Natl Univ Seoul South Korea Meta Real Labs Seoul South Korea

ISBN: (纸本)9798350301298

To apply optical flow in practice, it is often necessary to resize the input to smaller dimensions in order to reduce computational costs. However, downsizing inputs makes the estimation more challenging because objects and motion ranges become smaller. Even though recent approaches have demonstrated high-quality flow estimation, they tend to fail to accurately model small objects and precise boundaries when the input resolution is lowered, restricting their applicability to high-resolution inputs. In this paper, we introduce AnyFlow, a robust network that estimates accurate flow from images of various resolutions. By representing optical flow as a continuous coordinate-based representation, AnyFlow generates outputs at arbitrary scales from low-resolution inputs, demonstrating superior performance over prior works in capturing tiny objects with detail preservation on a wide range of scenes. We establish a new state-of-the-art performance of cross-dataset generalization on the KITTI dataset, while achieving comparable accuracy on the online benchmarks to other SOTA methods.

关键词： Low-level vision

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Architectural Backdoors in Neural Networks

Architectural Backdoors in Neural Networks

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

作者： Bober-Irizar, Mikel Shumailov, Ilia Zhao, Yiren Mullins, Robert Papernot, Nicolas Univ Cambridge Cambridge England Vector Inst Toronto ON Canada Univ Oxford Oxford England Imperial Coll London London England Univ Toronto Toronto ON Canada

ISBN: (纸本)9798350301298

Machine learning is vulnerable to adversarial manipulation. Previous literature demonstrated that at the training stage attackers can manipulate data [14] and data sampling procedures [29] to control model behaviour. A common attack goal is to plant backdoors i.e. force the victim model to learn to recognise a trigger known only by the adversary. In this paper, we introduce a new class of backdoor attacks that hide inside model architectures i.e. in the inductive bias of the functions used to train. These backdoors are simple to implement, for instance by publishing open-source code for a backdoored model architecture that others will reuse unknowingly. We demonstrate that model architectural backdoors represent a real threat and, unlike other approaches, can survive a complete re-training from scratch. We formalise the main construction principles behind architectural backdoors, such as a connection between the input and the output, and describe some possible protections against them. We evaluate our attacks on computer vision benchmarks of different scales and demonstrate the underlying vulnerability is pervasive in a variety of common training settings.

关键词： accountability ethics in vision fairness privacy Transparency

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Rethinking Feature-based Knowledge Distillation for Face recognition

Rethinking Feature-based Knowledge Distillation for Face Rec...

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

作者： Li, Jingzhi Guo, Zidong Li, Hui Han, Seungju Baek, Ji-won Yang, Min Yang, Ran Suh, Sungjoo Samsung R&D Inst China Xian SRCX Xian Peoples R China Samsung Adv Inst Technol SAIT Suwon South Korea

ISBN: (纸本)9798350301298

With the continual expansion of face datasets, feature-based distillation prevails for large-scale face recognition. In this work, we attempt to remove identity supervision in student training, to spare the GPU memory from saving massive class centers. However, this naive removal leads to inferior distillation result. We carefully inspect the performance degradation from the perspective of intrinsic dimension, and argue that the gap in intrinsic dimension, namely the intrinsic gap, is intimately connected to the infamous capacity gap problem. By constraining the teacher's search space with reverse distillation, we narrow the intrinsic gap and unleash the potential of feature-only distillation. Remarkably, the proposed reverse distillation creates universally student-friendly teacher that demonstrates outstanding student improvement. We further enhance its effectiveness by designing a student proxy to better bridge the intrinsic gap. As a result, the proposed method surpasses state-of-the-art distillation techniques with identity supervision on various face recognition benchmarks, and the improvements are consistent across different teacher-student pairs.

关键词： body gesture Humans: Face movement pose

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Topology-Guided Multi-Class Cell Context Generation for Digital Pathology

Topology-Guided Multi-Class Cell Context Generation for Digi...

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

作者： Ahousamra, Shahira Gupta, Rajarsi Kurc, Tahsin Samaras, Dimitris Saltz, Joel Chen, Chao SUNY Stony Brook Dept Comp Sci Stony Brook NY 11790 USA SUNY Stony Brook Dept Biomed Informat Stony Brook NY USA

ISBN: (纸本)9798350301298

In digital pathology, the spatial context of cells is important for cell classification, cancer diagnosis and prognosis. To model such complex cell context, however, is challenging. Cells form different mixtures, lineages, clusters and holes. To model such structural patterns in a learnable fashion, we introduce several mathematical tools from spatial statistics and topological data analysis. We incorporate such structural descriptors into a deep generative model as both conditional inputs and a differentiable loss. This way, we are able to generate high quality multi-class cell layouts for the first time. We show that the topology-rich cell layouts can be used for data augmentation and improve the performance of downstream tasks such as cell classification.

关键词： cell microscopy Medical and biological vision

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1% VS 100%: Parameter-Efficient Low Rank Adapter for Dense Predictions

1% VS 100%: Parameter-Efficient Low Rank Adapter for Dense P...

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

作者： Yin, Dongshuo Yang, Yiran Wang, Zhechao Yu, Hongfeng Wei, Kaiwen Sun, Xian Chinese Acad Sci Aerosp Informat Res Inst Key Lab Network Informat Syst Technol Beijing Peoples R China Univ Chinese Acad Sci Sch Elect Elect & Commun Engn Beijing Peoples R China

ISBN: (纸本)9798350301298

Fine-tuning large-scale pre-trained vision models to downstream tasks is a standard technique for achieving state-of-the-art performance on computer vision benchmarks. However, fine-tuning the whole model with millions of parameters is inefficient as it requires storing a same-sized new model copy for each task. In this work, we propose LoRand, a method for fine-tuning large-scale vision models with a better trade-off between task performance and the number of trainable parameters. LoRand generates tiny adapter structures with low-rank synthesis while keeping the original backbone parameters fixed, resulting in high parameter sharing. To demonstrate LoRand's effectiveness, we implement extensive experiments on object detection, semantic segmentation, and instance segmentation tasks. By only training a small percentage (1% to 3%) of the pre-trained backbone parameters, LoRand achieves comparable performance to standard fine-tuning on COCO and ADE20K and outperforms fine-tuning in low-resource PASCAL VOC dataset.

关键词： Efficient and scalable vision

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DVMSR: Distillated vision Mamba for Efficient Super-Resolution

DVMSR: Distillated Vision Mamba for Efficient Super-Resoluti...

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

作者： Lei, Xiaoyan Zhang, Wenlong Cao, Weifeng Zhengzhou Univ Light Ind Zhengzhou Peoples R China HongKong Polytech Univ Hong Kong Peoples R China

ISBN: (纸本)9798350365474

Efficient Image Super-Resolution (SR) aims to accelerate SR network inference by minimizing computational complexity and network parameters while preserving performance. Existing state-of-the-art Efficient Image Super-Resolution methods are based on convolutional neural networks. Few attempts have been made with Mamba to harness its long-range modeling capability and efficient computational complexity, which have shown impressive performance on high-level vision tasks. In this paper, we propose DVMSR, a novel lightweight Image SR network that incorporates vision Mamba and a distillation strategy. The network of DVMSR consists of three modules: feature extraction convolution, multiple stacked Residual State Space Blocks (RSSBs), and a reconstruction module. Specifically, the deep feature extraction module is composed of several residual state space blocks (RSSB), each of which has several vision Mamba Moudles(ViMM) together with a residual connection. To achieve efficiency improvement while maintaining comparable performance, we employ a distillation strategy to the vision Mamba network for superior performance. Specifically, we leverage the rich representation knowledge of teacher network as additional supervision for the output of lightweight student networks. Extensive experiments have demonstrated that our proposed DVMSR can outperform state-of-the-art efficient SR methods in terms of model parameters while maintaining the performance of both PSNR and SSIM. The source code is available at https://***/nathan66666/***

关键词： Efficient Image Super-Resolution vision Mamba

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