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检索条件"任意字段=2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2023"

共 11753 条记录，以下是121-130 订阅

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AVFormer: Injecting vision into Frozen Speech Models for Zero-Shot AV-ASR

AVFormer: Injecting Vision into Frozen Speech Models for Zer...

引用

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

作者： Seo, Paul Hongsuck Nagrani, Arsha Schmid, Cordelia Google Res Mountain View CA 94043 USA

ISBN: (纸本)9798350301298

Audiovisual automatic speech recognition (AV-ASR) aims to improve the robustness of a speech recognition system by incorporating visual information. Training fully supervised multimodal models for this task from scratch, however is limited by the need for large labelled audiovisual datasets (in each downstream domain of interest). We present AVFormer, a simple method for augmenting audio-only models with visual information, at the same time performing lightweight domain adaptation. We do this by (i) injecting visual embeddings into a frozen ASR model using lightweight trainable adaptors. We show that these can be trained on a small amount of weakly labelled video data with minimum additional training time and parameters. (ii) We also introduce a simple curriculum scheme during training which we show is crucial to enable the model to jointly process audio and visual information effectively;and finally (iii) we show that our model achieves state of the art zero-shot results on three different AV-ASR benchmarks (How2, VisSpeech and Ego4D), while also crucially preserving decent performance on traditional audio-only speech recognition benchmarks (LibriSpeech). Qualitative results show that our model effectively leverages visual information for robust speech recognition.

关键词： Multi-modal learning

来源：评论

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Image as a Foreign Language: BEIT Pretraining for vision and vision-Language Tasks

Image as a Foreign Language: BEIT Pretraining for Vision and...

引用

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

作者： Wang, Wenhui Bao, Hangbo Dong, Li Bjorck, Johan Peng, Zhiliang Liu, Qiang Aggarwal, Kriti Mohammed, Owais Khan Singhal, Saksham Som, Subhojit Wei, Furu Microsoft Corp Redmond WA 98052 USA

ISBN: (纸本)9798350301298

A big convergence of language, vision, and multimodal pretraining is emerging. In this work, we introduce a general-purpose multimodal foundation model BEIT-3, which achieves excellent transfer performance on both vision and vision-language tasks. Specifically, we advance the big convergence from three aspects: backbone architecture, pretraining task, and model scaling up. We use Multiway Transformers for general-purpose modeling, where the modular architecture enables both deep fusion and modality-specific encoding. Based on the shared backbone, we perform masked "language" modeling on images (Imglish), texts (English), and image-text pairs ("parallel sentences") in a unified manner. Experimental results show that BEIT-3 obtains remarkable performance on object detection (COCO), semantic segmentation (ADE20K), image classification (ImageNet), visual reasoning (NLVR2), visual question answering (VQAv2), image captioning (COCO), and cross-modal retrieval (Flickr30K, COCO).

关键词： language reasoning vision

来源：评论

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Directional Connectivity-based Segmentation of Medical Images

Directional Connectivity-based Segmentation of Medical Image...

引用

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

作者： Yang, Ziyun Farsiu, Sina Duke Univ Durham NC 27706 USA

ISBN: (纸本)9798350301298

Anatomical consistency in biomarker segmentation is crucial for many medical image analysis tasks. A promising paradigm for achieving anatomically consistent segmentation via deep networks is incorporating pixel connectivity, a basic concept in digital topology, to model inter-pixel relationships. However, previous works on connectivity modeling have ignored the rich channel-wise directional information in the latent space. In this work, we demonstrate that effective disentanglement of directional sub-space from the shared latent space can significantly enhance the feature representation in the connectivity-based network. To this end, we propose a directional connectivity modeling scheme for segmentation that decouples, tracks, and utilizes the directional information across the network. Experiments on various public medical image segmentation benchmarks show the effectiveness of our model as compared to the state-of-the-art methods. Code is available at https://***/Zyun-Y/DconnNet.

关键词： cell microscopy Medical and biological vision

来源：评论

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Pseudo-label Guided Contrastive Learning for Semi-supervised Medical Image Segmentation

Pseudo-label Guided Contrastive Learning for Semi-supervised...

引用

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

作者： Basak, Hritam Yin, Zhaozheng SUNY Stony Brook Stony Brook NY 11794 USA

ISBN: (纸本)9798350301298

Although recent works in semi-supervised learning (SemiSL) have accomplished significant success in natural image segmentation, the task of learning discriminative representations from limited annotations has been an open problem in medical images. Contrastive Learning (CL) frameworks use the notion of similarity measure which is useful for classification problems, however, they fail to transfer these quality representations for accurate pixel-level segmentation. To this end, we propose a novel semi-supervised patch-based CL framework for medical image segmentation without using any explicit pretext task. We harness the power of both CL and SemiSL, where the pseudo-labels generated from SemiSL aid CL by providing additional guidance, whereas discriminative class information learned in CL leads to accurate multi-class segmentation. Additionally, we formulate a novel loss that synergistically encourages inter-class separability and intraclass compactness among the learned representations. A new inter-patch semantic disparity mapping using average patch entropy is employed for a guided sampling of positives and negatives in the proposed CL framework. Experimental analysis on three publicly available datasets of multiple modalities reveals the superiority of our proposed method as compared to the state-of-the-art methods. Code is available at: GitHub.

关键词： cell microscopy Medical and biological vision

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Universal Guidance for Diffusion Models

Universal Guidance for Diffusion Models

引用

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

作者： Bansal, Arpit Chu, Hong-Min Schwarzschild, Avi Sengupta, Soumyadip Goldblum, Micah Geiping, Jonas Goldstein, Tom Univ Maryland College Pk MD 20742 USA Univ North Carolina Chapel Hill Chapel Hill NC USA NYU New York NY USA

ISBN: (纸本)9798350302493

Typical diffusion models are trained to accept a particular form of conditioning, most commonly text, and cannot be conditioned on other modalities without retraining. In this work, we propose a universal guidance algorithm that enables diffusion models to be controlled by arbitrary guidance modalities without the need to retrain any use-specific components. We show that our algorithm successfully generates quality images with guidance functions including segmentation, face recognition, object detection, and classifier signals. Code is available at ***/arpitbansal297/UniversalGuided-Diffusion.

关键词： Face recognition

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Toward Accurate Post-Training Quantization for Image Super Resolution

Toward Accurate Post-Training Quantization for Image Super R...

引用

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

作者： Tu, Zhijun Hu, Jie Chen, Hanting Wang, Yunhe Huawei Noahs Ark Lab Montreal PQ Canada

ISBN: (纸本)9798350301298

Model quantization is a crucial step for deploying super resolution (SR) networks on mobile devices. However, existing works focus on quantization-aware training, which requires complete dataset and expensive computational overhead. In this paper, we study post-training quantization (PTQ) for image super resolution using only a few unlabeled calibration images. As the SR model aims to maintain the texture and color information of input images, the distribution of activations are long-tailed, asymmetric and highly dynamic compared with classification models. To this end, we introduce the density-based dual clipping to cut off the outliers based on analyzing the asymmetric bounds of activations. Moreover, we present a novel pixel aware calibration method with the supervision of the full-precision model to accommodate the highly dynamic range of different samples. Extensive experiments demonstrate that the proposed method significantly outperforms existing PTQ algorithms on various models and datasets. For instance, we get a 2.091 dB increase on Urban100 benchmark when quantizing EDSRx4 to 4-bit with 100 unlabeled images. Our code is available at both PyTorch and MindSpore.

关键词： Low-level vision

来源：评论

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PolyFormer: Referring Image Segmentation as Sequential Polygon Generation

PolyFormer: Referring Image Segmentation as Sequential Polyg...

引用

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...

引用

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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LVQAC: Lattice Vector Quantization Coupled with Spatially Adaptive Companding for Efficient Learned Image Compression

LVQAC: Lattice Vector Quantization Coupled with Spatially Ad...

引用

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

作者： Zhang, Xi Wu, Xiaolin Shanghai Jiao Tong Univ Shanghai Peoples R China McMaster Univ Hamilton ON Canada

ISBN: (纸本)9798350301298

Recently, numerous end-to-end optimized image compression neural networks have been developed and proved themselves as leaders in rate-distortion performance. The main strength of these learnt compression methods is in powerful nonlinear analysis and synthesis transforms that can be facilitated by deep neural networks. However, out of operational expediency, most of these end-to-end methods adopt uniform scalar quantizers rather than vector quantizers, which are information-theoretically optimal. In this paper, we present a novel Lattice Vector Quantization scheme coupled with a spatially Adaptive Companding (LVQAC) mapping. LVQ can better exploit the inter-feature dependencies than scalar uniform quantization while being computationally almost as simple as the latter. Moreover, to improve the adaptability of LVQ to source statistics, we couple a spatially adaptive companding (AC) mapping with LVQ. The resulting LVQAC design can be easily embedded into any end-to-end optimized image compression system. Extensive experiments demonstrate that for any end-to-end CNN image compression models, replacing uniform quantizer by LVQAC achieves better rate-distortion performance without significantly increasing the model complexity.

关键词： Low-level vision

来源：评论

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MetaCLUE: Towards Comprehensive Visual Metaphors Research

MetaCLUE: Towards Comprehensive Visual Metaphors Research

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

作者： Akula, Arjun R. Driscoll, Brendan Narayana, Pradyumna Changpinyo, Soravit Jia, Zhiwei Damle, Suyash Pruthi, Garima Basu, Sugato Guibas, Leonidas Freeman, William T. Li, Yuanzhen Jampani, Varun Google Mountain View CA 94043 USA

ISBN: (纸本)9798350301298

Creativity is an indispensable part of human cognition and also an inherent part of how we make sense of the world. Metaphorical abstraction is fundamental in communicating creative ideas through nuanced relationships between abstract concepts such as feelings. While computer vision benchmarks and approaches predominantly focus on understanding and generating literal interpretations of images, metaphorical comprehension of images remains relatively unexplored. Towards this goal, we introduce MetaCLUE, a set of vision tasks on visual metaphor. We also collect high-quality and rich metaphor annotations (abstract objects, concepts, relationships along with their corresponding object boxes) as there do not exist any datasets that facilitate the evaluation of these tasks. We perform a comprehensive analysis of state-of-the-art models in vision and language based on our annotations, highlighting strengths and weaknesses of current approaches in visual metaphor classification, localization, understanding (retrieval, question answering, captioning) and generation (text-to-image synthesis) tasks. We hope this work provides a concrete step towards developing AI systems with human-like creative capabilities. Project page: https://***

关键词： and reasoning language vision

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