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检索条件"任意字段=1994 IEEE Computer-Society Conference on Computer Vision and Pattern Recognition"

共 22907 条记录，以下是471-480 订阅

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Doubly Right Object recognition: A Why Prompt for Visual Rationales

Doubly Right Object Recognition: A Why Prompt for Visual Rat...

引用

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

作者： Mao, Chengzhi Teotial, Revant Sundari, Amrutha Menon, Sachit Yang, Junfeng Wang, Xin Vondrick, Carl Columbia Univ New York NY 10027 USA Microsoft Res Redmond WA USA

ISBN: (纸本)9798350301298

Many visual recognition models are evaluated only on their classification accuracy, a metric for which they obtain strong performance. In this paper, we investigate whether computer vision models can also provide correct rationales for their predictions. We propose a "doubly right" object recognition benchmark, where the metric requires the model to simultaneously produce both the right labels as well as the right rationales. We find that state-of-the-art visual models, such as CLIP, often provide incorrect rationales for their categorical predictions. However, by transferring the rationales from language models into visual representations through a tailored dataset, we show that we can learn a "why prompt," which adapts large visual representations to produce correct rationales. Visualizations and empirical experiments show that our prompts significantly improve performance on doubly right object recognition, in addition to zero-shot transfer to unseen tasks and datasets.

关键词： Explainable computer vision

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ChAda-ViT : Channel Adaptive Attention for Joint Representation Learning of Heterogeneous Microscopy Images

ChAda-ViT : Channel Adaptive Attention for Joint Representat...

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

作者： Bourriez, Nicolas Bendidi, Ihab Cohen, Ethan Watkinson, Gabriel Sanchez, Maxime Bollot, Guillaume Genovesio, Auguste Ecole Normale Super PSL Paris France Minos Biosci Paris France Synsight Eviy France

ISBN: (纸本)9798350353006

Unlike color photography images, which are consistently encoded into RGB channels, biological images encompass various modalities, where the type of microscopy and the meaning of each channel varies with each experiment. Importantly, the number of channels can range from one to a dozen and their correlation is often comparatively much lower than RGB, as each of them brings specific information content. This aspect is largely overlooked by methods designed out of the bioimage field, and current solutions mostly focus on intra-channel spatial attention, often ignoring the relationship between channels, yet crucial in most biological applications. Importantly, the variable channel type and count prevent the projection of several experiments to a unified representation for large scale pre-training. In this study, we propose ChAda-ViT, a novel Channel Adaptive vision Transformer architecture employing an Inter-Channel Attention mechanism on images with an arbitrary number, order and type of channels. We also introduce IDR-Cell100k, a bioimage dataset with a rich set of 79 experiments covering 7 microscope modalities, with a multitude of channel types, and counts varying from 1 to 10 per experiment. Our architecture, trained in a self-supervised manner, outperforms existing approaches in several biologically relevant downstream tasks. Additionally, it can be used to bridge the gap for the first time between assays with different microscopes, channel numbers or types by embedding various image and experimental modalities into a unified biological image representation. The latter should facilitate interdisciplinary studies and pave the way for better adoption of deep learning in biological image-based analyses. [GRAPHICS] .

关键词： Channel Adaptive Channel Embeddings Computational Biology computer vision Deep Learning Inter-Channel Attention Long Context Microscopy Imaging Self Supervised Learning Transformers vision Transformers

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Spectral Transfer Guided Active Domain Adaptation For Thermal Imagery

Spectral Transfer Guided Active Domain Adaptation For Therma...

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

作者： Ustun, Berkcan Kaya, Ahmet Kagan Ayerden, Ezgi Cakir Altinel, Fazil Aselsan Inc Res Ctr Yenimahalle Turkiye Middle East Tech Univ Dept Elect & Elect Engn Ankara Turkiye

ISBN: (纸本)9798350302493

The exploitation of visible spectrum datasets has led deep networks to show remarkable success. However, real-world tasks include low-lighting conditions which arise performance bottlenecks for models trained on large-scale RGB image datasets. Thermal IR cameras are more robust against such conditions. Therefore, the usage of thermal imagery in real-world applications can be useful. Unsupervised domain adaptation (UDA) allows transferring information from a source domain to a fully unlabeled target domain. Despite substantial improvements in UDA, the performance gap between UDA and its supervised learning counterpart remains significant. By picking a small number of target samples to annotate and using them in training, active domain adaptation tries to mitigate this gap with minimum annotation expense. We propose an active domain adaptation method in order to examine the efficiency of combining the visible spectrum and thermal imagery modalities. When the domain gap is considerably large as in the visible-to-thermal task, we may conclude that the methods without explicit domain alignment cannot achieve their full potential. To this end, we propose a spectral transfer guided active domain adaptation method to select the most informative unlabeled target samples while aligning source and target domains. We used the large-scale visible spectrum dataset MS-COCO as the source domain and the thermal dataset FLIR ADAS as the target domain to present the results of our method. Extensive experimental evaluation demonstrates that our proposed method outperforms the state-of-the-art active domain adaptation methods. The code and models are publicly available.(1)

关键词： computer vision

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Learning to Name Classes for vision and Language Models

Learning to Name Classes for Vision and Language Models

引用

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

作者： Parisot, Sarah Yang, Yongxin McDonagh, Steven Huawei Noahs Ark Lab Montreal PQ Canada

ISBN: (纸本)9798350301298

Large scale vision and language models can achieve impressive zero-shot recognition performance by mapping class specific text queries to image content. Two distinct challenges that remain however, are high sensitivity to the choice of handcrafted class names that define queries, and the difficulty of adaptation to new, smaller datasets. Towards addressing these problems, we propose to leverage available data to learn, for each class, an optimal word embedding as a function of the visual content. By learning new word embeddings on an otherwise frozen model, we are able to retain zero-shot capabilities for new classes, easily adapt models to new datasets, and adjust potentially erroneous, non-descriptive or ambiguous class names. We show that our solution can easily be integrated in image classification and object detection pipelines, yields significant performance gains in multiple scenarios and provides insights into model biases and labelling errors.

关键词： language reasoning vision

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SIFU: Side-view Conditioned Implicit Function for Real-world Usable Clothed Human Reconstruction

SIFU: Side-view Conditioned Implicit Function for Real-world...

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

作者： Zhang, Zechuan Yang, Zongxin Yang, Yi Zhejiang Univ CCAL ReLER Hangzhou Peoples R China

ISBN: (纸本)9798350353006

Creating high-quality 3D models of clothed humans from single images for real-world applications is crucial. Despite recent advancements, accurately reconstructing humans in complex poses or with loose clothing from in-the-wild images, along with predicting textures for unseen areas, remains a significant challenge. A key limitation of previous methods is their insufficient prior guidance in transitioning from 2D to 3D and in texture prediction. In response, we introduce SIFU (Side-view Conditioned Implicit Function for Real-world Usable Clothed Human Reconstruction), a novel approach combining a Side-view Decoupling Transformer with a 3D Consistent Texture Refinement pipeline. SIFU employs a cross-attention mechanism within the transformer, using SMPL-X normals as queries to effectively decouple side-view features in the process of mapping 2D features to 3D. This method not only improves the precision of the 3D models but also their robustness, especially when SMPL-X estimates are not perfect. Our texture refinement process leverages text-to-image diffusion-based prior to generate realistic and consistent textures for invisible views. Through extensive experiments, SIFU surpasses SOTA methods in both geometry and texture reconstruction, showcasing enhanced robustness in com-lex scenarios and achieving an unprecedented Chamfer and P2S measurement. Our approach extends to practical applications such as 3D printing and scene building, demonstrating its broad utility in real-world scenarios.

关键词： 3D reconstruction 3D vision Clothed human diffusion model single image to 3D

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Super-Resolution Reconstruction from Bayer-pattern Spike Streams

Super-Resolution Reconstruction from Bayer-Pattern Spike Str...

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

作者： Dong, Yanchen Xiong, Ruiqin Zhang, Jian Yu, Zhaofei Fan, Xiaopeng Zhu, Shuyuan Huang, Tiejun Peking Univ Sch Comp Sci Beijing Peoples R China Peking Univ Sch Elect & Comp Engn Beijing Peoples R China Peking Univ Inst Artificial Intelligence Beijing Peoples R China Harbin Inst Technol Sch Comp Sci & Technol Harbin Peoples R China Univ Elect Sci & Technol China Chengdu Peoples R China

ISBN: (纸本)9798350353006

Spike camera is a neuromorphic vision sensor that can capture highly dynamic scenes by generating a continuous stream of binary spikes to represent the arrival of photons at very high temporal resolution. Equipped with Bayer color filter array (CFA), color spike camera (CSC) has been invented to capture color information. Although spike camera has already demonstrated great potential for high-speed imaging, its spatial resolution is limited compared with conventional digital cameras. This paper proposes a Color Spike Camera Super-Resolution (CSCSR) network to super-resolve higher-resolution color images from spike camera streams with Bayer CFA. To be specific, we first propose a representation for Bayer-pattern spike streams, exploring local temporal information with global perception to represent the binary data. Then we exploit the CFA layout and sub-pixel level motion to collect temporal pixels for the spatial super-resolution of each color channel. In particular, a residual-based module for feature refinement is developed to reduce the impact of motion estimation errors. Considering color correlation, we jointly utilize the multi-stage temporal-pixel features of color channels to reconstruct the high-resolution color image. Experimental results demonstrate that the proposed scheme can reconstruct satisfactory color images with both high temporal and spatial resolution from low-resolution Bayer-pattern spike streams. The source codes are available at https://***/csycdong/CSCSR.

关键词： Bayer pattern Color filter arrary Demosaicing Neuromorphic sensor Spike camera Super resolution

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Teeth-SEG: An Efficient Instance Segmentation Framework for Orthodontic Treatment based on Multi-Scale Aggregation and Anthropic Prior Knowledge

Teeth-SEG: An Efficient Instance Segmentation Framework for ...

引用

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

作者： Zou, Bo Wang, Shaofeng Liu, Hao Sun, Gaoyue Wang, Yajie Zuo, FeiFei Quan, Chengbin Zhaot, Youjian Tsinghua Univ Beijing Peoples R China Capital Med Univ Beijing Peoples R China Imperial Coll London London England LargeV Inc Beijing Peoples R China Tsinghua Univ Zhongguancun Lab Beijing Peoples R China

ISBN: (纸本)9798350353006

Teeth localization, segmentation, and labeling in 2D images have great potential in modern dentistry to enhance dental diagnostics, treatment planning, and population-based studies on oral health. However, general instance segmentation frameworks are incompetent due to 1) the subtle differences between some teeth' shapes ( e.g., maxillary first premolar and second premolar), 2) the teeth's position and shape variation across subjects, and 3) the presence of abnormalities in the dentition (e.g., caries and edentulism). To address these problems, we propose a ViT-based framework named TeethSEG, which consists of stacked Multi-Scale Aggregation (MSA) blocks and an Anthropic Prior Knowledge (APK) layer. Specifically, to compose the two modules, we design a unique permutation-based upscaler to ensure high efficiency while establishing clear segmentation boundaries with multi-head self/cross-gating layers to emphasize particular semantics meanwhile maintaining the divergence between token embeddings. Besides, we collect the first open-sourced intraoral image dataset IO150K, which comprises over 150k intraoral photos, and all photos are annotated by orthodontists using a human-machine hybrid algorithm. Experiments on IO150K demonstrate that our TeethSEG outperforms the state-of-the-art segmentation models on dental image segmentation.

关键词： Image Segmentation Intra-Oral Image Medical vision Teeth Segmentation

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WorDepth: Variational Language Prior for Monocular Depth Estimation

WorDepth: Variational Language Prior for Monocular Depth Est...

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

作者： Zeng, Ziyao Wang, Daniel Yang, Fengyu Park, Hyoungseob Wong, Alex Soatto, Stefano Lao, Dong Yale Univ New Haven CT 06511 USA Univ Calif Los Angeles Los Angeles CA USA

ISBN: (纸本)9798350353006

Three-dimensional (3D) reconstruction from a single image is an ill-posed problem with inherent ambiguities, i.e. scale. Predicting a 3D scene from text description(s) is similarly ill-posed, i.e. spatial arrangements of objects described. We investigate the question of whether two inherently ambiguous modalities can be used in conjunction to produce metric-scaled reconstructions. To test this, we focus on monocular depth estimation, the problem of predicting a dense depth map from a single image, but with an additional text caption describing the scene. To this end, we begin by encoding the text caption as a mean and standard deviation;using a variational framework, we learn the distribution of the plausible metric reconstructions of 3D scenes corresponding to the text captions as a prior. To "select" a specific reconstruction or depth map, we encode the given image through a conditional sampler that samples from the latent space of the variational text encoder, which is then decoded to the output depth map. Our approach is trained alternatingly between the text and image branches: in one optimization step, we predict the mean and standard deviation from the text description and sample from a standard Gaussian, and in the other, we sample using a (image) conditional sampler. Once trained, we directly predict depth from the encoded text using the conditional sampler. We demonstrate our approach on indoor (NYUv2) and out-door (KITTI) scenarios, where we show that language can consistently improve performance in both. Code: https: //***/Adonis-galaxy/WorDepth.

关键词： Monocular Depth Estimation Variational Model vision-Language Model

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3DGStream: On-the-Fly Training of 3D Gaussians for Efficient Streaming of Photo-Realistic Free-Viewpoint Videos

3DGStream: On-the-Fly Training of 3D Gaussians for Efficient...

引用

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

作者： Sun, Jiakai Jiao, Han Li, Guangyuan Zhang, Zhanjie Zhao, Lei Xing, Wei Zhejiang Univ Hangzhou Peoples R China

ISBN: (纸本)9798350353006

Constructing photo-realistic Free-Viewpoint Videos ( FVVs) of dynamic scenes from multi-view videos remains a challenging endeavor. Despite the remarkable advance-ments achieved by current neural rendering techniques, these methods generally require complete video sequences for offline training and are not capable of real-time render-ing. To address these constraints, we introduce 3DGStream, a method designed for efficient FVV streaming of real-world dynamic scenes. Our method achieves fast on-the-fly per-frame reconstruction within 12 seconds and real-time rendering at 200 FPS. Specifically, we utilize 3D Gaussians ( 3DGs) to represent the scene. Instead of the naive ap-proach of directly optimizing 3DGs per-frame, we employ a compact Neural Transformation Cache (NTC) to model the translations and rotations of 3DGs, markedly reducing the training time and storage required for each FVV frame. Furthermore, we propose an adaptive 3DG addition strat-egy to handle emerging objects in dynamic scenes. Exper-iments demonstrate that 3DGStream achieves competitive performance in terms of rendering speed, image quality, training time, and model storage when compared with state-of-the-art methods.

关键词： 3D Gaussian Splatting 3D vision Dynamic Scene Reconstruction Free-Viewpoint Video Streaming Media

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No Time to Train: Empowering Non-Parametric Networks for Few-shot 3D Scene Segmentation

No Time to Train: Empowering Non-Parametric Networks for Few...

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

作者： Zhu, Xiangyang Zhang, Renrui He, Bowei Guo, Ziyu Liu, Jiaming Xiao, Han Fu, Chaoyou Dong, Hao Gao, Peng City Univ Hong Kong Hong Kong Peoples R China Chinese Univ Hong Kong Hong Kong Peoples R China Shanghai AI Lab Shanghai Peoples R China Peking Univ Beijing Peoples R China Tencent Youtu Lab Shenzhen Peoples R China

ISBN: (纸本)9798350353013;9798350353006

To reduce the reliance on large-scale datasets, recent works in 3D segmentation resort to few-shot learning. Current 3D few-shot segmentation methods first pre-train models on 'seen' classes, and then evaluate their generalization performance on 'unseen' classes. However, the prior pre-training stage not only introduces excessive time over-head but also incurs a significant domain gap on 'unseen' classes. To tackle these issues, we propose a Non-parametric Network for few-shot 3D Segmentation, SegNN, and its Parametric variant, Seg-PN. Without training, Seg-NN extracts dense representations by hand-crafted filters and achieves comparable performance to existing parametric models. Due to the elimination of pre-training, SegNN can alleviate the domain gap issue and save a substantial amount of time. Based on Seg-NN, Seg-PN only requires training a lightweight QUEry-Support Transferring (QUEST) module, which enhances the interaction between the support set and query set. Experiments suggest that Seg-PN outperforms previous state-of-the-art method by +4.19% and +7.71% mIoU on S3DIS and ScanNet datasets respectively, while reducing training time by -90%, indicating its effectiveness and efficiency. Code is available here.

关键词： 3D Segmentation 3D vision Few-shot Learning

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