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检索条件"任意字段=2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2024"

共 11890 条记录，以下是521-530 订阅

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Neural Contours: Learning to Draw Lines from 3D Shapes

Neural Contours: Learning to Draw Lines from 3D Shapes

引用

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

作者： Liu, Difan Nabail, Mohamed Hertzmann, Aaron Kalogerakis, Evangelos Univ Massachusetts Amherst Amherst MA 01003 USA Adobe Res San Francisco CA USA

ISBN: (纸本)9781728171685

This paper introduces a method for learning to generate line drawings from 3D models. Our architecture incorporates a differentiable module operating on geometric features of the 3D model, and an image-based module operating on view-based shape representations. At test time, geometric and view-based reasoning are combined with the help of a neural module to create a line drawing. The model is trained on a large number of crowdsourced comparisons of line drawings. Experiments demonstrate that our method achieves significant improvements in line drawing over the state-of-the-art when evaluated on standard benchmarks, resulting in drawings that are comparable to those produced by experienced human artists.

关键词： computer vision

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Representation Flow for Action recognition 32

Representation Flow for Action Recognition

引用

32nd ieee/cvf conference on computer vision and pattern recognition (cvpr)

作者： Piergiovanni, A. J. Ryoo, Michael S. Indiana Univ Dept Comp Sci Bloomington IN 47408 USA

ISBN: (纸本)9781728132938

In this paper, we propose a convolutional layer inspired by optical flow algorithms to learn motion representations. Our representation flow layer is a fully-differentiable layer designed to capture the 'flow' of any representation channel within a convolutional neural network for action recognition. Its parameters for iterative flow optimization are learned in an end-to-end fashion together with the other CNN model parameters, maximizing the action recognition performance. Furthermore, we newly introduce the concept of learning 'flow of flow' representations by stacking multiple representation flow layers. We conducted extensive experimental evaluations, confirming its advantages over previous recognition models using traditional optical flows in both computational speed and performance. The code is publicly available.

关键词： Action recognition Deep Learning Representation Learning

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Human Hands as Probes for Interactive Object Understanding

Human Hands as Probes for Interactive Object Understanding

引用

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

作者： Goyal, Mohit Modi, Sahil Goyal, Rishabh Gupta, Saurabh Univ Illinois Champaign IL 61820 USA

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Interactive object understanding, or what we can do to objects and how is a long-standing goal of computer vision. In this paper, we tackle this problem through observation of human hands in in-the-wild egocentric videos. We demonstrate that observation of what human hands interact with and how can provide both the relevant data and the necessary supervision. Attending to hands, readily localizes and stabilizes active objects for learning and reveals places where interactions with objects occur. Analyzing the hands shows what we can do to objects and how. We apply these basic principles on the EPIC-KITCHENS dataset, and successfully learn state-sensitive features, and object affordances (regions of interaction and afforded grasps), purely by observing hands in egocentric videos.

关键词： computer vision Affordances pattern recognition Probes Unsupervised learning Videos

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The Neglected Tails in vision-Language Models

The Neglected Tails in Vision-Language Models

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

作者： Parashar, Shubham Lin, Zhiqiu Liu, Tian Dong, Xiangjue Li, Yanan Ramanan, Deva Caverlee, James Kong, Shu Texas A&M Univ College Stn TX 77840 USA Carnegie Mellon Univ Pittsburgh PA 15213 USA Zhejiang Lab Hangzhou Peoples R China Univ Macau Taipa Macao Peoples R China

ISBN: (纸本)9798350353006

vision-language models (VLMs) excel in zero-shot recognition but their performance varies greatly across different visual concepts. For example, although CLIP achieves impressive accuracy on ImageNet (60-80%), its performance drops below 10% for more than ten concepts like night snake, presumably due to their limited presence in the pretraining data. However, measuring the frequency of concepts in VLMs' large-scale datasets is challenging. We address this by using large language models (LLMs) to count the number of pretraining texts that contain synonyms of these concepts. Our analysis confirms that popular datasets, such as LAION, exhibit a long-tailed concept distribution, yielding biased performance in VLMs. We also find that downstream applications of VLMs, including visual chatbots (e.g., GPT-4V) and text-to-image models (e.g., Stable Diffusion), often fail to recognize or generate images of rare concepts identified by our method. To mitigate the imbalanced performance of zero-shot VLMs, we propose REtrieval-Augmented Learning (REAL). First, instead of prompting VLMs using the original class names, REAL uses their most frequent synonyms found in pretraining texts. This simple change already outperforms costly human-engineered and LLM-enriched prompts over nine benchmark datasets. Second, REAL trains a linear classifier on a small yet balanced set of pretraining data re-trieved using concept synonyms. REAL surpasses the previous zero-shot SOTA, using 400x less storage and 10,000x less training time!

关键词： Long tailed recognition vision-Language Models Zero-Shot recognition

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Camera Pose Estimation using Implicit Distortion Models

Camera Pose Estimation using Implicit Distortion Models

引用

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

作者： Pan, Linfei Pollefeys, Marc Larsson, Viktor Swiss Fed Inst Technol Zurich Switzerland Microsoft Redmond WA USA Lund Univ Lund Sweden

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Low-dimensional parametric models are the de-facto standard in computer vision for intrinsic camera calibration. These models explicitly describe the mapping between incoming viewing rays and image pixels. In this paper, we explore an alternative approach which implicitly models the lens distortion. The main idea is to replace the parametric model with a regularization term that ensures the latent distortion map varies smoothly throughout the image. The proposed model is effectively parameter-free and allows us to optimize the 6 degree-of-freedom camera pose without explicitly knowing the intrinsic calibration. We show that the method is applicable to a wide selection of cameras with varying distortion and in multiple applications, such as visual localization and structure-from-motion.

关键词： Solid modeling computer vision Visualization Computational modeling Pose estimation Cameras Distortion

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NTIRE 2024 Challenge on Night Photography Rendering

NTIRE 2024 Challenge on Night Photography Rendering

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

作者： Ershov, Egor Panshin, Artyom Karasev, Oleg Korchagin, Sergey Lev, Shepelev Startsev, Alexandr Vladimirov, Daniil Zaychenkova, Ekaterina Banic, Nikola Iarchuk, Dmitrii R. Efimova, Maria Timofte, Radu Terekhin, Arseniy Kharkevich Inst Inst Informat Transmiss Problems Moscow Russia Artifitial Intelligence Res Inst AIRI Moscow Russia Gideon Bros Osijek Croatia Swiss Fed Inst Technol Zurich Zurich Switzerland Univ Wurzburg Wurzburg Germany

ISBN: (纸本)9798350365474

This paper presents a review of the NTIRE 2024 challenge on night photography rendering. The goal of the challenge was to find solutions that process raw camera images taken in nighttime conditions, and thereby produce a photo-quality output images in the standard RGB (sRGB) space. Unlike the previous year's competition, the challenge images were collected with a mobile phone and the speed of algorithms was also measured alongside the quality of their output. To evaluate the results, a sufficient number of viewers were asked to assess the visual quality of the proposed solutions, considering the subjective nature of the task. There were 2 nominations: quality and efficiency. Top 5 solutions in terms of output quality were sorted by evaluation time (see Fig. 1). The top ranking participants' solutions effectively represent the state-of-the-art in nighttime photography rendering. More results can be found at https://***.

关键词： Photography

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Seeing the Unseen: Visual Common Sense for Semantic Placement

Seeing the Unseen: Visual Common Sense for Semantic Placemen...

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

作者： Ramrakhya, Ram Kembhavi, Aniruddha Batra, Dhruv Kira, Zsolt Zeng, Kuo-Hao Weihs, Luca Georgia Inst Technol Atlanta GA 30332 USA PRIOR Allen Inst AI Seattle WA USA PRIOR AI2 Seattle WA USA

ISBN: (纸本)9798350353006

computer vision tasks typically involve describing what is present in an image (e.g. classification, detection, segmentation, and captioning). We study a visual common sense task that requires understanding 'what is not present'. Specifically, given an image (e.g. of a living room) and a name of an object ("cushion"), a vision system is asked to predict semantically-meaningful regions (masks or bounding boxes) in the image where that object could be placed or is likely be placed by humans (e.g. on the sofa). We call this task: Semantic Placement (SP) and believe that such common-sense visual understanding is critical for assitive robots (tidying a house), AR devices (automatically rendering an object in the user's space), and visually-grounded chatbots with common sense. Studying the invisible is hard. Datasets for image description are typically constructed by curating relevant images (e.g. via image search with object names) and asking humans to annotate the contents of the image;neither of those two steps are straightforward for objects not present in the image. We overcome this challenge by operating in the opposite direction: we start with an image of an object in context, which is easy to find online, and then remove that object from the image via inpainting. This automated pipeline converts unstructured web data into a dataset comprising pairs of images with/without the object. With this proposed data generation pipeline, we collect a novel dataset, containing similar to 1.3M images across 9 object categories. We then train a SP prediction model, called CLIP-UNet, on our dataset. The CLIP-UNet outperforms existing VLMs and baselines that combine semantic priors with object detectors, generalizes well to real-world and simulated images and exhibits semantics-aware reasoning for object placement. In our user studies, we find that the SP masks predicted by CLIP-UNet are favored 43.7% and 31.3% times when comparing against the 4 SP baselines on real and simulated

关键词： Common Sense Reasoning computer vision Embodied AI

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Multiple Instance Captioning: Learning Representations from Histopathology Textbooks and Articles

Multiple Instance Captioning: Learning Representations from ...

引用

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

作者： Gamper, Jevgenij Rajpoot, Nasir Univ Warwick Coventry W Midlands England

ISBN: (纸本)9781665445092

We present ARCH, a computational pathology (CP) multiple instance captioning dataset to facilitate dense supervision of CP tasks. Existing CP datasets focus on narrow tasks;ARCH on the other hand contains dense diagnostic and morphological descriptions for a range of stains, tissue types and pathologies. Using intrinsic dimensionality estimation, we show that ARCH is the only CP dataset to (ARCH-)rival its computer vision analog MS-COCO Captions. We conjecture that an encoder pre-trained on dense image captions learns transferable representations for most CP tasks. We support the conjecture with evidence that ARCH representation transfers to a variety of pathology sub-tasks better than ImageNet features or representations obtained via self-supervised or multi-task learning on pathology images alone. We release our best model and invite other researchers to test it on their CP tasks.

关键词： computer vision Histopathology Computational modeling Estimation pattern recognition Task analysis

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Video Frame Interpolation via Direct Synthesis with the Event-based Reference

Video Frame Interpolation via Direct Synthesis with the Even...

引用

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

作者： Liu, Yuhan Deng, Yongjian Chen, Hao Yang, Hen Beijing Univ Technol Coll Comp Sci Beijing Peoples R China Southeast Univ Key Lab Comp Network & Informat Integrat Dhaka Bangladesh

ISBN: (纸本)9798350353013;9798350353006

Video Frame Interpolation (VFI) has witnessed a surge in popularity due to its abundant downstream applications. Event-based VFI (E-VFI) has recently propelled the advancement of VFI. Thanks to the high temporal resolution benefits, event cameras can bridge the informational void present between successive video frames. Most state-of-the-art E-VFI methodologies follow the conventional VFI paradigm, which pivots on motion estimation between consecutive frames to generate intermediate frames through a process of warping and refinement. However, this reliance engenders a heavy dependency on the quality and consistency of keyframes, rendering these methods susceptible to challenges in extreme real-world scenarios, such as missing moving objects and severe occlusion dilemmas. This study proposes a novel E-VFI framework that directly synthesize intermediate frames leveraging event-based reference, obviating the necessity for explicit motion estimation and substantially enhancing the capacity to handle motion occlusion. Given the sparse and inherently noisy nature of event data, we prioritize the reliability of the event-based reference, leading to the development of an innovative event-aware reconstruction strategy for accurate reference generation. Besides, we implement a bi-directional event- guided alignment from keyframes to the reference using the introduced E-PCD module. Finally, a transformer-based decoder is adopted for prediction refinement. Comprehensive experimental evaluations on both synthetic and real- world datasets underscore the superiority of our approach and its potential to execute high-quality VFI tasks.

关键词： Event-Based vision Video Frame Interpolation Video Synthesis

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End-to-End Learned Random Walker for Seeded Image Segmentation 32

End-to-End Learned Random Walker for Seeded Image Segmentati...

引用

32nd ieee/cvf conference on computer vision and pattern recognition (cvpr)

作者： Cerrone, Lorenzo Zeilmann, Alexander Hamprecht, Fred A. Heidelberg Univ Heidelberg Collaboratory Image Proc IWR Heidelberg Germany

ISBN: (纸本)9781728132938

We present an end-to-end learned algorithm for seeded segmentation. Our method is based on the Random Walker algorithm, where we predict the edge weights of the un- derlying graph using a convolutional neural network. This can be interpreted as learning context-dependent diffusivities for a linear diffusion process. After calculating the exact gradient for optimizing these diffusivities, we pro- pose simplifications that sparsely sample the gradient while still maintaining competitive results. The proposed method achieves the currently best results on the seeded CREMI neuron segmentation challenge.

关键词： Biological and Cell Microscopy computer vision Theory Grouping and Shape Medical Segmentation

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