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Learning Customized Visual Models with Retrieval-Augmented Knowledge

Learning Customized Visual Models with Retrieval-Augmented K...

引用

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

作者： Liu, Haotian Son, Kilho Yang, Jianwei Liu, Ce Gao, Jianfeng Lee, Yong Jae Li, Chunyuan Univ Wisconsin Madison Madison WI 53706 USA Microsoft Redmond WA USA

ISBN: (纸本)9798350301298

Image-text contrastive learning models such as CLIP have demonstrated strong task transfer ability. The high generality and usability of these visual models is achieved via a web-scale data collection process to ensure broad concept coverage, followed by expensive pre-training to feed all the knowledge into model weights. Alternatively, we propose REACT, REtrieval-Augmented CusTomization, a framework to acquire the relevant web knowledge to build customized visual models for target domains. We retrieve the most relevant image-text pairs (similar to 3% of CLIP pre-training data) from the web-scale database as external knowledge and propose to customize the model by only training new modularized blocks while freezing all the original weights. The effectiveness of REACT is demonstrated via extensive experiments on classification, retrieval, detection and segmentation tasks, including zero, few, and full-shot settings. Particularly, on the zero-shot classification task, compared with CLIP, it achieves up to 5.4% improvement on ImageNet and 3.7% on the ELEVATER benchmark (20 datasets).

关键词： and reasoning language vision

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Transfer4D: A framework for frugal motion capture and deformation transfer

Transfer4D: A framework for frugal motion capture and deform...

引用

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

作者： Maheshwari, Shubh Narain, Rahul Hebbalaguppe, Ramya TCS Res Gurugram India Indian Inst Technol Delhi New Delhi India

ISBN: (纸本)9798350301298

Animating a virtual character based on a real performance of an actor is a challenging task that currently requires expensive motion capture setups and additional effort by expert animators, rendering it accessible only to large production houses. The goal of our work is to democratize this task by developing a frugal alternative termed "Transfer4D" that uses only commodity depth sensors and further reduces animators' effort by automating the rigging and animation transfer process. Our approach can transfer motion from an incomplete, single-view depth video to a semantically similar target mesh, unlike prior works that make a stricter assumption on the source to be noise-free and watertight. To handle sparse, incomplete videos from depth video inputs and variations between source and target objects, we propose to use skeletons as an intermediary representation between motion capture and transfer. We propose a novel unsupervised skeleton extraction pipeline from a single-view depth sequence that incorporates additional geometric information, resulting in superior performance in motion reconstruction and transfer in comparison to the contemporary methods and making our approach generic. We use non-rigid reconstruction to track motion from the depth sequence, and then we rig the source object using skinning decomposition. Finally, the rig is embedded into the target object for motion retargeting.

关键词： vision + graphics

来源：评论

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Zero-shot Referring Image Segmentation with Global-Local Context Features

Zero-shot Referring Image Segmentation with Global-Local Con...

引用

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

作者： Yu, Seonghoon Seo, Paul Hongsuck Son, Jeany GIST AI Grad Sch Gwangju South Korea Google Res Mountain View CA USA

ISBN: (纸本)9798350301298

Referring image segmentation (RIS) aims to find a segmentation mask given a referring expression grounded to a region of the input image. Collecting labelled datasets for this task, however, is notoriously costly and labor-intensive. To overcome this issue, we propose a simple yet effective zero-shot referring image segmentation method by leveraging the pre-trained cross-modal knowledge from CLIP. In order to obtain segmentation masks grounded to the input text, we propose a mask-guided visual encoder that captures global and local contextual information of an input image. By utilizing instance masks obtained from off-the-shelf mask proposal techniques, our method is able to segment fine-detailed instance-level groundings. We also introduce a global-local text encoder where the global feature captures complex sentence-level semantics of the entire input expression while the local feature focuses on the target noun phrase extracted by a dependency parser. In our experiments, the proposed method outperforms several zero-shot baselines of the task and even the weakly supervised referring expression segmentation method with substantial margins. Our code is available at https://***/Seonghoon-Yu/Zero-shot-RIS.

关键词： language reasoning vision

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Manipulating Transfer Learning for Property Inference

Manipulating Transfer Learning for Property Inference

引用

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

作者： Tian, Yulong Suya, Fnu Suri, Anshuman Xu, Fengyuan Evans, David Nanjing Univ State Key Lab Novel Software Technol Nanjing Peoples R China Univ Virginia Charlottesville VA USA

ISBN: (纸本)9798350301298

Transfer learning is a popular method for tuning pre-trained (upstream) models for different downstream tasks using limited data and computational resources. We study how an adversary with control over an upstream model used in transfer learning can conduct property inference attacks on a victim's tuned downstream model. For example, to infer the presence of images of a specific individual in the downstream training set. We demonstrate attacks in which an adversary can manipulate the upstream model to conduct highly effective and specific property inference attacks (AUC score > 0.9), without incurring significant performance loss on the main task. The main idea of the manipulation is to make the upstream model generate activations (intermediate features) with different distributions for samples with and without a target property, thus enabling the adversary to distinguish easily between downstream models trained with and without training examples that have the target property. Our code is available at https:// ***/yulongt23/Transfer-Inference.

关键词： accountability ethics in vision fairness privacy Transparency

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Iterative Next Boundary Detection for Instance Segmentation of Tree Rings in Microscopy Images of Shrub Cross Sections

Iterative Next Boundary Detection for Instance Segmentation ...

引用

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

作者： Gillert, Alexander Resente, Giulia Anadon-Rosell, Alba Wilmking, Martin von Lukas, Uwe Freiherr Fraunhofer Inst Comp Graph Res IGD Rostock Germany Ernst Moritz Arndt Univ Inst Bot & Landscape Ecol Greifswald Germany Ctr Res Ecol & Forestry Applicat CREAF Barcelona Spain Univ Rostock Inst Visual & Analyt Comp Rostock Germany

ISBN: (纸本)9798350301298

We address the problem of detecting tree rings in microscopy images of shrub cross sections. This can be regarded as a special case of the instance segmentation task with several unique challenges such as the concentric circular ring shape of the objects and high precision requirements that result in inadequate performance of existing methods. We propose a new iterative method which we term Iterative Next Boundary Detection (INBD). It intuitively models the natural growth direction, starting from the center of the shrub cross section and detecting the next ring boundary in each iteration step. In our experiments, INBD shows superior performance to generic instance segmentation methods and is the only one with a built-in notion of chronological order. Our dataset and source code are available at http://***/alexander-g/INBD.

关键词： cell microscopy Medical and biological vision

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Unsupervised Intrinsic Image Decomposition with LiDAR Intensity

Unsupervised Intrinsic Image Decomposition with LiDAR Intens...

引用

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

作者： Sato, Shogo Yao, Yasuhiro Yoshida, Taiga Kaneko, Takuhiro Ando, Shingo Shimamura, Jun INTT Human Informat Labs Tokyo Japan NTT Commun Sci Labs Tokyo Japan

ISBN: (纸本)9798350301298

Intrinsic image decomposition (IID) is the task that decomposes a natural image into albedo and shade. While IID is typically solved through supervised learning methods, it is not ideal due to the difficulty in observing ground truth albedo and shade in general scenes. Conversely, unsupervised learning methods are currently underperforming supervised learning methods since there are no criteria for solving the ill-posed problems. Recently, light detection and ranging (LiDAR) is widely used due to its ability to make highly precise distance measurements. Thus, we have focused on the utilization of LiDAR, especially LiDAR intensity, to address this issue. In this paper, we propose unsupervised intrinsic image decomposition with LiDAR intensity (IID-LI). Since the conventional unsupervised learning methods consist of image-to-image transformations, simply inputting LiDAR intensity is not an effective approach. Therefore, we design an intensity consistency loss that computes the error between LiDAR intensity and gray-scaled albedo to provide a criterion for the ill-posed problem. In addition, LiDAR intensity is difficult to handle due to its sparsity and occlusion, hence, a LiDAR intensity densification module is proposed. We verified the estimating quality using our own dataset, which include RGB images, LiDAR intensity and human judged annotations. As a result, we achieved an estimation accuracy that outperforms conventional unsupervised learning methods.

关键词： Physics-based vision and shape-from-X

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Open-Vocabulary Panoptic Segmentation with Text-to-Image Diffusion Models

Open-Vocabulary Panoptic Segmentation with Text-to-Image Dif...

引用

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

作者： Xu, Jiarui Liu, Sifei Vahdat, Arash Byeon, Wonmin Wang, Xiaolong De Meo, Shalini Univ Calif San Diego La Jolla CA 92093 USA NVIDIA Santa Clara CA USA

ISBN: (纸本)9798350301298

We present ODISE: Open-vocabulary DIffusion-based panoptic SEgmentation, which unifies pre-trained textimage diffusion and discriminative models to perform open-vocabulary panoptic segmentation. Text-to-image diffusion models have the remarkable ability to generate high-quality images with diverse open-vocabulary language descriptions. This demonstrates that their internal representation space is highly correlated with open concepts in the real world. Text-image discriminative models like CLIP, on the other hand, are good at classifying images into open-vocabulary labels. We leverage the frozen internal representations of both these models to perform panoptic segmentation of any category in the wild. Our approach outperforms the previous state of the art by significant margins on both open-vocabulary panoptic and semantic segmentation tasks. In particular, with COCO training only, our method achieves 23.4 PQ and 30.0 mIoU on the ADE20K dataset, with 8.3 PQ and 7.9 mIoU absolute improvement over the previous state of the art. We open-source our code and models at https://***/NVlabs/ODISE.

关键词： and reasoning language vision

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Learning to Zoom and Unzoom

Learning to Zoom and Unzoom

引用

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

作者： Thavamani, Chittesh Li, Mengtian Ferroni, Francesco Ramanan, Deva Carnegie Mellon Univ Pittsburgh PA 15213 USA Argo AI Pittsburgh PA USA Waymo Mountain View CA USA Nvidia Santa Clara CA USA

ISBN: (纸本)9798350301298

Many perception systems in mobile computing, autonomous navigation, and AR/VR face strict compute constraints that are particularly challenging for high-resolution input images. Previous works propose nonuniform downsamplers that "learn to zoom" on salient image regions, reducing compute while retaining task-relevant image information. However, for tasks with spatial labels (such as 2D/3D object detection and semantic segmentation), such distortions may harm performance. In this work (LZU), we "learn to zoom" in on the input image, compute spatial features, and then "unzoom" to revert any deformations. To enable efficient and differentiable unzooming, we approximate the zooming warp with a piecewise bilinear mapping that is invertible. LZU can be applied to any task with 2D spatial input and any model with 2D spatial features, and we demonstrate this versatility by evaluating on a variety of tasks and datasets: object detection on Argoverse-HD, semantic segmentation on Cityscapes, and monocular 3D object detection on nuScenes. Interestingly, we observe boosts in performance even when high-resolution sensor data is unavailable, implying that LZU can be used to "learn to upsample" as well. Code and additional visuals are available at https://***/lzu/.

关键词： Efficient and scalable vision

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Towards Automated Polyp Segmentation Using Weakly- and Semi-Supervised Learning and Deformable Transformers

Towards Automated Polyp Segmentation Using Weakly- and Semi-...

引用

2023 ieee/CVF conference on computer vision and pattern recognition workshops, CVPRW 2023

作者： Ren, Guangyu Lazarou, Michalis Yuan, Jing Stathaki, Tania Imperial College London United Kingdom

ISBN: (纸本)9798350302493

Polyp segmentation is a crucial step towards computer-aided diagnosis of colorectal cancer. However, most of the polyp segmentation methods require pixel-wise annotated datasets. Annotated datasets are tedious and time-consuming to produce, especially for physicians who must dedicate their time to their patients. To this end, we propose a novel weakly- and semi-supervised learning polyp segmentation framework that can be trained using only weakly annotated images along with unlabeled images making it very cost-efficient to use. More specifically our contributions are: 1) a novel weakly annotated polyp dataset, 2) a novel sparse foreground loss that suppresses false positives and improves weakly-supervised training, 3) a deformable transformer encoder neck for feature enhancement by fusing information across levels and flexible spatial *** experimental results demonstrate the merits of our ideas on five challenging datasets outperforming some state-of-the-art fully supervised models. Also, our framework can be utilized to fine-tune models trained on natural image segmentation datasets drastically improving their performance for polyp segmentation and impressively demonstrating superior performance to fully supervised fine-tuning. Code can be found in https://***/ic-qialanqian/WS-DefSegNet. © 2023 ieee.

关键词： computer aided diagnosis

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PlenVDB: Memory Efficient VDB-Based Radiance Fields for Fast Training and Rendering

PlenVDB: Memory Efficient VDB-Based Radiance Fields for Fast...

引用

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

作者： Yan, Han Liu, Celong Ma, Chao Mei, Xing Shanghai Jiao Tong Univ AI Inst MoE Key Lab Artificial Shanghai Peoples R China ByteDance Inc Beijing Peoples R China

ISBN: (纸本)9798350301298

In this paper, we present a new representation for neural radiance fields that accelerates both the training and the inference processes with VDB, a hierarchical data structure for sparse volumes. VDB takes both the advantages of sparse and dense volumes for compact data representation and efficient data access, being a promising data structure for NeRF data interpolation and ray marching. Our method, Plenoptic VDB (PlenVDB), directly learns the VDB data structure from a set of posed images by means of a novel training strategy and then uses it for real-time rendering. Experimental results demonstrate the effectiveness and the efficiency of our method over previous arts: First, it converges faster in the training process. Second, it delivers a more compact data format for NeRF data presentation. Finally, it renders more efficiently on commodity graphics hardware. Our mobile PlenVDB demo achieves 30+ FPS, 1280x720 resolution on an iPhone12 mobile phone. Check *** for details.

关键词： vision + graphics

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