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Evaluating Confidence Calibration in Endoscopic Diagnosis Models

Evaluating Confidence Calibration in Endoscopic Diagnosis Mo...

引用

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

作者： Dehghani, Nikoo Thijssen, Ayla van der Zander, Quirine E. W. Schreuder, Ramon-Michel Schoon, Erik J. van der Sommen, Fons de With, Peter H. N. Eindhoven Univ Technol Eindhoven Netherlands Maastricht Univ Med Ctr Maastricht Netherlands GROW Res Inst Oncol & Reprod Maastricht Netherlands Catharina Hosp Eindhoven Netherlands Eindhoven Artificial Intelligence Syst Inst Eindhoven Netherlands

ISBN: (纸本)9798350365474

Colorectal polyps are prevalent precursors to colorectal cancer, making their accurate characterization essential for timely intervention and patient outcomes. Deep learning-based computer-aided diagnosis (CADx) systems have shown promising performance in the automated detection and categorization of colorectal polyps (CRP) using endoscopic images. However, alongside the advancement in diagnostic accuracy, the need for reliable and accurate quantification of uncertainty estimates within these systems has become increasingly important. The primary focus of this study is on refining the reliability of computer-aided diagnosis of CRPs within clinical practice. We perform an investigation of widely used model calibration techniques and how they translate into clinical applications, specifically for CRP categorization data. The experiments reveal that the Variational Inference method excels in intra-dataset calibration, but lacks efficiency and inter-dataset generalization. Laplace approximation and temperature scaling methods offer improved calibration across datasets.

关键词： Bayesian neural networks computer-aided diagnosis Confidence calibration Model reliability

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Test-Time Zero-Shot Temporal Action Localization

Test-Time Zero-Shot Temporal Action Localization

引用

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

作者： Liberatori, Benedetta Conti, Alessandro Rota, Paolo Wang, Yiming Ricci, Elisa Univ Trento Trento Italy Fdn Bruno Kessler Trento Italy

ISBN: (纸本)9798350353006

Zero-Shot Temporal Action Localization (ZS-TAL) seeks to identify and locate actions in untrimmed videos unseen during training. Existing ZS-TAL methods involve fine-tuning a model on a large amount of annotated training data. While effective, training-based ZS-TAL approaches assume the availability of labeled data for supervised learning, which can be impractical in some applications. Furthermore, the training process naturally induces a domain bias into the learned model, which may adversely affect the model's generalization ability to arbitrary videos. These considerations prompt us to approach the ZS-TAL problem from a radically novel perspective, relaxing the requirement for training data. To this aim, we introduce a novel method that performs Test-Time adaptation for Temporal Action Localization (T3AL). In a nutshell, T3AL adapts a pre-trained vision and Language Model (VLM). T3AL operates in three steps. First, a video-level pseudo-label of the action category is computed by aggregating information from the entire video. Then, action localization is performed adopting a novel procedure inspired by self-supervised learning. Finally, frame-level textual descriptions extracted with a state-of-the-art captioning model are employed for refining the action region proposals. We validate the effectiveness of T3AL by conducting experiments on the THUMOS14 and the ActivityNet-v1.3 datasets. Our results demonstrate that T3AL significantly outperforms zero-shot baselines based on state-of-the-art VLMs, confirming the benefit of a test-time adaptation approach.

关键词： temporal action localization vision and language

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3D Highlighter: Localizing Regions on 3D Shapes via Text Descriptions

3D Highlighter: Localizing Regions on 3D Shapes via Text Des...

引用

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

作者： Decatur, Dale Lang, Itai Hanocka, Rana Univ Chicago Chicago IL 60637 USA

ISBN: (纸本)9798350301298

We present 3D Highlighter, a technique for localizing semantic regions on a mesh using text as input. A key feature of our system is the ability to interpret "out-of-domain" localizations. Our system demonstrates the ability to reason about where to place non-obviously related concepts on an input 3D shape, such as adding clothing to a bare 3D animal model. Our method contextualizes the text description using a neural field and colors the corresponding region of the shape using a probability-weighted blend. Our neural optimization is guided by a pre-trained CLIP encoder, which bypasses the need for any 3D datasets or 3D annotations. Thus, 3D Highlighter is highly flexible, general, and capable of producing localizations on a myriad of input shapes. Our code is publicly available at https: //***/threedle/3DHighlighter.

关键词： vision + graphics

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PMatch: Paired Masked Image Modeling for Dense Geometric Matching

PMatch: Paired Masked Image Modeling for Dense Geometric Mat...

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

作者： Zhu, Shengjie Liu, Xiaoming Michigan State Univ Dept Comp Sci & Engn E Lansing MI 48824 USA

ISBN: (纸本)9798350301298

Dense geometric matching determines the dense pixel-wise correspondence between a source and support image corresponding to the same 3D structure. Prior works employ an encoder of transformer blocks to correlate the two-frame features. However, existing monocular pretraining tasks, e.g., image classification, and masked image modeling (MIM), can not pretrain the cross-frame module, yielding less optimal performance. To resolve this, we reformulate the MIM from reconstructing a single masked image to reconstructing a pair of masked images, enabling the pretraining of transformer module. Additionally, we incorporate a decoder into pretraining for improved upsampling results. Further, to be robust to the textureless area, we propose a novel cross-frame global matching module (CFGM). Since the most textureless area is planar surfaces, we propose a homography loss to further regularize its learning. Combined together, we achieve the State-of-The-Art (SoTA) performance on geometric matching. Codes and models are available at https://***/ShngJZ/PMatch.

关键词： Low-level vision

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CAMS: CAnonicalized Manipulation Spaces for Category-Level Functional Hand-Object Manipulation Synthesis

CAMS: CAnonicalized Manipulation Spaces for Category-Level F...

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

作者： Zheng, Juntian Zheng, Qingyuan Fang, Lixing Liu, Yun Yi, Li Tsinghua Univ IIIIS Beijing Peoples R China Shanghai Artificial Intelligence Lab Shanghai Peoples R China Shanghai Qi Zhi Inst Shanghai Peoples R China

ISBN: (纸本)9798350301298

In this work, we focus on a novel task of category-level functional hand-object manipulation synthesis covering both rigid and articulated object categories. Given an object geometry, an initial human hand pose as well as a sparse control sequence of object poses, our goal is to generate a physically reasonable hand-object manipulation sequence that performs like human beings. To address such a challenge, we first design CAnonicalized Manipulation Spaces (CAMS), a two-level space hierarchy that canonicalizes the hand poses in an object-centric and contact-centric view. Benefiting from the representation capability of CAMS, we then present a two-stage framework for synthesizing human-like manipulation animations. Our framework achieves state-of-the-art performance for both rigid and articulated categories with impressive visual effects. Codes and video results can be found at our project home-page: https://***/.

关键词： vision + graphics

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Classifier Guided Cluster Density Reduction for Dataset Selection

Classifier Guided Cluster Density Reduction for Dataset Sele...

引用

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

作者： Chang, Cheng Long, Keyu Li, Zijian Rai, Himanshu Layer 6 AI Toronto ON Canada

ISBN: (纸本)9798350365474

In this paper, we address the challenge of selecting an optimal dataset from a source pool with annotations to enhance performance on a target dataset derived from a different source. This is important in scenarios where it is hard to afford on-the-fly dataset annotation and is also the theme of the second Visual Data Understanding (VDU) Challenge. Our solution, the Classifier Guided Cluster Density Reduction (CCDR) framework, operates in two stages. Initially, we employ a filtering technique to identify images that align with the target dataset's distribution. Subsequently, we implement a graph-based cluster density reduction method, steered by a classifier that approximates the distance between the target distribution and source distribution. This classifier is trained to distinguish between images that resemble the target dataset and those that do not, facilitating the pruning process shown in Figure 1. Our approach maintains a balance between selecting pertinent images that match the target distribution and eliminating redundant ones that do not contribute to the enhancement of the detection model. We demonstrate the superiority of our method over various baselines in object detection tasks, particularly in optimizing the training set distribution on the region100 dataset. We have released our code here: https://***/ himsR/DataCVChallenge-2024/tree/main

关键词： computer vision Data Search deep learning domain Transfer

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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...

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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

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Generate Like Experts: Multi-Stage Font Generation by Incorporating Font Transfer Process into Diffusion Models

Generate Like Experts: Multi-Stage Font Generation by Incorp...

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

作者： Fu, Bin Yu, Fanghua Liu, Anran Wang, Zixuan Wen, Jie He, Junjun Qiao, Yu Chinese Acad Sci Shenzhen Inst Adv Technol ShenZhen Key Lab Comp Vis & Pattern Recognit Shenzhen Peoples R China Shanghai Artificial Intelligence Lab Shanghai Peoples R China Univ Hong Kong Hong Kong Peoples R China Harbin Inst Technol Shenzhen Peoples R China

ISBN: (纸本)9798350353013;9798350353006

Few-shot font generation (FFG) produces stylized font images with a limited number of reference samples, which can significantly reduce labor costs in manual font designs. Most existing FFG methods follow the style-content disentanglement paradigm and employ the Generative Adversarial Network (GAN) to generate target fonts by combining the decoupled content and style representations. The complicated structure and detailed style are simultaneously generated in those methods, which may be the sub-optimal solutions for FFG task. Inspired by most manual font design processes of expert designers, in this paper, we model font generation as a multi-stage generative process. Specifically, as the injected noise and the data distribution in diffusion models can be well-separated into different sub-spaces, we are able to incorporate the font transfer process into these models. Based on this observation, we generalize diffusion methods to model font generative process by separating the reverse diffusion process into three stages with different functions: The structure construction stage first generates the structure information for the target character based on the source image, and the font transfer stage subsequently transforms the source font to the target font. Finally, the font refinement stage enhances the appearances and local details of the target font images. Based on the above multi-stage generative process, we construct our font generation framework, named MSD-Font, with a dual-network approach to generate font images. The superior performance demonstrates the effectiveness of our model. The code is available at: https://***/fubinfb/MSD-Font

关键词： Diffusion Model Few-shot Font Generation Probabilistic Generative Model

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Confidence-Aware RGB-D Face recognition via Virtual Depth Synthesis

Confidence-Aware RGB-D Face Recognition via Virtual Depth Sy...

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

作者： Chen, Zijian Wang, Mei Deng, Weihong Shi, Hongzhi Wen, Dongchao Zhang, Yingjie Cui, Xingchen Zhao, Jian Inspur Beijing Elect Informat Ind Co Ltd Beijing Peoples R China

ISBN: (纸本)9798350365474

2D face recognition encounters challenges in unconstrained environments due to varying illumination, occlusion, and pose. Recent studies focus on RGB-D face recognition to improve robustness by incorporating depth information. However, collecting sufficient paired RGB-D training data is expensive and time-consuming, hindering wide deployment. In this work, we first construct a diverse depth dataset generated by 3D Morphable Models for depth model pre-training. Then, we propose a domain-independent pre-training framework that utilizes readily available pre-trained RGB and depth models to separately perform face recognition without needing additional paired data for retraining. To seamlessly integrate the two distinct networks and harness the complementary benefits of RGB and depth information for improved accuracy, we propose an innovative Adaptive Confidence Weighting (ACW). This mechanism is designed to learn confidence estimates for each modality to achieve modality fusion at the score level. Our method is simple and lightweight, only requiring ACW training beyond the backbone models. Experiments on multiple public RGB-D face recognition benchmarks demonstrate state-of-the-art performance surpassing previous methods based on depth estimation and feature fusion, validating the efficacy of our approach.

关键词： domain-independent pre-training modality fusion RGB-D face recognition

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Recognizing Rigid patterns of Unlabeled Point Clouds by Complete and Continuous Isometry Invariants with no False Negatives and no False Positives

Recognizing Rigid Patterns of Unlabeled Point Clouds by Comp...

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

作者： Widdowson, Daniel Kurlin, Vitaliy Univ Liverpool Comp Sci Dept Liverpool Merseyside England

ISBN: (纸本)9798350301298

Rigid structures such as cars or any other solid objects are often represented by finite clouds of unlabeled points. The most natural equivalence on these point clouds is rigid motion or isometry maintaining all inter-point distances. Rigid patterns of point clouds can be reliably compared only by complete isometry invariants that can also be called equivariant descriptors without false negatives (isometric clouds having different descriptions) and without false positives (non-isometric clouds with the same description). Noise and motion in data motivate a search for invariants that are continuous under perturbations of points in a suitable metric. We propose the first continuous and complete invariant of unlabeled clouds in any Euclidean space. For a fixed dimension, the new metric for this invariant is computable in a polynomial time in the number of points.

关键词： detection recognition: Categorization retrieval

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