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Decomposing Disease Descriptions for Enhanced Pathology Detection: A Multi-Aspect vision-Language Pre-training Framework

Decomposing Disease Descriptions for Enhanced Pathology Dete...

引用

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

作者： Vu Minh Hieu Phan Xie, Yutong Qi, Yuankai Liu, Linggiao Liu, Liyang Zhang, Bowen Liao, Zhibin Wu, Qi To, Minh-Son Verjans, Johan W. Univ Adelaide Australian Inst Machine Learning Adelaide SA Australia Macquarie Univ Sydney NSW Australia Flinders Univ S Australia Adelaide SA Australia

ISBN: (纸本)9798350353006

Medical vision language pre-training (VLP) has emerged as a frontier of research, enabling zero-shot pathological recognition by comparing the query image with the textual descriptions for each disease. Due to the complex semantics of biomedical texts, current methods struggle to align medical images with key pathological findings in unstructured reports. This leads to the misalignment with the target disease's textual representation. In this paper, we introduce a novel VLP framework designed to dissect disease descriptions into their fundamental aspects, leveraging prior knowledge about the visual manifestations of pathologies. This is achieved by consulting a large language model and medical experts. Integrating a Transformer module, our approach aligns an input image with the diverse elements of a disease, generating aspect-centric image representations. By consolidating the matches from each aspect, we improve the compatibility between an image and its associated disease. Additionally, capitalizing on the aspect-oriented representations, we present a dual-head Transformer tailored to process known and unknown diseases, optimizing the comprehensive detection efficacy. Conducting experiments on seven downstream datasets, ours improves the accuracy of recent methods by up to 8.56% and 17.26% for seen and unseen categories, respectively. Our code is released at https://***/HieuPhan33/MAVL.

关键词： Medical vision-language pre-training vision-language pre-training Visual grounding Zero-shot classification

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GeneAvatar: Generic Expression-Aware Volumetric Head Avatar Editing from a Single Image

GeneAvatar: Generic Expression-Aware Volumetric Head Avatar ...

引用

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

作者： Bao, Chong Zhang, Yinda Li, Yuan Zhang, Xiyu Yang, Bangbang Bao, Hujun Pollefeys, Marc Zhang, Guofeng Cui, Zhaopeng Zhejiang Univ State Key Lab CAD & CG Hangzhou Peoples R China Google Mountain View CA 94043 USA Swiss Fed Inst Technol Zurich Switzerland ByteDance Beijing Peoples R China

ISBN: (纸本)9798350353013;9798350353006

Recently, we have witnessed the explosive growth of various volumetric representations in modeling animatable head avatars. However, due to the diversity of frameworks, there is no practical method to support high-level applications like 3D head avatar editing across different representations. In this paper, we propose a generic avatar editing approach that can be universally applied to various 3DMM-driving volumetric head avatars. To achieve this goal, we design a novel expression-aware modification generative model, which enables lift 2D editing from a single image to a consistent 3D modification field. To ensure the effectiveness of the generative modification process, we develop several techniques, including an expression-dependent modification distillation scheme to draw knowledge from the large-scale head avatar model and 2D facial texture editing tools, implicit latent space guidance to enhance model convergence, and a segmentation-based loss reweight strategy for fine-grained texture inversion. Extensive experiments demonstrate that our method delivers high-quality and consistent results across multiple expression and viewpoints. Project page: https://***/geneavatar/.

关键词： 3D computer vision avatar editing head avatar neural rendering

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Enhancing Traffic Safety with Parallel Dense Video Captioning for End-to-End Event Analysis

Enhancing Traffic Safety with Parallel Dense Video Captionin...

引用

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

作者： Shoman, Maged Wang, Dongdong Aboah, Armstrong Abdel-Aty, Mohamed Univ Cent Florida Dept Civil Environm & Construct Engn Smart & Safe Transportat SST Lab Orlando FL 32816 USA North Dakota State Univ Dept Civil Construct & Environm Engn Fargo ND USA Univ Cent Florida Joint Appointment Dept Comp Sci Dept Civil Environm & Construct Engn Smart & Safe Transportat SST Lab Orlando FL USA

ISBN: (纸本)9798350365474

This paper introduces our solution for Track 2 in AI City Challenge 2024. The task aims to solve traffic safety description and analysis with the dataset of Woven Traffic Safety (WTS), a real-world Pedestrian-Centric Traffic Video Dataset for Fine-grained Spatial-Temporal Understanding. Our solution mainly focuses on the following points: 1) To solve dense video captioning, we leverage the framework of dense video captioning with parallel decoding (PDVC) to model visual-language sequences and generate dense caption by chapters for video. 2) Our work leverages CLIP to extract visual features to more efficiently perform cross-modality training between visual and textual representations. 3) We conduct domain-specific model adaptation to mitigate domain shift problem that poses recognition challenge in video understanding. 4) Moreover, we leverage BDD-5K captioned videos to conduct knowledge transfer for better understanding WTS videos and more accurate captioning. Our solution has yielded on the test set, achieving 6th place in the competition. The opensource code will be available at https://***/UCF-SST-Lab/AICity2024CVPRW

关键词： computer vision cross-modality learning dense video captioning domain adaptation natural language processing

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AAPL: Adding Attributes to Prompt Learning for vision-Language Models

AAPL: Adding Attributes to Prompt Learning for Vision-Langua...

引用

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

作者： Kim, Gahyeon Kim, Sohee Lee, Seokju Korea Inst Energy Technol KENTECH Naju South Korea

ISBN: (纸本)9798350365474

Recent advances in large pre-trained vision-language models have demonstrated remarkable performance on zero-shot downstream tasks. Building upon this, recent studies, such as CoOp and CoCoOp, have proposed the use of prompt learning, where context within a prompt is replaced with learnable vectors, leading to significant improvements over manually crafted prompts. However, the performance improvement for unseen classes is still marginal, and to tackle this problem, data augmentation has been frequently used in traditional zero-shot learning techniques. Through our experiments, we have identified important issues in CoOp and CoCoOp: the context learned through traditional image augmentation is biased toward seen classes, negatively impacting generalization to unseen classes. To address this problem, we propose adversarial token embedding to disentangle low-level visual augmentation features from highlevel class information when inducing bias in learnable prompts. Through our novel mechanism called "Adding Attributes to Prompt Learning", AAPL, we guide the learnable context to effectively extract text features by focusing on high-level features for unseen classes. We have conducted experiments across 11 datasets, and overall, AAPL shows favorable performances compared to the existing methods in few-shot learning, zero-shot learning, cross-dataset, and domain generalization tasks.

关键词： prompt learning vision language model VLMs

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RegionPLC: Regional Point-Language Contrastive Learning for Open-World 3D Scene Understanding

RegionPLC: Regional Point-Language Contrastive Learning for ...

引用

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

作者： Yang, Jihan Ding, Runyu Deng, Weipeng Wang, Zhe Qi, Xiaojuan Univ Hong Kong Hong Kong Peoples R China SenseTime Res Hong Kong Peoples R China

ISBN: (纸本)9798350353006

We propose a lightweight and scalable Regional Point-Language Contrastive learning framework, namely RegionPLC, for open-world 3D scene understanding, aiming to identify and recognize open-set objects and categories. Specifically, based on our empirical studies, we introduce a 3D-aware SFusion strategy that fuses 3D vision-language pairs derived from multiple 2D foundation models, yielding high-quality, dense region-level language descriptions without human 3D annotations. Subsequently, we devise a region-aware point-discriminative contrastive learning objective to enable robust and effective 3D learning from dense regional language supervision. We carry out extensive experiments on ScanNet, ScanNet200, and nuScenes datasets, and our model outperforms prior 3D open-world scene understanding approaches by an average of 17.2% and 9.1% for semantic and instance segmentation, respectively, while maintaining greater scalability and lower resource demands. Furthermore, our method has the flexibility to be effortlessly integrated with language models to enable open-ended grounded 3D reasoning without extra task-specific training. Code will be released at github.

关键词： 3D vision embodied AI Open-world

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Beyond the Screen: Evaluating Deepfake Detectors under Moire pattern Effects

Beyond the Screen: Evaluating Deepfake Detectors under Moire...

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

作者： Tariq, Razaib Heo, Minji Woo, Simon S. Tariq, Shahroz Sungkyunkwan Univ Seoul South Korea CSIROs Data61 Eveleigh Australia

ISBN: (纸本)9798350365474

The detection of deepfakes is crucial for mitigating the societal impact of falsified video content. Despite the development of various algorithms for this purpose, challenges arise for detectors in real-world scenarios, especially when users capture deepfake content from screens and upload it online or when detectors operate on external devices like smartphones, requiring the capture of potential deepfakes through the camera for evaluation. A significant challenge in these scenarios is the presence of Moire patterns, which degrade image quality and complicate conventional classification methods, notably deep neural networks (DNNs). However, the impact of Moire patterns on the effectiveness of deepfake detection systems has not been adequately explored. This study aims to investigate how capturing deepfake videos via digital screen cameras affects the accuracy of detection mechanisms. We introduced the Moire patterns by capturing the display of a monitor using a smartphone camera and conducted empirical evaluations using four widely recognized datasets: CelebDF, DFD, DFDC, and FF++. We compare the performance of twelve SOTA detectors on deepfake videos captured under the influence of Moire patterns. Our findings reveal a performance decrease of up to 33.1 and 31.3 percentage points for image- and video-based detectors. Therefore, highlighting the challenges posed by Moire patterns and other naturally induced artifacts is critical for improving the effectiveness of real-world deepfake detection efforts. To facilitate further research, we will release the Moire pattern impact version of CelebDF, DFD, DFDC, and FF++ datasets with this paper. Our code is available here: https: //***/Razaib-Tariq/deepmoire

关键词： Deepfake Fake Media Media Forensics Moire pattern

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StableVITON: Learning Semantic Correspondence with Latent Diffusion Model for Virtual Try-On

StableVITON: Learning Semantic Correspondence with Latent Di...

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

作者： Kim, Jeongho Gu, Gyojung Park, Minho Park, Sunghyun Choo, Jaegul Korea Adv Inst Sci & Technol Daejeon South Korea

ISBN: (纸本)9798350353013;9798350353006

Given a clothing image and a person image, an image-based virtual try-on aims to generate a customized image that appears natural and accurately reflects the characteristics of the clothing image. In this work, we aim to expand the applicability of the pre-trained diffusion model so that it can be utilized independently for the virtual try-on task. The main challenge is to preserve the clothing details while effectively utilizing the robust generative capability of the pre-trained model. In order to tackle these issues, we propose StableVITON, learning the semantic correspondence between the clothing and the human body within the latent space of the pre-trained diffusion model in an end-to-end manner. Our proposed zero cross-attention blocks not only preserve the clothing details by learning the semantic correspondence but also generate high-fidelity images by utilizing the inherent knowledge of the pre-trained model in the warping process. Through our proposed novel attention total variation loss and applying augmentation, we achieve the sharp attention map, resulting in a more precise representation of clothing details. StableVITON out-performs the baselines in qualitative and quantitative eval-uation, showing promising quality in arbitrary person images. Our code is available at https://***/ rlawjdghek/StableVITON.

关键词： computer vision Diffusion Model Virtual Try-On

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PIGEON: Predicting Image Geolocations

PIGEON: Predicting Image Geolocations

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

作者： Haas, Lukas Skreta, Michal Alberti, Silas Finn, Chelsea Stanford Univ Stanford CA 94305 USA

ISBN: (纸本)9798350353006

Planet-scale image geolocalization remains a challenging problem due to the diversity of images originating from anywhere in the world. Although approaches based on vision transformers have made significant progress in geolocalization accuracy, success in prior literature is constrained to narrow distributions of images of landmarks, and performance has not generalized to unseen places. We present a new geolocalization system that combines semantic geocell creation, multi-task contrastive pretraining, and a novel loss function. Additionally, our work is the first to perform retrieval over location clusters for guess refinements. We train two models for evaluations on street-level data and general-purpose image geolocalization;the first model, PIGEON, is trained on data from the game of GeoGuessr and is capable of placing over 40% of its guesses within 25 kilometers of the target location globally. We also develop a bot and deploy PIGEON in a blind experiment against humans, ranking in the top 0.01% of players. We further challenge one of the world's foremost professional GeoGuessr players to a series of six matches with millions of viewers, winning all six games. Our second model, PIGEOTTO, differs in that it is trained on a dataset of images from Flickr and Wikipedia, achieving state-of-the-art results on a wide range of image geolocalization benchmarks, outperforming the previous SOTA by up to 7.7 percentage points on the city accuracy level and up to 38.8 percentage points on the country level. Our findings suggest that PIGEOTTO is the first image geolocalization model that effectively generalizes to unseen places and that our approach can pave the way for highly accurate, planet-scale image geolocalization systems. Our code is available on GitHub.(1)

关键词： Blind Experiment CLIP Clustering computer vision GeoGuessr Haversine Image Geolocalization Location Refinement Multi-Modal Multi-Task Multi-Task Pretraining Photo Geolocalization Pretraining Retrieval Semantic Geocells Visual Place recognition Voronoi

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Lift3D: Zero-Shot Lifting of Any 2D vision Model to 3D

Lift3D: Zero-Shot Lifting of Any 2D Vision Model to 3D

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

作者： Varma, Mukund T. Wang, Peihao Fan, Zhiwen Wang, Zhangyang Su, Hao Ramamoorthi, Ravi Univ Calif San Diego La Jolla CA 92093 USA Univ Texas Austin Austin TX USA

ISBN: (纸本)9798350353006

In recent years, there has been an explosion of 2D vision models for numerous tasks such as semantic segmentation, style transfer or scene editing, enabled by large-scale 2D image datasets. At the same time, there has been renewed interest in 3D scene representations such as neural radiance fields from multi-view images. However, the availability of 3D or multiview data is still substantially limited compared to 2D image datasets, making extending 2D vision models to 3D data highly desirable but also very challenging. Indeed, extending a single 2D vision operator like scene editing to 3D typically requires a highly creative method specialized to that task and often requires per-scene optimization. In this paper, we ask the question of whether any 2D vision model can be lifted to make 3D consistent predictions. We answer this question in the affirmative;our new Lift3D method trains to predict unseen views on feature spaces generated by a few visual models (i.e. DINO and CLIP), but then generalizes to novel vision operators and tasks, such as style transfer, super-resolution, open vocabulary segmentation and image colorization;for some of these tasks, there is no comparable previous 3D method. In many cases, we even outperform

关键词： Semantic Segmentation

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GLID: Pre-training a Generalist Encoder-Decoder vision Model

GLID: Pre-training a Generalist Encoder-Decoder Vision Model

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

作者： Liu, Jihao Zheng, Jinliang Liu, Yu Li, Hongsheng CUHK MMLab Hong Kong Peoples R China SenseTime Res Hong Kong Peoples R China Shanghai AI Lab Shanghai Peoples R China CPII InnoHK Hong Kong Peoples R China Tsinghua Univ Inst AI Ind Res AIR Shanghai Peoples R China

ISBN: (纸本)9798350353006

This paper proposes a GeneraLIst encoder-Decoder (GLID) pre-training method for better handling various downstream computer vision tasks. While self-supervised pre-training approaches, e.g., Masked Autoencoder, have shown success in transfer learning, task-specific sub-architectures are still required to be appended for different downstream tasks, which cannot enjoy the benefits of large-scale pre-training. GLID overcomes this challenge by allowing the pre-trained generalist encoder-decoder to be fine-tuned on various vision tasks with minimal task-specific architecture modifications. In the GLID training scheme, pre-training pretext task and other downstream tasks are modeled as "query-to-answer" problems, including the pre-training pretext task and other downstream tasks. We pre-train a task-agnostic encoder-decoder with query-mask pairs. During fine-tuning, GLID maintains the pre-trained encoder-decoder and queries, only replacing the topmost linear transformation layer with task-specific linear heads. This minimizes the pretrain-finetune architecture inconsistency and enables the pre-trained model to better adapt to downstream tasks. GLID achieves competitive performance on various vision tasks, including object detection, image segmentation, pose estimation, and depth estimation, outper-forming or matching specialist models such as Mask2Former, DETR, ViTPose, and BinsFormer.

关键词： Image segmentation

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