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共 11281 条记录，以下是611-620 订阅

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Bootstrapping Chest CT Image Understanding by Distilling Knowledge from X-ray Expert Models

Bootstrapping Chest CT Image Understanding by Distilling Kno...

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

作者： Cao, Weiwei Zhang, Jianpeng Xia, Yingda Mok, Tony C. W. Li, Zi Ye, Xianghua Lu, Le Zheng, Jian Tang, Yuxing Zhang, Ling Alibaba Grp DAMO Acad Hangzhou Peoples R China Univ Sci & Technol China Hefei Peoples R China Chinese Acad Sci Suzhou Inst Biomed Engn & Technol Beijing Peoples R China Zhejiang Univ Affiliated Hosp 1 Coll Med Hangzhou Peoples R China Zhejiang Univ Coll Comp Sci & Technol Hangzhou Peoples R China Hupan Lab Hangzhou 310023 Peoples R China

ISBN: (纸本)9798350353006

Radiologists highly desire fully automated versatile AI for medical imaging interpretation. However, the lack of extensively annotated large-scale multi-disease datasets has hindered the achievement of this goal. In this paper, we explore the feasibility of leveraging language as a naturally high-quality supervision for chest CT imaging. In light of the limited availability of image-report pairs, we bootstrap the understanding of 3D chest CT images by distilling chest-related diagnostic knowledge from an extensively pre-trained 2D X-ray expert model. Specifically, we propose a language-guided retrieval method to match each 3D CT image with its semantically closest 2D X-ray image, and perform pair-wise and semantic relation knowledge distillation. Subsequently, we use contrastive learning to align images and reports within the same patient while distinguishing them from the other patients. However, the challenge arises when patients have similar semantic diagnoses, such as healthy patients, potentially confusing if treated as negatives. We introduce a robust contrastive learning that identifies and corrects these false negatives. We train our model with over 12K pairs of chest CT images and radiology reports. Extensive experiments across multiple scenarios, including zero-shot learning, report generation, and fine-tuning processes, demonstrate the model's feasibility in interpreting chest CT images.

关键词： Medical vision language pre-training Report generation

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OPERA: Alleviating Hallucination in Multi-Modal Large Language Models via Over-Trust Penalty and Retrospection-Allocation

OPERA: Alleviating Hallucination in Multi-Modal Large Langua...

引用

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

作者： Hu, Qidong Dong, Xiaoyi Zhang, Pan Wang, Bin He, Conghui Wang, Jiaqi Lin, Dahua Zhang, Weiming Yu, Nenghai Univ Sci & Technol China Anhui Prov Key Lab Digital Secur Hefei Peoples R China Shanghai AI Lab Shanghai Peoples R China Chinese Univ Hong Kong Hong Kong Peoples R China

ISBN: (纸本)9798350353006

Hallucination, posed as a pervasive challenge of multi-modal large language models (MLLMs), has significantly impeded their real-world usage that demands precise judgment. Existing methods mitigate this issue with either training with specific designed data or inferencing with external knowledge from other sources, incurring inevitable additional costs. In this paper, we present OPERA, a novel MLLM decoding method grounded in an Overtrust Penalty and a Retrospection-Allocation strategy, serving as a nearly free lunch to alleviate the hallucination issue without additional data, knowledge, or training. Our approach begins with an interesting observation that, most hallucinations are closely tied to the knowledge aggregation patterns manifested in the self-attention matrix, i.e., MLLMs tend to generate new tokens by focusing on a few summary tokens, but not all the previous tokens. Such partial over-trust inclination results in the neglecting of image tokens and describes the image content with hallucination. Based on the observation, OPERA introduces a penalty term on the model logits during the beam-search decoding to mitigate the over-trust issue, along with a rollback strategy that retrospects the presence of summary tokens in the previ-ously generated tokens, and re-allocate the token selection if necessary. With extensive experiments, OPERA shows significant hallucination-mitigating performance on differ-ent MLLMs and metrics, proving its effectiveness and gen-erality. Our code is at: https://github. com/shikiw/OPERA.

关键词： Hallucination Large vision-language model LLM Multimodal LLM

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Towards Engineered Safe AI with Modular Concept Models

Towards Engineered Safe AI with Modular Concept Models

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

作者： Heidemann, Lena Kurzidem, Iwo Monnet, Maureen Roscher, Karsten Guennemann, Stephan Fraunhofer IKS Munich Germany Tech Univ Munich Munich Germany

ISBN: (纸本)9798350365474

The inherent complexity and uncertainty of Machine Learning (ML) makes it difficult for ML-based computer vision (CV) approaches to become prevalent in safety-critical domains like autonomous driving, despite their high performance. A crucial challenge in these domains is the safety assurance of ML-based systems. To address this, recent safety standardization in the automotive domain has introduced an ML safety lifecycle following an iterative development process. While this approach facilitates safety assurance, its iterative nature requires frequent adaptation and optimization of the ML function, which might include costly retraining of the ML model and is not guaranteed to converge to a safe AI solution. In this paper, we propose a modular ML approach which allows for more efficient and targeted measures to each of the modules and process steps. Each module of the modular concept model represents one visual concept and is aggregated with the other modules' outputs into a task output. The design choices of a modular concept model can be categorized into the selection of the concept modules, the aggregation of their output and the training of the concept modules. Using the example of traffic sign classification, we present each step of the involved design choices and the corresponding targeted measures to take in an iterative development process for engineering safe AI.

关键词： computer vision Concept Models Deep Neural Networks Explainable AI Interpretable Models Machine Learning ML Safety Modular Concept Models Modular Deep Learning Safe AI Safe ML

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Domain Targeted Synthetic Plant Style Transfer using Stable Diffusion, LoRA and ControlNet

Domain Targeted Synthetic Plant Style Transfer using Stable ...

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

作者： Hartley, Zane K. J. Lind, Rob J. Pound, Michael P. French, Andrew P. Univ Nottingham Wollaton Rd Nottingham NG8 1BB England Syngenta Jealotts Hill Int Res Ctr Warfield England

ISBN: (纸本)9798350365474

Synthetic images can help alleviate much of the cost in the creation of training data for plant phenotyping-focused AI development. Synthetic-to-real style transfer is of particular interest to users of artificial data because of the domain shift problem created by training neural networks on images generated in a digital environment. In this paper we present a pipeline for synthetic plant creation and image-to-image style transfer, with a particular interest in synthetic to real domain adaptation targeting specific real datasets. Utilizing new advances in generative AI, we employ a combination of Stable diffusion, Low Ranked Adapters (LoRA) and ControlNets to produce an advanced system of style transfer. We focus our work on the core task of leaf instance segmentation, exploring both synthetic to real style transfer as well as inter-species style transfer and find that our pipeline makes numerous improvements over CycleGAN for style transfer, and the images we produce are comparable to real images when used as training data.

关键词： Agriculture computer vision ControlNet Deep Learning Diffusion LoRA Plant Phenotyping

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Cross-Domain Image Captioning with Discriminative Finetuning

Cross-Domain Image Captioning with Discriminative Finetuning

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

作者： Dessi, Roberto Bevilacqua, Michele Gualdoni, Eleonora Carraz Rakotonirina, Nathanael Franzon, Francesca Baroni, Marco UPF Meta AI Barcelona Spain Samaya AI Mountain View CA USA UPF Barcelona Spain UPF ICREA Barcelona Spain

ISBN: (纸本)9798350301298

Neural captioners are typically trained to mimic human-generated references without optimizing for any specific communication goal, leading to problems such as the generation of vague captions. In this paper, we show that fine-tuning an out-of-the-box neural captioner with a self-supervised discriminative communication objective helps to recover a plain, visually descriptive language that is more informative about image contents. Given a target image, the system must learn to produce a description that enables an out-of-the-box text-conditioned image retriever to identify such image among a set of candidates. We experiment with the popular ClipCap captioner, also replicating the main results with BLIP. In terms of similarity to ground-truth human descriptions, the captions emerging from discriminative finetuning lag slightly behind those generated by the non-finetuned model, when the latter is trained and tested on the same caption dataset. However, when the model is used without further tuning to generate captions for out-of-domain datasets, our discriminatively-finetuned captioner generates descriptions that resemble human references more than those produced by the same captioner wihtout finetuning. We further show that, on the Conceptual Captions dataset, discriminatively finetuned captions are more helpful than either vanilla ClipCap captions or ground-truth captions for human annotators tasked with an image discrimination task.(1)

关键词： language reasoning vision

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Object Detection with Self-Supervised Scene Adaptation

Object Detection with Self-Supervised Scene Adaptation

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

作者： Zhang, Zekun Hoai, Minh SUNY Stony Brook Stony Brook NY 11794 USA VinAI Res Hanoi Vietnam

ISBN: (纸本)9798350301298

This paper proposes a novel method to improve the performance of a trained object detector on scenes with fixed camera perspectives based on self-supervised adaptation. Given a specific scene, the trained detector is adapted using pseudo-ground truth labels generated by the detector itself and an object tracker in a cross-teaching manner. When the camera perspective is fixed, our method can utilize the background equivariance by proposing artifact-free object mixup as a means of data augmentation, and utilize accurate background extraction as an additional input modality. We also introduce a large-scale and diverse dataset for the development and evaluation of scene-adaptive object detection. Experiments on this dataset show that our method can improve the average precision of the original detector, outperforming the previous state-of-the-art selfsupervised domain adaptive object detection methods by a large margin. Our dataset and code are published at https://***/cvlab-stonybrook/scenes100.

关键词： detection recognition: Categorization retrieval

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Learning Attribute and Class-Specific Representation Duet for Fine-grained Fashion Analysis

Learning Attribute and Class-Specific Representation Duet fo...

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

作者： Jiao, Yang Gao, Yan Meng, Jingjing Shang, Tin Sun, Yi Amazon Seattle WA 98109 USA

ISBN: (纸本)9798350301298

Fashion representation learning involves the analysis and understanding of various visual elements at different granularities and the interactions among them. Existing works often learn fine-grained fashion representations at the attribute level without considering their relationships and inter-dependencies across different classes. In this work, we propose to learn an attribute and class-specific fashion representation duet to better model such attribute relationships and inter-dependencies by leveraging prior knowledge about the taxonomy of fashion attributes and classes. Through two sub-networks for the attributes and classes, respectively, our proposed an embedding network progressively learns and refines the visual representation of a fashion image to improve its robustness for fashion retrieval. A multi-granularity loss consisting of attribute-level and class-level losses is proposed to introduce appropriate inductive bias to learn across different granularities of the fashion representations. Experimental results on three benchmark datasets demonstrate the effectiveness of our method, which outperforms the state-of-the-art methods by a large margin.

关键词： detection recognition: Categorization retrieval

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Drone-HAT: Hybrid Attention Transformer for Complex Action recognition in Drone Surveillance Videos

Drone-HAT: Hybrid Attention Transformer for Complex Action R...

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

作者： Khan, Mustaqeem Ahmad, Jamil El Saddik, Abdulmotaleb Gueaieb, Wail De Masi, Giulia Karray, Fakhri MBZUAI Abu Dhabi U Arab Emirates Univ Ottawa Ottawa ON Canada Technol Innovat Inst Abu Dhabi U Arab Emirates Univ Waterloo Waterloo ON Canada

ISBN: (纸本)9798350365474

Ultra-high-resolution aerial videos are becoming increasingly popular for enhancing surveillance capabilities in sparsely populated areas. However, analyzing human activities automatically, such as "who is doing what?" in these videos, is desirable to realize their surveillance potential. In contrast, atomic visual action detection has successfully recognized such activities in movie data. However, adapting it to ultra-high resolution aerial videos is challenging because the target persons appear relatively tiny from overhead views and are sparsely located. Additionally, existing atomic visual action detection methods are based on single-label actions. However, people can perform multiple actions simultaneously, so a multi-label approach would be more appropriate. To address these problems, we propose a multi-label action detection/recognition framework using a hybrid attention vision transformer (HAT) to recognize recurrent actions more efficiently. Additionally, a multi-scale, multi-granularity module inside the action recognition transformer block extracts relevant features without redundancy. Using the Okutama Dataset, we demonstrated that our method performs better than existing state-of-the-art methodologies for interpreting aerial videos for human activity.

关键词： Aerial Surveillance Hybrid Attention Transformer Multi-granularity and Multi-scale Fusion Multi-label Action recognition

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ZInD-Tell: Towards Translating Indoor Panoramas into Descriptions

ZInD-Tell: Towards Translating Indoor Panoramas into Descrip...

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

作者： Deb, Tonmoay Wang, Lichen Bessinger, Zachary Khosravan, Naji Penner, Eric Kang, Sing Bing Northwestern Univ Evanston IL 60208 USA Zillow Grp Seattle WA USA

ISBN: (纸本)9798350365474

This paper focuses on bridging the gap between natural language descriptions, 360 degrees panoramas, room shapes, and layouts/floorplans of indoor spaces. To enable new multimodal (image, geometry, language) research directions in indoor environment understanding, we propose a novel extension to the Zillow Indoor Dataset (ZInD) which we call ZInD-Tell1. We first introduce an effective technique for extracting geometric information from ZInD's raw structural data, which facilitates the generation of accurate ground truth descriptions using GPT-4. A human-in-the-loop approach is then employed to ensure the quality of these descriptions. To demonstrate the vast potential of our dataset, we introduce the ZInD-Tell benchmark, focusing on two exemplary tasks: language-based home retrieval and indoor description generation. Furthermore, we propose an end-to-end, zero-shot baseline model, ZInD-Agent, designed to process an unordered set of panorama images and generate home descriptions. ZInD-Agent outperforms naive methods in both tasks, hence, can be considered as a complement to the naive to show potential use of the data and impact of geometry. We believe this work initiates new trajectories in leveraging computer vision techniques to analyze indoor panorama images descriptively by learning the latent relation between vision, geometry, and language modalities.

关键词： computer vision

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MADTP: Multimodal Alignment-Guided Dynamic Token Pruning for Accelerating vision-Language Transformer

MADTP: Multimodal Alignment-Guided Dynamic Token Pruning for...

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

作者： Cao, Jianjian Ye, Peng Li, Shengze Yu, Chong Tang, Yansong Lu, Jiwen Chen, Tao Fudan Univ Sch Informat Sci & Technol Shanghai Peoples R China Fudan Univ Acad Engn & Technol Shanghai Peoples R China Tsinghua Univ Tsinghua Shenzhen Int Grad Sch Beijing Peoples R China Tsinghua Univ Dept Automat Beijing Peoples R China

ISBN: (纸本)9798350353006

vision-Language Transformers (VLTs) have shown great success recently, but are meanwhile accompanied by heavy computation costs, where a major reason can be attributed to the large number of visual and language tokens. Existing token pruning research for compressing VLTs mainly follows a single-modality-based scheme yet ignores the critical role of aligning different modalities for guiding the token pruning process, causing the important tokens for one modality to be falsely pruned in another modality branch. Meanwhile, existing VLT pruning works also lack the flexibility to dynamically compress each layer based on different input samples. To this end, we propose a novel framework named Multimodal Alignment-Guided Dynamic Token Pruning (MADTP) for accelerating various VLTs. Specifically, we first introduce a well-designed Multi-modality Alignment Guidance (MAG) module that can align features of the same semantic concept from different modalities, to ensure the pruned tokens are less important for all modalities. We further design a novel Dynamic Token Pruning (DTP) module, which can adaptively adjust the token compression ratio in each layer based on different input instances. Extensive experiments on various benchmarks demonstrate that MADTP significantly reduces the computational complexity of kinds of multimodal models while preserving competitive performance. Notably, when applied to the BLIP model in the NLVR2 dataset, MADTP can reduce the GFLOPs by 80% with less than 4% performance degradation. The code is available at https://***/double125/MADTP.

关键词： Model Compress Token Pruning

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