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Unifying vision, Text, and Layout for Universal Document Processing

Unifying Vision, Text, and Layout for Universal Document Pro...

引用

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

作者： Tang, Zineng Yang, Ziyi Wang, Guoxin Fang, Yuwei Liu, Yang Zhu, Chenguang Zeng, Michael Zhang, Cha Bansal, Mohit Univ North Carolina Chapel Hill Chapel Hill NC 27599 USA Microsoft Azure Cognit Serv Res Redmond WA 98052 USA Microsoft Azure Visual Document Intelligence Redmond WA USA

ISBN: (纸本)9798350301298

We propose Universal Document Processing (UDOP), a foundation Document AI model which unifies text, image, and layout modalities together with varied task formats, including document understanding and generation. UDOP leverages the spatial correlation between textual content and document image to model image, text, and layout modalities with one uniform representation. With a novel vision-Text-Layout Transformer, UDOP unifies pretraining and multi-domain downstream tasks into a prompt-based sequence generation scheme. UDOP is pretrained on both large-scale unlabeled document corpora using innovative self-supervised objectives and diverse labeled data. UDOP also learns to generate document images from text and layout modalities via masked image reconstruction. To the best of our knowledge, this is the first time in the field of document AI that one model simultaneously achieves high-quality neural document editing and content customization. Our method sets the state-of-the-art on 8 Document AI tasks, e.g., document understanding and QA, across diverse data domains like finance reports, academic papers, and websites. UDOP ranks first on the leaderboard of the Document Understanding Benchmark.(1)

关键词： Document analysis and understanding

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Mobile User Interface Element Detection Via Adaptively Prompt Tuning

Mobile User Interface Element Detection Via Adaptively Promp...

引用

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

作者： Gu, Zhangxuan Xu, Zhuoer Chen, Haoxing Lan, Jun Meng, Changhua Wang, Weiqiang Tiansuan Lab Ant Grp Hangzhou Peoples R China

ISBN: (纸本)9798350301298

Recent object detection approaches rely on pretrained vision-language models for image-text alignment. However, they fail to detect the Mobile User Interface (MUI) element since it contains additional OCR information, which describes its content and function but is often ignored. In this paper, we develop a new MUI element detection dataset named MUI-zh and propose an Adaptively Prompt Tuning (APT) module to take advantage of discriminating OCR information. APT is a lightweight and effective module to jointly optimize category prompts across different modalities. For every element, APT uniformly encodes its visual features and OCR descriptions to dynamically adjust the representation of frozen category prompts. We evaluate the effectiveness of our plug-and-play APT upon several existing CLIP-based detectors for both standard and open-vocabulary MUI element detection. Extensive experiments show that our method achieves considerable improvements on two datasets. The datasets is available at github. com/antmachineintelligence/MUI-zh.

关键词： language reasoning vision

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Discrete Point-wise Attack Is Not Enough: Generalized Manifold Adversarial Attack for Face recognition

Discrete Point-wise Attack Is Not Enough: Generalized Manifo...

引用

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

作者： Li, Qian Hu, Yuxiao Liu, Ye Zhang, Dongxiao Jin, Xin Chen, Yuntian Eastern Inst Adv Study Ningbo Zhejiang Peoples R China

ISBN: (纸本)9798350301298

Classical adversarial attacks for Face recognition (FR) models typically generate discrete examples for target identity with a single state image. However, such paradigm of point-wise attack exhibits poor generalization against numerous unknown states of identity and can be easily defended. In this paper, by rethinking the inherent relationship between the face of target identity and its variants, we introduce a new pipeline of Generalized Manifold Adversarial Attack (GMAA)(1) to achieve a better attack performance by expanding the attack range. Specifically, this expansion lies on two aspects - GMAA not only expands the target to be attacked from one to many to encourage a good generalization ability for the generated adversarial examples, but it also expands the latter from discrete points to manifold by leveraging the domain knowledge that face expression change can be continuous, which enhances the attack effect as a data augmentation mechanism did. Moreover, we further design a dual supervision with local and global constraints as a minor contribution to improve the visual quality of the generated adversarial examples. We demonstrate the effectiveness of our method based on extensive experiments, and reveal that GMAA promises a semantic continuous adversarial space with a higher generalization ability and visual quality.

关键词： Adversarial attack and defense

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Scattering Prompt Tuning: A Fine-tuned Foundation Model for SAR Object recognition

Scattering Prompt Tuning: A Fine-tuned Foundation Model for ...

引用

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

作者： Guo, Weilong Li, Shengyang Yang, Jian Chinese Acad Sci Key Lab Space Utilizat Beijing 100864 Peoples R China Chinese Acad Sci Technol & Engn Ctr Space Utilizat Beijing 100864 Peoples R China Univ Chinese Acad Sci Beijing Peoples R China

ISBN: (纸本)9798350365474

Synthetic Aperture Radar (SAR) serves as a vital tool in various earth observation applications, providing robust imaging under challenging weather conditions. While the fine-tuned foundation models excel in many downstream tasks, they struggle with SAR object recognition because of SAR's unique imaging and scattering characteristics. In this study, we propose a novel approach named Scattering Prompt Tuning (SPT) based vision foundation model. It uses SAR image scattering information as a prompt and integrates learnable parameters into the pre-trained model's input space to help learn SAR's unique information. We also employ a lightweight Residual AdapterMLP for fine-tuning, design a Sequential Feature Aggregation (SFA) to selectively fuse features from different transformer blocks effectively, and develop a Dynamic Distributional Contrast loss (DCLoss) to maintain the proper distance between different objects in feature space. Additionally, a four-stage training strategy, incorporating semi-supervised learning, is deployed to enhance SAR object recognition performance further. Our approach reaches a Top-1 accuracy of 37.9% and an AUROC of 0.83 on the final dataset, winning the first place in the SAR Classification track of PBVS 2024 Multi-modal Aerial View Object Classification Challenge, which is better than the latest advanced fine-tuned foundation models.

关键词： Fine-tuned Foundation Model Object recognition SAR

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Block Selective Reprogramming for On-device Training of vision Transformers

Block Selective Reprogramming for On-device Training of Visi...

引用

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

作者： Sarkar, Sreetama Kundu, Souvik Zheng, Kai Beerel, Peter A. Univ Southern Calif Los Angeles CA 90007 USA Intel Labs San Diego CA USA

ISBN: (纸本)9798350365474

The ubiquity of vision transformers (ViTs) for various edge applications, including personalized learning, has created the demand for on-device fine-tuning. However, training with the limited memory and computation power of edge devices remains a significant challenge. In particular, the memory required for training is much higher than that needed for inference, primarily due to the need to store activations across all layers in order to compute the gradients needed for weight updates. Previous works have explored reducing this memory requirement via frozen-weight training as well storing the activations in a compressed format. However, these methods are deemed inefficient due to their inability to provide training or inference speedup. In this paper, we first investigate the limitations of existing on-device training methods aimed at reducing memory and compute requirements. We then present block selective reprogramming (BSR) in which we fine-tune only a fraction of total blocks of a pre-trained model and selectively drop tokens based on self-attention scores of the frozen layers. To show the efficacy of BSR, we present extensive evaluations on ViT-B and DeiT-S with five different datasets. Compared to the existing alternatives, our approach simultaneously reduces training memory by up to 1.4x and compute cost by up to 2x while maintaining similar accuracy. We also showcase results for Mixture-of-Expert (MoE) models, demonstrating the effectiveness of our approach in multitask learning scenarios. Code will be available at: https://***/sreetamasarkar/BSR.

关键词： on-device training token pruning vision transformer

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Generalized Deep 3D Shape Prior via Part-Discretized Diffusion Process

Generalized Deep 3D Shape Prior via Part-Discretized Diffusi...

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

作者： Li, Yuhan Dou, Yishun Chen, Xuanhong Ni, Bingbing Sun, Yilin Liu, Yutian Wang, Fuzhen Shanghai Jiao Tong Univ Shanghai 200240 Peoples R China Huawei Shenzhen Peoples R China

ISBN: (纸本)9798350301298

We develop a generalized 3D shape generation prior model, tailored for multiple 3D tasks including unconditional shape generation, point cloud completion, and cross-modality shape generation, etc. On one hand, to precisely capture local fine detailed shape information, a vector quantized variational autoencoder (VQ-VAE) is utilized to index local geometry from a compactly learned code-book based on a broad set of task training data. On the other hand, a discrete diffusion generator is introduced to model the inherent structural dependencies among different tokens. In the meantime, a multi-frequency fusion module (MFM) is developed to suppress high-frequency shape feature fluctuations, guided by multi-frequency contextual information. The above designs jointly equip our proposed 3D shape prior model with high-fidelity, diverse features as well as the capability of cross-modality alignment, and extensive experiments have demonstrated superior performances on various 3D shape generation tasks.

关键词： vision + graphics

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DEGPR: Deep Guided Posterior Regularization for Multi-Class Cell Detection and Counting

DEGPR: Deep Guided Posterior Regularization for Multi-Class ...

引用

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

作者： Tyagi, Aayush Kumar Mohapatra, Chirag Das, Prasenjit Makharia, Govind Mehra, Lalita Prathosh, A. P. Mausam IIT Delhi New Delhi India IISc Bangalore Karnataka India AIIMS New Delhi India

ISBN: (纸本)9798350301298

Multi-class cell detection and counting is an essential task for many pathological diagnoses. Manual counting is tedious and often leads to inter-observer variations among pathologists. While there exist multiple, general-purpose, deep learning-based object detection and counting methods, they may not readily transfer to detecting and counting cells in medical images, due to the limited data, presence of tiny overlapping objects, multiple cell types, severe class-imbalance, minute differences in size/shape of cells, etc. In response, we propose guided posterior regularization (DEGPR), which assists an object detector by guiding it to exploit discriminative features among cells. The features may be pathologist-provided or inferred directly from visual data. We validate our model on two publicly available datasets (CoNSeP and MoNuSAC), and on MuCeD, a novel dataset that we contribute. MuCeD consists of 55 biopsy images of the human duodenum for predicting celiac disease. We perform extensive experimentation with three object detection baselines on three datasets to show that DEGPR is model-agnostic, and consistently improves baselines obtaining up to 9% (absolute) mAP gains.

关键词： cell microscopy Medical and biological vision

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Weakly-supervised Single-view Image Relighting

Weakly-supervised Single-view Image Relighting

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

作者： Yi, Renjiao Zhu, Chenyang Xu, Kai Natl Univ Def Technol Changsha Hunan Peoples R China

ISBN: (纸本)9798350301298

We present a learning-based approach to relight a single image of Lambertian and low-frequency specular objects. Our method enables inserting objects from photographs into new scenes and relighting them under the new environment lighting, which is essential for AR applications. To relight the object, we solve both inverse rendering and re-rendering. To resolve the ill-posed inverse rendering, we propose a weakly-supervised method by a low-rank constraint. To facilitate the weakly-supervised training, we contribute Relit, a large-scale (750K images) dataset of videos with aligned objects under changing illuminations. For re-rendering, we propose a differentiable specular rendering layer to render low-frequency non-Lambertian materials under various illuminations of spherical harmonics. The whole pipeline is end-to-end and efficient, allowing for a mobile app implementation of AR object insertion. Extensive evaluations demonstrate that our method achieves state-of-the-art performance. Project page: https://***/relighting/.

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

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A Practical Upper Bound for the Worst-Case Attribution Deviations

A Practical Upper Bound for the Worst-Case Attribution Devia...

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

作者： Wang, Fan Kong, Adams Wai-Kin Nanyang Technol Univ Sch Comp Sci & Engn Singapore Singapore Nanyang Technol Univ Rapid Rich Object Search ROSE Lab IGP Singapore Singapore

ISBN: (纸本)9798350301298

Model attribution is a critical component of deep neural networks (DNNs) for its interpretability to complex models. Recent studies bring up attention to the security of attribution methods as they are vulnerable to attribution attacks that generate similar images with dramatically different attributions. Existing works have been investigating empirically improving the robustness of DNNs against those attacks;however, none of them explicitly quantifies the actual deviations of attributions. In this work, for the first time, a constrained optimization problem is formulated to derive an upper bound that measures the largest dissimilarity of attributions after the samples are perturbed by any noises within a certain region while the classification results remain the same. Based on the formulation, different practical approaches are introduced to bound the attributions above using Euclidean distance and cosine similarity under both l(2) and l(infinity)-norm perturbations constraints. The bounds developed by our theoretical study are validated on various datasets and two different types of attacks (PGD attack and IFIA attribution attack). Over 10 million attacks in the experiments indicate that the proposed upper bounds effectively quantify the robustness of models based on the worst-case attribution dissimilarities.

关键词： Explainable computer vision

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Slimmable Dataset Condensation

Slimmable Dataset Condensation

引用

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

作者： Liu, Songhua Ye, Jingwen Yu, Runpeng Wang, Xinchao Natl Univ Singapore Singapore Singapore

ISBN: (纸本)9798350301298

Dataset distillation, also known as dataset condensation, aims to compress a large dataset into a compact synthetic one. Existing methods perform dataset condensation by assuming a fixed storage or transmission budget. When the budget changes, however, they have to repeat the synthesizing process with access to original datasets, which is highly cumbersome if not infeasible at all. In this paper, we explore the problem of slimmable dataset condensation, to extract a smaller synthetic dataset given only previous condensation results. We first study the limitations of existing dataset condensation algorithms on such a successive compression setting and identify two key factors: (1) the inconsistency of neural networks over different compression times and (2) the underdetermined solution space for synthetic data. Accordingly, we propose a novel training objective for slimmable dataset condensation to explicitly account for both factors. Moreover, synthetic datasets in our method adopt a significance-aware parameterization. Theoretical derivation indicates that an upper-bounded error can be achieved by discarding the minor components without training. Alternatively, if training is allowed, this strategy can serve as a strong initialization that enables a fast convergence. Extensive comparisons and ablations demonstrate the superiority of the proposed solution over existing methods on multiple benchmarks.

关键词： Efficient and scalable vision

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