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RegionGPT: Towards Region Understanding vision Language Model

RegionGPT: Towards Region Understanding Vision Language Mode...

引用

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

作者： Guo, Qiushan De Mello, Shalini Yin, Hongxu Byeon, Wonmin Cheung, Ka Chun Yu, Yizhou Luo, Ping Liu, Sifei Univ Hong Kong Hong Kong Peoples R China NVIDIA San Francisco CA USA

ISBN: (纸本)9798350353006

vision language models (VLMs) have experienced rapid advancements through the integration of large language models (LLMs) with image-text pairs, yet they struggle with detailed regional visual understanding due to limited spatial awareness of the vision encoder, and the use of coarse-grained training data that lacks detailed, region-specific captions. To address this, we introduce RegionGPT (short as RGPT), a novel framework designed for complex region-level captioning and understanding. RGPT enhances the spatial awareness of regional representation with simple yet effective modifications to existing visual encoders in VLMs. We further improve performance on tasks requiring a specific output scope by integrating task-guided instruction prompts during both training and inference phases, while maintaining the model's versatility for general-purpose tasks. Additionally, we develop an automated region caption data generation pipeline, enriching the training set with detailed region-level captions. We demonstrate that a universal RGPT model can be effectively applied and significantly enhancing performance across a range of region-level tasks, including but not limited to complex region descriptions, reasoning, object classification, and referring expressions comprehension. Code will be released at the project page.

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Draw Step by Step: Reconstructing CAD Construction Sequences from Point Clouds via Multimodal Diffusion

Draw Step by Step: Reconstructing CAD Construction Sequences...

引用

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

作者： Ma, Weijian Chen, Shuaiqi Lou, Yunzhong Li, Xueyang Zhou, Xiangdong Fudan Univ Sch Comp Sci & Technol Shanghai Peoples R China

ISBN: (纸本)9798350353006

Reconstructing CAD construction sequences from raw 3D geometry serves as an interface between real-world objects and digital designs. In this paper, we propose CAD-Diffuser, a multimodal diffusion scheme aiming at integrating top-down design paradigm into generative reconstruction. In particular, we unify CAD point clouds and CAD construction sequences at the token level, guiding our proposed multimodal diffusion strategy to understand and link between the geometry and the design intent concentrated in construction sequences. Leveraging the strong decoding abilities of language models, the forward process is modeled as a random walk between the original token and the [MASK] token, while the reverse process naturally fits the masked token modeling scheme. A volume-based noise schedule is designed to encourage outline-first generation, decomposing the top-down design methodology into a machine-understandable procedure. For tokenizing CAD data of multiple modalities, we introduce a tokenizer with a self-supervised face segmentation task to compress local and global geometric information for CAD point clouds, and the CAD construction sequence is transformed into a primitive token string. Experimental results show that our CAD-Diffuser can perceive geometric details and the results are more likely to be reused by human designers.

关键词： computer-Aided Design Diffusion Models Point Cloud

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PromptSync: Bridging Domain Gaps in vision-Language Models through Class-Aware Prototype Alignment and Discrimination

PromptSync: Bridging Domain Gaps in Vision-Language Models t...

引用

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

作者： Khandelwal, Anant Glance AI Bangalore Karnataka India

ISBN: (纸本)9798350365474

The potential for zero-shot generalization in vision-language (V-L) models such as CLIP has spurred their widespread adoption in addressing numerous downstream tasks. Previous methods have employed test-time prompt tuning to adapt the model to unseen domains, but they overlooked the issue of imbalanced class distributions. In this study, we explicitly address this problem by employing class-aware prototype alignment weighted by mean class probabilities obtained for the test sample and filtered augmented views. Additionally, we ensure that the class probabilities are as accurate as possible by performing prototype discrimination using contrastive learning. The combination of alignment and discriminative loss serves as a geometric regularizer, preventing the prompt representation from collapsing onto a single class and effectively bridging the distribution gap between the source and test domains. Our method, named PromptSync, synchronizes the prompts for each test sample on both the text and vision branches of the V-L model. In empirical evaluations on the domain generalization benchmark, our method outperforms previous best methods by 2.33% in overall performance, by 1% in base-to-novel generalization, and by 2.84% in cross-dataset transfer tasks.

关键词： Visual languages

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Open-Set Domain Adaptation for Semantic Segmentation

Open-Set Domain Adaptation for Semantic Segmentation

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

作者： Choe, Seun-An Shin, Ah-Hyung Park, Keon-Hee Choi, Jinwoo Park, Gyeong-Moon Kyung Hee Univ Yongin South Korea

ISBN: (纸本)9798350353006

Unsupervised domain adaptation (UDA) for semantic segmentation aims to transfer the pixel-wise knowledge from the labeled source domain to the unlabeled target domain. However, current UDA methods typically assume a shared label space between source and target, limiting their applicability in real-world scenarios where novel categories may emerge in the target domain. In this paper, we introduce Open-Set Domain Adaptation for Semantic Segmentation (OSDA-SS) for the first time, where the target domain includes unknown classes. We identify two major problems in the OSDA-SS scenario as follows: 1) the existing UDA methods struggle to predict the exact boundary of the unknown classes, and 2) they fail to accurately predict the shape of the unknown classes. To address these issues, we propose Boundary and Unknown Shape-Aware open-set domain adaptation, coined BUS. Our BUS can accurately discern the boundaries between known and unknown classes in a contrastive manner using a novel dilation-erosion-based contrastive loss. In addition, we propose OpenReMix, a new domain mixing augmentation method that guides our model to effectively learn domain and size-invariant features for improving the shape detection of the known and unknown classes. Through extensive experiments, we demonstrate that our proposed BUS effectively detects unknown classes in the challenging OSDA-SS scenario compared to the previous methods by a large margin. The code is available at https://***/KHU-AGI/BUS.

关键词： Contrastive Learning Domain Adaptation Domain Mixing Augmentation Open-Set recognition Semantic Segmentation

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OTE: Exploring Accurate Scene Text recognition Using One Token

OTE: Exploring Accurate Scene Text Recognition Using One Tok...

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

作者： Xu, Jianjun Wang, Yuxin Xie, Hongtao Zhang, Yongdong Univ Sci & Technol China Hefei Peoples R China

ISBN: (纸本)9798350353006

In this paper, we propose a novel framework to fully exploit the potential of a single vector for scene text recognition (STR). Different from previous sequence-to-sequence methods that rely on a sequence of visual tokens to represent scene text images, we prove that just one token is enough to characterize the entire text image and achieve accurate text recognition. Based on this insight, we introduce a new paradigm for STR, called One Token rEcognizer (OTE). Specifically, we implement an image-to-vector encoder to extract the fine-grained global semantics, eliminating the need for sequential features. Furthermore, an elegant yet potent vector-to-sequence decoder is designed to adaptively diffuse global semantics to corresponding character locations, enabling both autoregressive and non-autoregressive decoding schemes. By executing decoding within a high-level representational space, our vector-to-sequence (V2S) approach avoids the alignment issues between visual tokens and character embeddings prevalent in traditional sequence-to-sequence methods. Remarkably, due to introducing character-wise fine-grained information, such global tokens also boost the performance of scene text retrieval tasks. Extensive experiments on synthetic and real datasets demonstrate the effectiveness of our method by achieving new state-of-the-art results on various public STR benchmarks. Our code is available at https://***/Xu-Jianjun/OTE.

关键词： CLIP Scene Text recognition Scene Text Retrieval

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HEAL-SWIN: A vision Transformer On The Sphere

HEAL-SWIN: A Vision Transformer On The Sphere

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

作者： Carlsson, Oscar Gerken, Jan E. Linander, Hampus Spiess, Heiner Ohlsson, Fredrik Petersson, Christoffer Persson, Daniel Univ Gothenburg Chalmers Univ Technol Dept Math Sci SE-41296 Gothenburg Sweden Tech Univ Berlin Neural Informat Proc Sci Intelligence DE-10623 Berlin Germany Umea Univ Dept Math & Math Stat Umea degrees SE-90187 Umea Sweden Zenseact SE-41756 Gothenburg Sweden

ISBN: (纸本)9798350353013;9798350353006

High-resolution wide-angle fisheye images are becoming more and more important for robotics applications such as autonomous driving. However, using ordinary convolutional neural networks or vision transformers on this data is problematic due to projection and distortion losses introduced when projecting to a rectangular grid on the plane. We introduce the HEAL-SWIN transformer, which combines the highly uniform Hierarchical Equal Area iso-Latitude Pixelation (HEALPix) grid used in astrophysics and cosmology with the Hierarchical Shifted-Window (SWIN) transformer to yield an efficient and flexible model capable of training on high-resolution, distortion-free spherical data. In HEAL-SWIN, the nested structure of the HEALPix grid is used to perform the patching and windowing operations of the SWIN transformer, enabling the network to process spherical representations with minimal computational overhead. We demonstrate the superior performance of our model on both synthetic and real automotive datasets, as well as a selection of other image datasets, for semantic segmentation, depth regression and classification tasks. Our code is publicly available(1).

关键词： depth estimation fisheye images image classification omni-directional images semantic segmentation spherical grid transformer

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Zero-Reference Low-Light Enhancement via Physical Quadruple Priors

Zero-Reference Low-Light Enhancement via Physical Quadruple ...

引用

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

作者： Wang, Wenjing Yang, Huan Fu, Jianlong Liu, Jiaying Peking Univ Beijing Peoples R China 01 AI Beijing Peoples R China Microsoft Res Asia Beijing Peoples R China

ISBN: (纸本)9798350353006

Understanding illumination and reducing the need for supervision pose a significant challenge in low-light enhancement. Current approaches are highly sensitive to data usage during training and illumination-specific hyper-parameters, limiting their ability to handle unseen scenarios. In this paper, we propose a new zero-reference low-light enhancement framework trainable solely with normal light images. To accomplish this, we devise an illumination-invariant prior inspired by the theory of physical light transfer. This prior serves as the bridge between normal and low-light images. Then, we develop a prior-to-image framework trained without low-light data. During testing, this frame-work is able to restore our illumination-invariant prior back to images, automatically achieving low-light enhancement. Within this framework, we leverage a pretrained generative diffusion model for model ability, introduce a bypass decoder to handle detail distortion, as well as offer a lightweight version for practicality. Extensive experiments demonstrate our framework's superiority in various scenarios as well as good interpretability, robustness, and efficiency. Code is available on our project homepage.

关键词： diffusion image processing low-level vision Low-light enhancement zero-reference

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ZeroShape: Regression-based Zero-shot Shape Reconstruction

ZeroShape: Regression-based Zero-shot Shape Reconstruction

引用

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

作者： Huang, Zixuan Stojanov, Stefan Thai, Anh Jampani, Varun Rehg, James M. Univ Illinois Champaign IL 61820 USA Georgia Inst Technol Atlanta GA 30332 USA Stabil AI London England

ISBN: (纸本)9798350353006

We study the problem of single-image zero-shot 3D shape reconstruction. Recent works learn zero-shot shape reconstruction through generative modeling of 3D assets, but these models are computationally expensive at train and inference time. In contrast, the traditional approach to this problem is regression-based, where deterministic models are trained to directly regress the object shape. Such regression methods possess much higher computational efficiency than generative methods. This raises a natural question: is generative modeling necessary for high performance, or conversely, are regression-based approaches still competitive? To answer this, we design a strong regression-based model, called ZeroShape, based on the converging findings in this field and a novel insight. We also curate a large real-world evaluation benchmark, with objects from three different real-world 3D datasets. This evaluation benchmark is more diverse and an order of magnitude larger than what prior works use to quantitatively evaluate their models, aiming at reducing the evaluation variance in our field. We show that ZeroShape not only achieves superior performance over state-of-the-art methods, but also demonstrates significantly higher computational and data efficiency.(1)

关键词： 3D generation 3D reconstruction 3D vision computer vision Deep Learning

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Purposeful Regularization with Reinforcement Learning for Facial Expression recognition In-the-Wild

Purposeful Regularization with Reinforcement Learning for Fa...

引用

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

作者： Hong, SangHwa Seoul Natl Univ Sci & Technol Dept Ind Engn Gongreung Ro 232 Seoul South Korea

ISBN: (纸本)9798350365474

Facial Expression recognition (FER), an essential aspect of emotion analysis through artificial intelligence, is a crucial research area. Although traditional approaches utilizing Convolutional Neural Networks (CNNs) for analyzing human emotions from facial expressions achieve superior accuracy over conventional machine learning methods, overfitting -especially arising severely from data collected in uncontrolled, In-the-wild settings -significantly impedes CNNs performance. This is due to the data scarcity and inherent noise inside In-the-wild data. To address this challenge, this paper introduces a novel regularization method that employs Reinforcement Learning (RL) to adaptively apply regularization hyperparameters appropriate for the evolving state of trained CNNs. Through experiments on various datasets such as CIFAR100, FER2013, and Affect-Net datasets including diverse perspective analysis such as graphical, Grad-CAM and numerical analysis, it is demonstrated that the suggested method can alleviate memorization of noise in training data and promote learning of essential features. The significance of the suggested method lies in its demonstrated remarkable effectiveness in enhancing CNNs' generalization and reducing overfitting.

关键词： FER Overfitting Reinforcement Learning

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Unified Language-driven Zero-shot Domain Adaptation

Unified Language-driven Zero-shot Domain Adaptation

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

作者： Yang, Senqiao Tian, Zhuotao Jiang, Li Jia, Jiaya Chinese Univ Hong Kong Hong Kong Peoples R China Harbin Inst Technol Shenzhen Peoples R China Chinese Univ Hong Kong Shenzhen Peoples R China

ISBN: (纸本)9798350353006

This paper introduces Unified Language-driven Zero-shot Domain Adaptation ( ULDA), a novel task setting that enables a single model to adapt to diverse target domains without explicit domain-ID knowledge. We identify the constraints in the existing language-driven zero-shot domain adaptation task, particularly the requirement for domain IDs and domain-specific models, which may restrict flexibility and scalability. To overcome these issues, we propose a new framework for ULDA, consisting of Hierarchical Context Alignment (HCA), Domain Consistent Representation Learning (DCRL), and Text-Driven Rectifier (TDR). These components work synergistically to align simulated features with target text across multiple visual levels, retain semantic correlations between different regional representations, and rectify biases between simulated and real target visual features, respectively. Our extensive empirical evaluations demonstrate that this framework achieves competitive performance in both settings, surpassing even the model that requires domain-ID, showcasing its superiority and generalization ability. The proposed method is not only effective but also maintains practicality and efficiency, as it does not introduce additional computational costs during inference. The code is available on the project website(1).

关键词： Domain Adaptation Segmentation vision-Language Model

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