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Visual Layout Composer: Image-Vector Dual Diffusion Model for Design Layout Generation

Visual Layout Composer: Image-Vector Dual Diffusion Model fo...

引用

conference on computer vision and pattern recognition (cvpr)

作者： Mohammad Amin Shabani Zhaowen Wang Difan Liu Nanxuan Zhao Jimei Yang Yasutaka Furukawa Simon Fraser University Adobe Research

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

This paper proposes an image-vector dual diffusion model for generative layout design. Distinct from prior efforts that mostly ignores visual information of elements and the whole canvas, our approach integrates the power of a pre-trained large image diffusion model to guide layout composition in a vector diffusion model by providing enhanced salient region understanding and high-level inter-element relationship reasoning. Our proposed model simultaneously operates in two domains: it generates the overall design appearance in the image domain while optimizing the size and position of each design element in the vector domain. The proposed method achieves the state-of-the-art results on several datasets and enables new layout design applications. Project webpage: https://***/visual_layout_composer.

关键词： Visualization computer vision Computational modeling Layout Diffusion models Controllability Vectors

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Question Aware vision Transformer for Multimodal Reasoning

Question Aware Vision Transformer for Multimodal Reasoning

引用

conference on computer vision and pattern recognition (cvpr)

作者： Roy Ganz Yair Kittenplon Aviad Aberdam Elad Ben Avraham Oren Nuriel Shai Mazor Ron Litman Technion Israel AWS AI Labs

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

vision-Language (VL) models have gained significant research focus, enabling remarkable advances in multimodal reasoning. These architectures typically comprise a vision encoder, a Large Language Model (LLM), and a projection module that aligns visual features with the LLM's representation space. Despite their success, a critical limitation persists: the vision encoding process remains decoupled from user queries, often in the form of image-related questions. Consequently, the resulting visual features may not be optimally attuned to the query-specific elements of the image. To address this, we introduce QA-ViT, a Question Aware vision Transformer approach for multimodal reasoning, which embeds question awareness directly within the vision encoder. This integration results in dynamic visual features focusing on relevant image aspects to the posed question. QA-ViT is model-agnostic and can be incorporated efficiently into any VL architecture. Extensive experiments demonstrate the effectiveness of applying our method to various multimodal architectures, leading to consistent improvement across diverse tasks and showcasing its potential for enhancing visual and scene-text understanding.

关键词： Visualization computer vision Image coding Large language models Focusing computer architecture Transformers

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Learning Fine-Grained Segmentation of 3D Shapes without Part Labels

Learning Fine-Grained Segmentation of 3D Shapes without Part...

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

作者： Wang, Xiaogang Sun, Xun Cao, Xinyu Xu, Kai Zhou, Bin Southwest Univ Chongqing Peoples R China Beihang Univ State Key Lab Virtual Real Technol & Syst Beijing Peoples R China Natl Univ Def Technol Changsha Peoples R China

ISBN: (纸本)9781665445092

Learning-based 3D shape segmentation is usually formulated as a semantic labeling problem, assuming that all parts of training shapes are annotated with a given set of tags. This assumption, however, is impractical for learning fine-grained segmentation. Although most off-the-shelf CAD models are, by construction, composed of fine-grained parts, they usually miss semantic tags and labeling those fine-grained parts is extremely tedious. We approach the problem with deep clustering, where the key idea is to learn part priors from a shape dataset with fine-grained segmentation but no part labels. Given point sampled 3D shapes, we model the clustering priors of points with a similarity matrix and achieve part segmentation through minimizing a novel low rank loss. To handle highly densely sampled point sets, we adopt a divide-and-conquer strategy. We partition the large point set into a number of blocks. Each block is segmented using a deep-clustering-based part prior network trained in a category-agnostic manner. We then train a graph convolution network to merge the segments of all blocks to form the final segmentation result. Our method is evaluated with a challenging benchmark of fine-grained segmentation, showing state-of-the-art performance.

关键词： Training Solid modeling Three-dimensional displays Shape Semantics Graph neural networks pattern recognition

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LaPred: Lane-Aware Prediction of Multi-Modal Future Trajectories of Dynamic Agents

LaPred: Lane-Aware Prediction of Multi-Modal Future Trajecto...

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

作者： Kim, Byeoung Do Park, Seong Hyeon Lee, Seokhwan Khoshimjonov, Elbek Kum, Dongsuk Kim, Junsoo Kim, Jeong Soo Choi, Jun Won Hanyang Univ Seoul South Korea Korea Adv Inst Sci & Technol Seoul South Korea Hyundai Motor Co Seoul South Korea

ISBN: (纸本)9781665445092

In this paper, we address the problem of predicting the future motion of a dynamic agent (called a target agent) given its current and past states as well as the information on its environment. It is paramount to develop a prediction model that can exploit the contextual information in both static and dynamic environments surrounding the target agent and generate diverse trajectory samples that are meaningful in a traffic context. We propose a novel prediction model, referred to as the lane-aware prediction (LaPred) network, which uses the instance-level lane entities extracted from a semantic map to predict the multi-modal future trajectories. For each lane candidate found in the neighborhood of the target agent, LaPred extracts the joint features relating the lane and the trajectories of the neighboring agents. Then, the features for all lane candidates are fused with the attention weights learned through a self-supervised learning task that identifies the lane candidate likely to be followed by the target agent. Using the instance-level lane information, LaPred can produce the trajectories compliant with the surroundings better than 2D raster image-based methods and generate the diverse future trajectories given multiple lane candidates. The experiments conducted on the public nuScenes dataset and Argoverse dataset demonstrate that the proposed LaPred method significantly outperforms the existing prediction models, achieving state-of-the-art performance in the benchmarks.

关键词： Measurement Dynamics Semantics Predictive models Feature extraction Trajectory pattern recognition

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Ultra-High-Definition Image Dehazing via Multi-Guided Bilateral Learning

Ultra-High-Definition Image Dehazing via Multi-Guided Bilate...

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

作者： Zheng, Zhuoran Ren, Wenqi Cao, Xiaochun Hu, Xiaobin Wang, Tao Song, Fenglong Jia, Xiuyi Nanjing Univ Sci & Technol CSE Nanjing Peoples R China Chinese Acad Sci IIE SKLOIS Beijing Peoples R China Tech Univ Munich Informat Munich Germany Huawei Noahs Ark Lab Montreal PQ Canada

ISBN: (纸本)9781665445092

Convolutional neural networks (CNNs) have achieved significant success in the single image dehazing task. Unfortunately, most existing deep dehazing models have high computational complexity, which hinders their application to high-resolution images, especially for UHD (ultra-high-definition) or 4K resolution images. To address the problem, we propose a novel network capable of real-time dehazing of 4K images on a single GPU, which consists of three deep CNNs. The first CNN extracts haze-relevant features at a reduced resolution of the hazy input and then fits locally-affine models in the bilateral space. Another CNN is used to learn multiple full-resolution guidance maps corresponding to the learned bilateral model. As a result, the feature maps with high-frequency can be reconstructed by multi-guided bilateral upsampling. Finally, the third CNN fuses the high-quality feature maps into a dehazed image. In addition, we create a large-scale 4K image dehazing dataset to support the training and testing of compared models. Experimental results demonstrate that the proposed algorithm performs favorably against the state-of-the-art dehazing approaches on various benchmarks.

关键词： Training Image resolution Image edge detection Feature extraction Real-time systems pattern recognition Image restoration

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Semi-Supervised Video Deraining with Dynamical Rain Generator

Semi-Supervised Video Deraining with Dynamical Rain Generato...

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

作者： Yue, Zongsheng Xie, Jianwen Zhao, Qian Meng, Deyu Xi An Jiao Tong Univ Xian Peoples R China Baidu Res Cognit Comp Lab Bellevue WA USA Macau Univ Sci & Technol Macau Peoples R China

ISBN: (纸本)9781665445092

While deep learning (DL)-based video deraining methods have achieved significant successes in recent years, they still have two major drawbacks. Firstly, most of them are insufficient to model the characteristics of rain layers contained in rainy videos. In fact, the rain layers exhibit strong visual properties (e.g., direction, scale, and thickness) in spatial dimension and causal properties (e.g., velocity and acceleration) in temporal dimension, and thus can be modeled by the spatial-temporal process in statistics. Secondly, current DL-based methods rely heavily on the labeled training data, whose rain layers are synthetic, thus leading to a deviation from real data. Such a gap between synthetic and real data sets results in poor performance when applying them to real scenarios. To address these issues, this paper proposes a new semi-supervised video deraining method, in which a dynamical rain generator is employed to fit the rain layer for the sake of better depicting its intrinsic characteristics. Specifically, the dynamical generator consists of one emission model and one transition model to simultaneously encode the spatial appearance and temporal dynamics of rain streaks, respectively, both of which are parameterized by deep neural networks (DNNs). Furthermore, different prior formats are designed for the labeled synthetic and unlabeled real data so as to fully exploit their underlying common knowledge. Last but not least, we design a Monte Carlo-based EM algorithm to learn the model. Extensive experiments are conducted to verify the superiority of the proposed semi-supervised deraining model.

关键词： Deep learning Visualization computer vision Rain Monte Carlo methods Heuristic algorithms Training data

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Multi-Modal Relational Graph for Cross-Modal Video Moment Retrieval

Multi-Modal Relational Graph for Cross-Modal Video Moment Re...

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

作者： Zeng, Yawen Cao, Da Wei, Xiaochi Liu, Meng Zhao, Zhou Qin, Zheng Hunan Univ Changsha Hunan Peoples R China Baidu Inc Beijing Peoples R China Shandong Jianzhu Univ Jinan Peoples R China Zhejiang Univ Hangzhou Peoples R China

ISBN: (纸本)9781665445092

Given an untrimmed video and a query sentence, cross-modal video moment retrieval aims to rank a video moment from pre-segmented video moment candidates that best matches the query sentence. Pioneering work typically learns the representations of the textual and visual content separately and then obtains the interactions or alignments between different modalities. However, the task of cross-modal video moment retrieval is not yet thoroughly addressed as it needs to further identify the fine-grained differences of video moment candidates with high repeatability and similarity. Moveover, the relation among objects in both video and sentence is intuitive and efficient for understanding semantics but is rarely considered. Toward this end, we contribute a multi-modal relational graph to capture the interactions among objects from the visual and textual content to identify the differences among similar video moment candidates. Specifically, we first introduce a visual relational graph and a textual relational graph to form relation-aware representations via message propagation. Thereafter, a multi-task pre-training is designed to capture domain-specific knowledge about objects and relations, enhancing the structured visual representation after explicitly defined relation. Finally, the graph matching and boundary regression are employed to perform the cross-modal retrieval. We conduct extensive experiments on two datasets about daily activities and cooking activities, demonstrating significant improvements over state-of-the-art solutions.

关键词： Visualization computer vision Semantics pattern recognition Object recognition Task analysis

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Adversarial Robust Model Compression using In-Train Pruning

Adversarial Robust Model Compression using In-Train Pruning

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

作者： Vemparala, Manoj-Rohit Fasfous, Nael Frickenstein, Alexander Sarkar, Sreetama Zhao, Qi Kuhn, Sabine Frickenstein, Lukas Singh, Anmol Unger, Christian Nagaraja, Naveen-Shankar Wressnegger, Christian Stechele, Walter BMW Autonomous Driving Munich Germany Tech Univ Munich Munich Germany Karlsruhe Inst Technol Karlsruhe Germany

ISBN: (纸本)9781665448994

Efficiently deploying learning-based systems on embedded hardware is challenging for various reasons, two of which are considered in this paper: The model's size and its robustness against attacks. Both need to be addressed even-handedly. We combine adversarial training and model pruning in a joint formulation of the fundamental learning objective during training. Unlike existing post-train pruning approaches, our method does not use heuristics and eliminates the need for a pre-trained model. This allows for a classifier which is robust against attacks and enables better compression of the model, reducing its computational effort. In comparison to prior work, our approach yields 6.21 pp higher accuracy for an 85 % reduction in parameters for ResNet20 on the CIFAR-10 dataset.

关键词： Training computer vision Computational modeling conferences Robustness Hardware pattern recognition

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ABMDRNet: Adaptive-weighted Bi-directional Modality Difference Reduction Network for RGB-T Semantic Segmentation

ABMDRNet: Adaptive-weighted Bi-directional Modality Differen...

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

作者： Zhang, Qiang Zhao, Shenlu Luo, Yongjiang Zhang, Dingwen Huang, Nianchang Han, Jungong Xidian Univ Sch Mechano Elect Engn Xian Peoples R China Xidian Univ Sch Elect Engn Xian Peoples R China Aberystwyth Univ Comp Sci Dept Aberystwyth Dyfed Wales

ISBN: (纸本)9781665445092

Semantic segmentation models gain robustness against poor lighting conditions by virtue of complementary information from visible (RGB) and thermal images. Despite its importance, most existing RGB-T semantic segmentation models perform primitive fusion strategies, such as concatenation, element-wise summation and weighted summation, to fuse features from different modalities. These strategies, unfortunately, overlook the modality differences due to different imaging mechanisms, so that they suffer from the reduced discriminability of the fused features. To address such an issue, we propose, for the first time, the strategy of bridging-then-fusing, where the innovation lies in a novel Adaptive-weighted Bi-directional Modality Difference Reduction Network (ABMDRNet). Concretely, a Modality Difference Reduction and Fusion (MDRF) subnetwork is designed, which first employs a bi-directional image-to-image translation based method to reduce the modality differences between RGB features and thermal features, and then adaptively selects those discriminative multi-modality features for RGB-T semantic segmentation in a channel-wise weighted fusion way. Furthermore, considering the importance of contextual information in semantic segmentation, a Multi-Scale Spatial Context (MSC) module and a Multi-Scale Channel Context (MCC) module are proposed to exploit the interactions among multi-scale contextual information of cross-modality features together with their long-range dependencies along spatial and channel dimensions, respectively. Comprehensive experiments on MFNet dataset demonstrate that our method achieves new state-of-the-art results.

关键词： Image segmentation Technological innovation computer vision Fuses Semantics Lighting Imaging

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Adapt Before Comparison: A New Perspective on Cross-Domain Few-Shot Segmentation

Adapt Before Comparison: A New Perspective on Cross-Domain F...

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conference on computer vision and pattern recognition (cvpr)

作者： Jonas Herzog Zhejiang University

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

Few-shot segmentation performance declines substantially when facing images from a domain different than the training domain, effectively limiting real-world use cases. To alleviate this, recently cross-domain few-shot segmentation (CD-FSS) has emerged. Works that address this task mainly attempted to learn segmentation on a source domain in a manner that generalizes across domains. Surprisingly, we can outperform these approaches while eliminating the training stage and removing their main segmentation network. We show test-time task-adaption is the key for successful CD-FSS instead. Task-adaption is achieved by appending small networks to the feature pyramid of a conventionally classification-pretrained backbone. To avoid overfitting to the few labeled samples in supervised fine-tuning, consistency across augmented views of input images serves as guidance while learning the parameters of the attached layers. Despite our self-restriction not to use any images other than the few labeled samples at test time, we achieve new state-of-the-art performance in CD-FSS, evidencing the need to rethink approaches for the task. Code is available at https://***/vision-Kek/ABCDFSS.

关键词： Training Image segmentation computer vision Adaptation models Limiting Codes pattern recognition

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