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Adaptive Weighted Discriminator for Training Generative Adversarial Networks

Adaptive Weighted Discriminator for Training Generative Adve...

引用

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

作者： Zadorozhnyy, Vasily Cheng, Qiang Ye, Qiang Univ Kentucky Dept Math Lexington KY 40506 USA Univ Kentucky Inst Biomed Informat Dept Comp Sci Lexington KY 40506 USA Univ Kentucky Inst Biomed Informat Dept Internal Med Lexington KY 40506 USA

ISBN: (纸本)9781665445092

Generative adversarial network (GAN) has become one of the most important neural network models for classical unsupervised machine learning. A variety of discriminator loss functions have been developed to train GAN's discriminators and they all have a common structure: a sum of real and fake losses that only depends on the actual and generated data respectively. One challenge associated with an equally weighted sum of two losses is that the training may benefit one loss but harm the other, which we show causes instability and mode collapse. In this paper, we introduce a new family of discriminator loss functions that adopts a weighted sum of real and fake parts, which we call adaptive weighted loss functions or aw-loss functions. Using the gradients of the real and fake parts of the loss, we can adaptively choose weights to train a discriminator in the direction that benefits the GAN's stability. Our method can be potentially applied to any discriminator model with a loss that is a sum of the real and fake parts. Experiments validated the effectiveness of our loss functions on unconditional and conditional image generation tasks, improving the baseline results by a significant margin on CIFAR-10, STL-10, and CIFAR-100 datasets in Inception Scores (IS) and Frechet Inception Distance (FID) metrics.

关键词： Training Measurement computer vision Image synthesis Neural networks Machine learning Generative adversarial networks

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Seeing Behind Objects for 3D Multi-Object Tracking in RGB-D Sequences

Seeing Behind Objects for 3D Multi-Object Tracking in RGB-D ...

引用

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

作者： Mueller, Norman Wong, Yu-Shiang Mitra, Niloy J. Dai, Angela Niessner, Matthias Tech Univ Munich Munich Germany UCL London England Adobe Res San Jose CA USA

ISBN: (纸本)9781665445092

Multi-object tracking from RGB-D video sequences is a challenging problem due to the combination of changing viewpoints, motion, and occlusions over time. We observe that having the complete geometry of objects aids in their tracking, and thus propose to jointly infer the complete geometry of objects as well as track them, for rigidly moving objects over time. Our key insight is that inferring the complete geometry of the objects significantly helps in tracking. By hallucinating unseen regions of objects, we can obtain additional correspondences between the same instance, thus providing robust tracking even under strong change of appearance. From a sequence of RGB-D frames, we detect objects in each frame and learn to predict their complete object geometry as well as a dense correspondence mapping into a canonical space. This allows us to derive 6DoF poses for the objects in each frame, along with their correspondence between frames, providing robust object tracking across the RGB-D sequence. Experiments on both synthetic and real-world RGB-D data demonstrate that we achieve state-of-the-art performance on dynamic object tracking. Furthermore, we show that our object completion significantly helps tracking, providing an improvement of 6.5% in mean MOTA.

关键词： Geometry computer vision Three-dimensional displays Video sequences Pose estimation Pipelines Cameras

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Cycle4Completion: Unpaired Point Cloud Completion using Cycle Transformation with Missing Region Coding

Cycle4Completion: Unpaired Point Cloud Completion using Cycl...

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

作者： Wen, Xin Han, Zhizhong Cao, Yan-Pei Wan, Pengfei Zheng, Wen Liu, Yu-Shen Tsinghua Univ Sch Software BNRist Beijing Peoples R China Wayne State Univ Dept Comp Sci Detroit MI 48202 USA Kuaishou Technol Y Tech Beijing Peoples R China

ISBN: (纸本)9781665445092

In this paper, we present a novel unpaired point cloud completion network, named Cycle4Completion, to infer the complete geometries from a partial 3D object. Previous unpaired completion methods merely focus on the learning of geometric correspondence from incomplete shapes to complete shapes, and ignore the learning in the reverse direction, which makes them suffer from low completion accuracy due to the limited 3D shape understanding ability. To address this problem, we propose two simultaneous cycle transformations between the latent spaces of complete shapes and incomplete ones. Specifically, the first cycle transforms shapes from incomplete domain to complete domain, and then projects them back to the incomplete domain. This process learns the geometric characteristic of complete shapes, and maintains the shape consistency between the complete prediction and the incomplete input. Similarly, the inverse cycle transformation starts from complete domain to incomplete domain, and goes back to complete domain to learn the characteristic of incomplete shapes. We experimentally show that our model with the learned bidirectional geometry correspondence outperforms state-of-the-art unpaired completion methods. Code will be available at https://***/diviswen/Cycle4Completion.

关键词： Geometry computer vision Three-dimensional displays Codes Shape Computational modeling Transforms

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CR-FIQA: Face Image Quality Assessment by Learning Sample Relative Classifiability

CR-FIQA: Face Image Quality Assessment by Learning Sample Re...

引用

conference on computer vision and pattern recognition (cvpr)

作者： Fadi Boutros Meiling Fang Marcel Klemt Biying Fu Naser Damer Fraunhofer Institute for Computer Graphics Research IGD Darmstadt Germany Department of Computer Science TU Darmstadt Darmstadt Germany

Face image quality assessment (FIQA) estimates the utility of the captured image in achieving reliable and accurate recognition performance. This work proposes a novel FIQA method, CR-FIQA, that estimates the face image quality of a sample by learning to predict its relative classifiability. This classifiability is measured based on the allocation of the training sample feature representation in angular space with respect to its class center and the nearest negative class center. We experimentally illustrate the correlation between the face image quality and the sample relative classifiability. As such property is only observable for the training dataset, we propose to learn this property by probing internal network observations during the training process and utilizing it to predict the quality of unseen samples. Through extensive evaluation experiments on eight benchmarks and four face recognition models, we demonstrate the superiority of our proposed CR-FIQA over state-of-the-art (SOTA) FIQA algorithms. 1 1 https://***/fdbtrs/CR-FIQA

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HOOD: Hierarchical Graphs for Generalized Modelling of Clothing Dynamics

HOOD: Hierarchical Graphs for Generalized Modelling of Cloth...

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conference on computer vision and pattern recognition (cvpr)

作者： Artur Grigorev Michael J. Black Otmar Hilliges Department of Computer Science ETH Zurich Max Planck Institute for Intelligent Systems Tübingen

We propose a method that leverages graph neural networks, multi-level message passing, and unsupervised training to enable efficient prediction of realistic clothing dynamics. Whereas existing methods based on linear blend skinning must be trained for specific garments, our method, called HOOD, is agnostic to body shape and applies to tight-fitting garments as well as loose, free-flowing clothing. Furthermore, HOOD handles changes in topology (e.g., garments with buttons or zippers) and material properties at inference time. As one key contribution, we propose a hierarchical message-passing scheme that efficiently propagates stiff stretching modes while preserving local detail. We empirically show that HOOD outperforms strong baselines quantitatively and that its results are perceived as more realistic than state-of-the-art methods.

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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

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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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