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Neural Transformation Fields for Arbitrary-Styled Font Generation

Neural Transformation Fields for Arbitrary-Styled Font Gener...

引用

conference on computer vision and pattern recognition (cvpr)

作者： Bin Fu Junjun He Jianjun Wang Yu Qiao ShenZhen Key Lab of Computer Vision and Pattern Recognition Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shanghai Artificial Intelligence Laboratory

Few-shot font generation (FFG), aiming at generating font images with a few samples, is an emerging topic in recent years due to the academic and commercial values. Typically, the FFG approaches follow the style-content disentanglement paradigm, which transfers the target font styles to characters by combining the content representations of source characters and the style codes of reference samples. Most existing methods attempt to increase font generation ability via exploring powerful style representations, which may be a sub-optimal solution for the FFG task due to the lack of modeling spatial transformation in transferring font styles. In this paper, we model font generation as a continuous transformation process from the source character image to the target font image via the creation and dissipation of font pixels, and embed the corresponding transformations into a neural transformation field. With the estimated transformation path, the neural transformation field generates a set of intermediate transformation results via the sampling process, and a font rendering formula is developed to accumulate them into the target font image. Extensive experiments show that our method achieves state-of-the-art performance on few-shot font generation task, which demonstrates the effectiveness of our proposed model. Our implementation is available at: https://***/fubinfb/NTF.

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MELTR: Meta Loss Transformer for Learning to Fine-tune Video Foundation Models

MELTR: Meta Loss Transformer for Learning to Fine-tune Video...

引用

conference on computer vision and pattern recognition (cvpr)

作者： Dohwan Ko Joonmyung Choi Hyeong Kyu Choi Kyoung-Woon On Byungseok Roh Hyunwoo J. Kim Department of Computer Science and Engineering Korea University Kakao Brain

Foundation models have shown outstanding performance and generalization capabilities across domains. Since most studies on foundation models mainly focus on the pretraining phase, a naive strategy to minimize a single task-specific loss is adopted for fine-tuning. However, such fine-tuning methods do not fully leverage other losses that are potentially beneficial for the target task. Therefore, we propose MEta Loss TRansformer (MELTR), a plug-in module that automatically and non-linearly combines various loss functions to aid learning the target task via auxiliary learning. We formulate the auxiliary learning as a bi-level optimization problem and present an efficient optimization algorithm based on Approximate Implicit Differentiation (AID). For evaluation, we apply our framework to various video foundation models (UniVL, Violet and All-in-one), and show significant performance gain on all four downstream tasks: text-to-video retrieval, video question answering, video captioning, and multimodal sentiment analysis. Our qualitative analyses demonstrate that MELTR adequately 'transforms' individual loss functions and 'melts' them into an effective unified loss. Code is available at https://***/mlvlab/MELTR.

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Exploiting Edge-Oriented Reasoning for 3D Point-based Scene Graph Analysis

Exploiting Edge-Oriented Reasoning for 3D Point-based Scene ...

引用

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

作者： Zhang, Chaoyi Yu, Jianhui Song, Yang Cai, Weidong Univ Sydney Sydney NSW Australia Univ New South Wales Sydney NSW Australia

ISBN: (纸本)9781665445092

Scene understanding is a critical problem in computer vision. In this paper, we propose a 3D point-based scene graph generation (SGG(point)) framework to effectively bridge perception and reasoning to achieve scene understanding via three sequential stages, namely scene graph construction, reasoning, and inference. Within the reasoning stage, an EDGE-oriented Graph Convolutional Network (EdgeGCN) is created to exploit multi-dimensional edge features for explicit relationship modeling, together with the exploration of two associated twinning interaction mechanisms between nodes and edges for the independent evolution of scene graph representations. Overall, our integrated SGG(point) framework is established to seek and infer scene structures of interest from both real-world and synthetic 3D point-based scenes. Our experimental results show promising edge-oriented reasoning effects on scene graph generation studies. We also demonstrate our method advantage on several traditional graph representation learning benchmark datasets, including the node-wise classification on citation networks and whole-graph recognition problems for molecular analysis.

关键词： Bridges computer vision Three-dimensional displays Benchmark testing Cognition pattern recognition

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Incremental Few-Shot Instance Segmentation

Incremental Few-Shot Instance Segmentation

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

作者： Ganea, Dan Andrei Boom, Bas Poppe, Ronald Univ Utrecht Utrecht Netherlands Cyclomedia Technol Zaltbommel Netherlands

ISBN: (纸本)9781665445092

Few-shot instance segmentation methods are promising when labeled training data for novel classes is scarce. However, current approaches do not facilitate flexible addition of novel classes. They also require that examples of each class are provided at train and test time, which is memory intensive. In this paper, we address these limitations by presenting the first incremental approach to few-shot instance segmentation: iMTFA. We learn discriminative embeddings for object instances that are merged into class representatives. Storing embedding vectors rather than images effectively solves the memory overhead problem. We match these class embeddings at the RoI-level using cosine similarity. This allows us to add new classes without the need for further training or access to previous training data. In a series of experiments, we consistently outperform the current state-of-the-art. Moreover, the reduced memory requirements allow us to evaluate, for the first time, few-shot instance segmentation performance on all classes in COCO jointly(1).

关键词： Location awareness Training Image segmentation computer vision Memory management Training data Transfer functions

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Cloud2Curve: Generation and Vectorization of Parametric Sketches

Cloud2Curve: Generation and Vectorization of Parametric Sket...

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

作者： Das, Ayan Yang, Yongxin Hospedales, Timothy Xiang, Tao Song, Yi-Zhe Univ Surrey CVSSP SketchX Guildford Surrey England iFlyTek Surrey Joint Res Ctr Artificial Intellige Guildford Surrey England Univ Edinburgh Edinburgh Midlothian Scotland

ISBN: (纸本)9781665445092

Analysis of human sketches in deep learning has advanced immensely through the use of waypoint-sequences rather than raster-graphic representations. We further aim to model sketches as a sequence of low-dimensional parametric curves. To this end, we propose an inverse graphics framework capable of approximating a raster or waypoint based stroke encoded as a point-cloud with a variable-degree Bezier curve. Building on this module, we present Cloud2Curve, a generative model for scalable high-resolution vector sketches that can be trained end-to-end using point-cloud data alone. As a consequence, our model is also capable of deterministic vectorization which can map novel raster or waypoint based sketches to their corresponding high-resolution scalable Bezier equivalent. We evaluate the generation and vectorization capabilities of our model on Quick, Draw! and K-MNIST datasets.

关键词： Training Graphics Deep learning computer vision Computational modeling Fitting computer architecture

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Towards Real-World Blind Face Restoration with Generative Facial Prior

Towards Real-World Blind Face Restoration with Generative Fa...

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

作者： Wang, Xintao Li, Yu Zhang, Honglun Shan, Ying Tencent PCG Appl Res Ctr ARC Shenzhen Peoples R China

ISBN: (纸本)9781665445092

Blind face restoration usually relies on facial priors, such as facial geometry prior or reference prior, to restore realistic and faithful details. However, very low-quality inputs cannot offer accurate geometric prior while high-quality references are inaccessible, limiting the applicability in real-world scenarios. In this work, we propose GFP-GAN that leverages rich and diverse priors encapsulated in a pretrained face GAN for blind face restoration. This Generative Facial Prior (GFP) is incorporated into the face restoration process via spatial feature transform layers, which allow our method to achieve a good balance of realness and fidelity. Thanks to the powerful generative facial prior and delicate designs, our GFP-GAN could jointly restore facial details and enhance colors with just a single forward pass, while GAN inversion methods require image-specific optimization at inference. Extensive experiments show that our method achieves superior performance to prior art on both synthetic and real-world datasets.

关键词： Geometry computer vision Art Limiting Image color analysis Face recognition Transforms

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Wasserstein Contrastive Representation Distillation

Wasserstein Contrastive Representation Distillation

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

作者： Chen, Liqun Wang, Dong Gan, Zhe Liu, Jingjing Henao, Ricardo Carin, Lawrence Duke Univ Durham NC 27706 USA Microsoft Corp Redmond WA 98052 USA

ISBN: (纸本)9781665445092

The primary goal of knowledge distillation (KD) is to encapsulate the information of a model learned from a teacher network into a student network, with the latter being more compact than the former. Existing work, e.g., using Kullback-Leibler divergence for distillation, may fail to capture important structural knowledge in the teacher network and often lacks the ability for feature generalization, particularly in situations when teacher and student are built to address different classification tasks. We propose Wasserstein Contrastive Representation Distillation (WCoRD), which leverages both primal and dual forms of Wasserstein distance for KD. The dual form is used for global knowledge transfer, yielding a contrastive learning objective that maximizes the lower bound of mutual information between the teacher and the student networks. The primal form is used for local contrastive knowledge transfer within a mini-batch, effectively matching the distributions of features between the teacher and the student networks. Experiments demonstrate that the proposed WCoRD method outperforms state-of-the-art approaches on privileged information distillation, model compression and cross-modal transfer.

关键词： Knowledge engineering Measurement computer vision Computational modeling Collaborative work Robustness pattern recognition

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DeepSurfels: Learning Online Appearance Fusion

DeepSurfels: Learning Online Appearance Fusion

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

作者： Mihajlovic, Marko Weder, Silvan Pollefeys, Marc Oswald, Martin R. Swiss Fed Inst Technol Dept Comp Sci Zurich Switzerland Microsoft Mixed Real Zurich Switzerland AI Zurich Lab Zurich Switzerland

ISBN: (纸本)9781665445092

We present DeepSurfels, a novel hybrid scene representation for geometry and appearance information. DeepSurfels combines explicit and neural building blocks to jointly encode geometry and appearance information. In contrast to established representations, DeepSurfels better represents high-frequency textures, is well-suited for online updates of appearance information, and can be easily combined with machine learning methods. We further present an end-to-end trainable online appearance fusion pipeline that fuses information from RGB images into the proposed scene representation and is trained using self-supervision imposed by the reprojection error with respect to the input images. Our method compares favorably to classical texture mapping approaches as well as recent learning-based techniques. Moreover, we demonstrate lower runtime, improved generalization capabilities, and better scalability to larger scenes compared to existing methods.

关键词： Geometry Runtime Shape Scalability Pipelines Machine learning Rendering (computer graphics)

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On Disentanglement and Mutual Information in Semi-Supervised Variational Auto-Encoders

On Disentanglement and Mutual Information in Semi-Supervised...

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

作者： Gordon Rodriguez, Elliott Columbia Univ Dept Stat New York NY 10027 USA

ISBN: (纸本)9781665448994

In the context of variational auto-encoders, learning disentangled latent variable representations remains a challenging problem. In this abstract, we consider the semi-supervised setting, in which the factors of variation are labelled for a small fraction of our samples. We examine how the quality of learned representations is affected by the dimension of the unsupervised component of the latent space. We also consider a variational lower bound for the mutual information between the data and the semi-supervised component of the latent space, and analyze its role in the context of disentangled representation learning.

关键词： computer vision conferences pattern recognition Mutual information

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Gradient Norm Aware Minimization Seeks First-Order Flatness and Improves Generalization

Gradient Norm Aware Minimization Seeks First-Order Flatness ...

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conference on computer vision and pattern recognition (cvpr)

作者： Xingxuan Zhang Renzhe Xu Han Yu Hao Zou Peng Cui Department of Computer Science Tsinghua University

Recently, flat minima are proven to be effective for improving generalization and sharpness-aware minimization (SAM) achieves state-of-the-art performance. Yet the current definition of flatness discussed in SAM and its follow-ups are limited to the zeroth-order flatness (i.e., the worst-case loss within a perturbation radius). We show that the zeroth-order flatness can be insufficient to discriminate minima with low generalization error from those with high generalization error both when there is a single minimum or multiple minima within the given perturbation radius. Thus we present first-order flatness, a stronger measure of flatness focusing on the maximal gradient norm within a perturbation radius which bounds both the maximal eigenvalue of Hessian at local minima and the regularization function of SAM. We also present a novel training procedure named Gradient norm Aware Minimization (GAM) to seek minima with uniformly small curvature across all directions. Experimental results show that GAM improves the generalization of models trained with current optimizers such as SGD and Adam W on various datasets and networks. Furthermore, we show that GAM can help SAM find flatter minima and achieve better generalization. The code is available at https://***/xxgege/GAM.

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