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VecFontSDF: Learning to Reconstruct and Synthesize High-quality Vector Fonts via Signed Distance Functions

VecFontSDF: Learning to Reconstruct and Synthesize High-qual...

引用

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

作者： Xia, Zeqing Xiong, Bojun Lian, Zhouhui Peking Univ Wangxuan Inst Comp Technol Beijing Peoples R China

ISBN: (纸本)9798350301298

Font design is of vital importance in the digital content design and modern printing industry. Developing algorithms capable of automatically synthesizing vector fonts can significantly facilitate the font design process. However, existing methods mainly concentrate on raster image generation, and only a few approaches can directly synthesize vector fonts. This paper proposes an end-to-end trainable method, VecFontSDF, to reconstruct and synthesize high-quality vector fonts using signed distance functions (SDFs). Specifically, based on the proposed SDF-based implicit shape representation, VecFontSDF learns to model each glyph as shape primitives enclosed by several parabolic curves, which can be precisely converted to quadratic Bezier curves that are widely used in vector font products. In this manner, most image generation methods can be easily extended to synthesize vector fonts. Qualitative and quantitative experiments conducted on a publicly-available dataset demonstrate that our method obtains high-quality results on several tasks, including vector font reconstruction, interpolation, and few-shot vector font synthesis, markedly outperforming the state of the art.

关键词： vision + graphics

来源：评论

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Weakly-supervised Single-view Image Relighting

Weakly-supervised Single-view Image Relighting

引用

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

来源：评论

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Spatial-temporal Concept based Explanation of 3D ConvNets

Spatial-temporal Concept based Explanation of 3D ConvNets

引用

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

作者： Ji, Ying Wang, Yu Kato, Jien Nagoya Univ Nagoya Aichi Japan Hitotsubashi Univ Kunitachi Tokyo Japan Ritsumeikan Univ Kyoto Japan

ISBN: (纸本)9798350301298

Convolutional neural networks (CNNs) have shown remarkable performance on various tasks. Despite its widespread adoption, the decision procedure of the network still lacks transparency and interpretability, making it difficult to enhance the performance further. Hence, there has been considerable interest in providing explanation and interpretability for CNNs over the last few years. Explainable artificial intelligence (XAI) investigates the relationship between input images or videos and output predictions. Recent studies have achieved outstanding success in explaining 2D image classification ConvNets. On the other hand, due to the high computation cost and complexity of video data, the explanation of 3D video recognition ConvNets is relatively less studied. And none of them are able to produce a high-level explanation. In this paper, we propose a STCE (Spatial-temporal Concept-based Explanation) framework for interpreting 3D ConvNets. In our approach: (1) videos are represented with high-level supervoxels, similar supervoxels are clustered as a concept, which is straightforward for human to understand;and (2) the interpreting framework calculates a score for each concept, which reflects its significance in the ConvNet decision procedure. Experiments on diverse 3D ConvNets demonstrate that our method can identify global concepts with different importance levels, allowing us to investigate the impact of the concepts on a target task, such as action recognition, in-depth. The source codes are publicly available at https://***/yingji425/STCE.

关键词： Explainable computer vision

来源：评论

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Pic2Word: Mapping Pictures to Words for Zero-shot Composed Image Retrieval

Pic2Word: Mapping Pictures to Words for Zero-shot Composed I...

引用

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

作者： Saito, Kuniaki Sohn, Kihyuk Zhang, Xiang Li, Chun-Liang Lee, Chen-Yu Saenko, Kate Pfister, Tomas Boston Univ Boston MA 02215 USA Google Cloud AI Res Mountain View CA 94043 USA Google Res Mountain View CA USA MIT IBM Watson AI Lab Cambridge MA USA

ISBN: (纸本)9798350301298

In Composed Image Retrieval (CIR), a user combines a query image with text to describe their intended target. Existing methods rely on supervised learning of CIR models using labeled triplets consisting of the query image, text specification, and the target image. Labeling such triplets is expensive and hinders broad applicability of CIR. In this work, we propose to study an important task, Zero-Shot Composed Image Retrieval (ZS-CIR), whose goal is to build a CIR model without requiring labeled triplets for training. To this end, we propose a novel method, called Pic2Word, that requires only weakly labeled image-caption pairs and unlabeled image datasets to train. Unlike existing supervised CIR models, our model trained on weakly labeled or unlabeled datasets shows strong generalization across diverse ZS-CIR tasks, e.g., attribute editing, object composition, and domain conversion. Our approach outperforms several supervised CIR methods on the common CIR benchmark, CIRR and Fashion-IQ. Code will be made publicly available at https://***/google-research/composed_image_retrieval

关键词： language reasoning vision

来源：评论

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AMT: All-Pairs Multi-Field Transforms for Efficient Frame Interpolation

AMT: All-Pairs Multi-Field Transforms for Efficient Frame In...

引用

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

作者： Li, Zhen Zhu, Zuo-Liang Han, Ling-Hao Hou, Qibin Guo, Chun-Le Cheng, Ming-Ming Nankai Univ VCIP CS Tianjin Peoples R China

ISBN: (纸本)9798350301298

We present All-Pairs Multi-Field Transforms (AMT), a new network architecture for video frame interpolation. It is based on two essential designs. First, we build bidirectional correlation volumes for all pairs of pixels, and use the predicted bilateral flows to retrieve correlations for updating both flows and the interpolated content feature. Second, we derive multiple groups of fine-grained flow fields from one pair of updated coarse flows for performing backward warping on the input frames separately. Combining these two designs enables us to generate promising task-oriented flows and reduce the difficulties in modeling large motions and handling occluded areas during frame interpolation. These qualities promote our model to achieve state-of-the-art performance on various benchmarks with high efficiency. Moreover, our convolution-based model competes favorably compared to Transformer-based models in terms of accuracy and efficiency. Our code is available at https://***/MCG-NKU/AMT.

关键词： Low-level vision

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Zero-shot Model Diagnosis

Zero-shot Model Diagnosis

引用

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

作者： Luo, Jinqi Wang, Zhaoning Wu, Chen Henry Huang, Dong De la Torre, Fernando Carnegie Mellon Univ Pittsburgh PA 15213 USA

ISBN: (纸本)9798350301298

When it comes to deploying deep vision models, the behavior of these systems must be explicable to ensure confidence in their reliability and fairness. A common approach to evaluate deep learning models is to build a labeled test set with attributes of interest and assess how well it performs. However, creating a balanced test set (i.e., one that is uniformly sampled over all the important traits) is often time-consuming, expensive, and prone to mistakes. The question we try to address is: can we evaluate the sensitivity of deep learning models to arbitrary visual attributes without an annotated test set? This paper argues the case that Zero-shot Model Diagnosis (ZOOM) is possible without the need for a test set nor labeling. To avoid the need for test sets, our system relies on a generative model and CLIP. The key idea is enabling the user to select a set of prompts (relevant to the problem) and our system will automatically search for semantic counterfactual images (i.e., synthesized images that flip the prediction in the case of a binary classifier) using the generative model. We evaluate several visual tasks (classification, key-point detection, and segmentation) in multiple visual domains to demonstrate the viability of our methodology. Extensive experiments demonstrate that our method is capable of producing counterfactual images and offering sensitivity analysis for model diagnosis without the need for a test set.

关键词： Explainable computer vision

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Modernizing Old Photos Using Multiple References via Photorealistic Style Transfer

Modernizing Old Photos Using Multiple References via Photore...

引用

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

作者： Gunawan, Agus Kim, Soo Ye Sim, Hyeonjun Lee, Jae-Ho Kim, Munchurl Korea Adv Inst Sci & Technol Daejeon South Korea Adobe Res San Francisco CA USA ETRI Daejeon South Korea Qualcomm San Diego CA USA

ISBN: (纸本)9798350301298

This paper firstly presents old photo modernization using multiple references by performing stylization and enhancement in a unified manner. In order to modernize old photos, we propose a novel multi-reference-based old photo modernization (MROPM) framework consisting of a network MROPM-Net and a novel synthetic data generation scheme. MROPM-Net stylizes old photos using multiple references via photorealistic style transfer (PST) and further enhances the results to produce modern-looking images. Meanwhile, the synthetic data generation scheme trains the network to effectively utilize multiple references to perform modernization. To evaluate the performance, we propose a new old photos benchmark dataset (CHD) consisting of diverse natural indoor and outdoor scenes. Extensive experiments show that the proposed method outperforms other baselines in performing modernization on real old photos, even though no old photos were used during training. Moreover, our method can appropriately select styles from multiple references for each semantic region in the old photo to further improve the modernization performance.

关键词： Low-level vision

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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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Learning Distortion Invariant Representation for Image Restoration from A Causality Perspective

Learning Distortion Invariant Representation for Image Resto...

引用

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

作者： Li, Xin Li, Bingchen Jin, Xin Lan, Cuiling Chen, Zhibo Univ Sci & Technol China Hefei Peoples R China Eastern Inst Adv Study Ningbo Peoples R China Microsoft Res Asia Beijing Peoples R China

ISBN: (纸本)9798350301298

In recent years, we have witnessed the great advancement of Deep neural networks (DNNs) in image restoration. However, a critical limitation is that they cannot generalize well to real-world degradations with different degrees or types. In this paper, we are the first to propose a novel training strategy for image restoration from the causality perspective, to improve the generalization ability of DNNs for unknown degradations. Our method, termed Distortion Invariant representation Learning (DIL), treats each distortion type and degree as one specific confounder, and learns the distortion-invariant representation by eliminating the harmful confounding effect of each degradation. We derive our DIL with the back-door criterion in causality by modeling the interventions of different distortions from the optimization perspective. Particularly, we introduce counterfactual distortion augmentation to simulate the virtual distortion types and degrees as the confounders. Then, we instantiate the intervention of each distortion with a virtual model updating based on corresponding distorted images, and eliminate them from the meta-learning perspective. Extensive experiments demonstrate the generalization capability of our DIL on unseen distortion types and degrees. Our code will be available at https://***/lixinustc/Causal-IR-DIL.

关键词： Low-level vision

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ABLE-NeRF: Attention-Based Rendering with Learnable Embeddings for Neural Radiance Field

ABLE-NeRF: Attention-Based Rendering with Learnable Embeddin...

引用

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

作者： Tang, Zhe Jun Cham, Tat-Jen Zhao, Haiyu Nanyang Technol Univ S Lab Singapore Singapore Nanyang Technol Univ Singapore Singapore SenseTime Res Singapore Singapore

ISBN: (纸本)9798350301298

Neural Radiance Field (NeRF) is a popular method in representing 3D scenes by optimising a continuous volumetric scene function. Its large success which lies in applying volumetric rendering (VR) is also its Achilles' heel in producing view-dependent effects. As a consequence, glossy and transparent surfaces often appear murky. A remedy to reduce these artefacts is to constrain this VR equation by excluding volumes with back-facing normal. While this approach has some success in rendering glossy surfaces, translucent objects are still poorly represented. In this paper, we present an alternative to the physics-based VR approach by introducing a self-attention-based framework on volumes along a ray. In addition, inspired by modern game engines which utilise Light Probes to store local lighting passing through the scene, we incorporate Learnable Embeddings to capture view dependent effects within the scene. Our method, which we call ABLE-NeRF, significantly reduces 'blurry' glossy surfaces in rendering and produces realistic translucent surfaces which lack in prior art. In the Blender dataset, ABLE-NeRF achieves SOTA results and surpasses Ref-NeRF in all 3 image quality metrics PSNR, SSIM, LPIPS.

关键词： vision + graphics

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