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Local Implicit Normalizing Flow for Arbitrary-Scale Image Super-Resolution

Local Implicit Normalizing Flow for Arbitrary-Scale Image Su...

引用

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

作者： Yao, Jie-En Tsao, Li-Yuan Lo, Yi-Chen Tseng, Roy Chang, Chia-Che Lee, Chun-Yi Natl Tsing Hua Univ 1 ElsaLab Hsinchu Taiwan MediaTek Inc Hsinchu Taiwan

ISBN: (纸本)9798350301298

Flow-based methods have demonstrated promising results in addressing the ill-posed nature of super-resolution (SR) by learning the distribution of high-resolution (HR) images with the normalizing flow. However, these methods can only perform a predefined fixed-scale SR, limiting their potential in real-world applications. Meanwhile, arbitrary-scale SR has gained more attention and achieved great progress. Nonetheless, previous arbitrary-scale SR methods ignore the ill-posed problem and train the model with per-pixel L1 loss, leading to blurry SR outputs. In this work, we propose "Local Implicit Normalizing Flow" (LINF) as a unified solution to the above problems. LINF models the distribution of texture details under different scaling factors with normalizing flow. Thus, LINF can generate photo-realistic HR images with rich texture details in arbitrary scale factors. We evaluate LINF with extensive experiments and show that LINF achieves the state-of-the-art perceptual quality compared with prior arbitrary-scale SR methods.

关键词： Low-level vision

来源：评论

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On the Importance of Accurate Geometry Data for Dense 3D vision Tasks

On the Importance of Accurate Geometry Data for Dense 3D Vis...

引用

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

作者： Jung, HyunJun Ruhkamp, Patrick Zhai, Guangyao Brasch, Nikolas Li, Yitong Verdie, Yannick Song, Jifei Zhou, Yiren Armagan, Anil Ilic, Slobodan Leonardis, Ales Navab, Nassir Busam, Benjamin Tech Univ Munich Munich Germany Dwe Ai Munich Germany Huawei Noahs Ark Lab Montreal PQ Canada Siemens AG Munich Germany

ISBN: (纸本)9798350301298

Learning-based methods to solve dense 3D vision problems typically train on 3D sensor data. The respectively used principle of measuring distances provides advantages and drawbacks. These are typically not compared nor discussed in the literature due to a lack of multi-modal datasets. Texture-less regions are problematic for structure from motion and stereo, reflective material poses issues for active sensing, and distances for translucent objects are intricate to measure with existing hardware. Training on inaccurate or corrupt data induces model bias and hampers generalisation capabilities. These effects remain unnoticed if the sensor measurement is considered as ground truth during the evaluation. This paper investigates the effect of sensor errors for the dense 3D vision tasks of depth estimation and reconstruction. We rigorously show the significant impact of sensor characteristics on the learned predictions and notice generalisation issues arising from various technologies in everyday household environments. For evaluation, we introduce a carefully designed dataset1 comprising measurements from commodity sensors, namely D-ToF, I-ToF, passive/active stereo, and monocular RGB+P. Our study quantifies the considerable sensor noise impact and paves the way to improved dense vision estimates and targeted data fusion.

关键词： 3D from single images

来源：评论

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Event-based Video Frame Interpolation with Cross-Modal Asymmetric Bidirectional Motion Fields

Event-based Video Frame Interpolation with Cross-Modal Asymm...

引用

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

作者： Kim, Taewoo Chae, Yujeong Jang, Hyun-Kurl Yoon, Kuk-Jin Korea Adv Inst Sci & Technol Daejeon South Korea

ISBN: (纸本)9798350301298

Video Frame Interpolation (VFI) aims to generate intermediate video frames between consecutive input frames. Since the event cameras are bio-inspired sensors that only encode brightness changes with a micro-second temporal resolution, several works utilized the event camera to enhance the performance of VFI. However, existing methods estimate bidirectional inter-frame motion fields with only events or approximations, which can not consider the complex motion in real-world scenarios. In this paper, we propose a novel event-based VFI framework with cross-modal asymmetric bidirectional motion field estimation. In detail, our EIF-BiOFNet utilizes each valuable characteristic of the events and images for direct estimation of inter-frame motion fields without any approximation methods. Moreover, we develop an interactive attention-based frame synthesis network to efficiently leverage the complementary warping-based and synthesis-based features. Finally, we build a large-scale event-based VFI dataset, ERF-X170FPS, with a high frame rate, extreme motion, and dynamic textures to overcome the limitations of previous event-based VFI datasets. Extensive experimental results validate that our method shows significant performance improvement over the state-of-the-art VFI methods on various datasets. Our project pages are available at: https://***/intelpro/CBMNet

关键词： Low-level vision

来源：评论

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HarvestNet: A Dataset for Detecting Smallholder Farming Activity Using Harvest Piles and Remote Sensing

HarvestNet: A Dataset for Detecting Smallholder Farming Acti...

引用

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

作者： Xu, Jonathan Elmustafa, Amna Weldegebriel, Liya Negash, Emnet Lee, Richard Meng, Chenlin Ermon, Stefano Lobell, David Stanford Univ Stanford CA 94305 USA Univ Waterloo Waterloo ON Canada Univ Ghent Ghent Belgium Mekelle Univ Mekele Ethiopia

ISBN: (纸本)9798350365474

Small farms contribute to a large share of the productive land in developing countries. In regions such as subSaharan Africa, where 80% of farms are small (under 2 ha in size), the task of mapping smallholder cropland is an important part of tracking sustainability measures such as crop productivity. However, the visually diverse and nuanced appearance of small farms has limited the effectiveness of traditional approaches to cropland mapping. Here we introduce a new approach based on the detection of harvest piles characteristic of many smallholder systems throughout the world. We present HarvestNet, a dataset for mapping the presence of farms in the Ethiopian regions of Tigray and Amhara during 2020-2023, collected using expert knowledge and satellite images, totaling 7k hand-labeled images and 2k ground-collected labels. We also benchmark a set of baselines, including SOTA models in remote sensing, with our best models having around 80% classification performance on hand labelled data and 90% and 98% accuracy on ground truth data for Tigray and Amhara, respectively. We also perform a visual comparison with a widely used pre-existing coverage map and show that our model detects an extra 56,621 hectares of cropland in Tigray. We conclude that remote sensing of harvest piles can contribute to more timely and accurate cropland assessments in food insecure regions. The dataset can be accessed through https://***/s/45a7b45556b90a9a11d2, while the code for the dataset and benchmarks is publicly available at https://***/jonxuxu/harvestpiles.

关键词： agriculture computer vision dataset harvest piles machine learning Remote sensing sustainability

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GENIE: Show Me the Data for Quantization

GENIE: Show Me the Data for Quantization

引用

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

作者： Jeon, Yongkweon Lee, Chungman Kim, Ho-young Samsung Res Berkeley Hts NJ 07922 USA

ISBN: (纸本)9798350301298

Zero-shot quantization is a promising approach for developing lightweight deep neural networks when data is inaccessible owing to various reasons, including cost and issues related to privacy. By exploiting the learned parameters (mu and sigma) of batch normalization layers in an FP32-pre-trained model, zero-shot quantization schemes focus on generating synthetic data. Subsequently, they distill knowledge from the pre-trained model (teacher) to the quantized model (student) such that the quantized model can be optimized with the synthetic dataset. However, thus far, zero-shot quantization has primarily been discussed in the context of quantization-aware training methods, which require task-specific losses and long-term optimization as much as retraining. We thus introduce a post-training quantization scheme for zero-shot quantization that produces high-quality quantized networks within a few hours. Furthermore, we propose a framework called GENIE that generates data suited for quantization. With the data synthesized by GENIE, we can produce robust quantized models without real datasets, which is comparable to few-shot quantization. We also propose a post-training quantization algorithm to enhance the performance of quantized models. By combining them, we can bridge the gap between zero-shot and few-shot quantization while significantly improving the quantization performance compared to that of existing approaches. In other words, we can obtain a unique state-of-the-art zero-shot quantization approach. The code is available at https://***/SamsungLabs/Genie.

关键词： Efficient and scalable vision

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Samples with Low Loss Curvature Improve Data Efficiency

Samples with Low Loss Curvature Improve Data Efficiency

引用

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

作者： Garg, Isha Roy, Kaushik Purdue Univ W Lafayette IN 47907 USA

ISBN: (纸本)9798350301298

In this paper, we study the second order properties of the loss of trained deep neural networks with respect to the training data points to understand the curvature of the loss surface in the vicinity of these points. We find that there is an unexpected concentration of samples with very low curvature. We note that these low curvature samples are largely consistent across completely different architectures, and identifiable in the early epochs of training. We show that the curvature relates to the 'cleanliness' of the data points, with low curvatures samples corresponding to clean, higher clarity samples, representative of their category. Alternatively, high curvature samples are often occluded, have conflicting features and visually atypical of their category. Armed with this insight, we introduce SLo-Curves, a novel coreset identification and training algorithm. SLo-curves identifies the samples with low curvatures as being more data-efficient and trains on them with an additional regularizer that penalizes high curvature of the loss surface in their vicinity. We demonstrate the efficacy of SLo-Curves on CIFAR-10 and CIFAR-100 datasets, where it outperforms state of the art coreset selection methods at small coreset sizes by up to 9%. The identified coresets generalize across architectures, and hence can be pre-computed to generate condensed versions of datasets for use in downstream tasks. Code is available at https://***/ishagarg/SLo-Curves.

关键词： Efficient and scalable vision

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Compacting Binary Neural Networks by Sparse Kernel Selection

Compacting Binary Neural Networks by Sparse Kernel Selection

引用

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

作者： Wang, Yikai Huang, Wenbing Dong, Yinpeng Sun, Fuchun Yao, Anbang Tsinghua Univ Dept Comp Sci & Technol State Key Lab Intelligent Technol & Syst BNRist Ctr Beijing Peoples R China Renmin Univ China Gaoling Sch Artificial Intelligence Suzhou Peoples R China RealAI Shenzhen Peoples R China Intel Labs China Shenzhen Peoples R China

ISBN: (纸本)9798350301298

Binary Neural Network (BNN) represents convolution weights with 1-bit values, which enhances the efficiency of storage and computation. This paper is motivated by a previously revealed phenomenon that the binary kernels in successful BNNs are nearly power-law distributed: their values are mostly clustered into a small number of codewords. This phenomenon encourages us to compact typical BNNs and obtain further close performance through learning non-repetitive kernels within a binary kernel subspace. Specifically, we regard the binarization process as kernel grouping in terms of a binary codebook, and our task lies in learning to select a smaller subset of codewords from the full codebook. We then leverage the Gumbel-Sinkhorn technique to approximate the codeword selection process, and develop the Permutation Straight-Through Estimator (PSTE) that is able to not only optimize the selection process end-to-end but also maintain the non-repetitive occupancy of selected codewords. Experiments verify that our method reduces both the model size and bit-wise computational costs, and achieves accuracy improvements compared with state-of-the-art BNNs under comparable budgets.

关键词： Efficient and scalable vision

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Tell Me What Happened: Unifying Text-guided Video Completion via Multimodal Masked Video Generation

Tell Me What Happened: Unifying Text-guided Video Completion...

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

作者： Fu, Tsu-Jui Yu, Licheng Zhang, Ning Fu, Cheng-Yang Su, Jong-Chyi Wang, William Yang Bell, Sean UC Santa Barbara Santa Barbara CA 93106 USA Meta Menlo Pk CA USA NEC Labs Amer Princeton NJ USA

ISBN: (纸本)9798350301298

Generating a video given the first several static frames is challenging as it anticipates reasonable future frames with temporal coherence. Besides video prediction, the ability to rewind from the last frame or infilling between the head and tail is also crucial, but they have rarely been explored for video completion. Since there could be different outcomes from the hints of just a few frames, a system that can follow natural language to perform video completion may significantly improve controllability. Inspired by this, we introduce a novel task, text-guided video completion (TVC), which requests the model to generate a video from partial frames guided by an instruction. We then propose Multimodal Masked Video Generation (MMVG) to address this TVC task. During training, MMVG discretizes the video frames into visual tokens and masks most of them to perform video completion from any time point. At inference time, a single MMVG model can address all 3 cases of TVC, including video prediction, rewind, and infilling, by applying corresponding masking conditions. We evaluate MMVG in various video scenarios, including egocentric, animation, and gaming. Extensive experimental results indicate that MMVG is effective in generating high-quality visual appearances with text guidance for TVC.

关键词： and reasoning language vision

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Spectral Enhanced Rectangle Transformer for Hyperspectral Image Denoising

Spectral Enhanced Rectangle Transformer for Hyperspectral Im...

引用

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

作者： Li, Miaoyu Liu, Ji Fu, Ying Zhang, Yulun Dou, Dejing Beijing Inst Technol Beijing Peoples R China Baidu Inc Beijing Peoples R China Swiss Fed Inst Technol Zurich Switzerland BCG X Boston MA USA

ISBN: (纸本)9798350301298

Denoising is a crucial step for hyperspectral image (HSI) applications. Though witnessing the great power of deep learning, existing HSI denoising methods suffer from limitations in capturing the non-local self-similarity. Transformers have shown potential in capturing long-range dependencies, but few attempts have been made with specifically designed Transformer to model the spatial and spectral correlation in HSIs. In this paper, we address these issues by proposing a spectral enhanced rectangle Transformer, driving it to explore the non-local spatial similarity and global spectral low-rank property of HSIs. For the former, we exploit the rectangle self-attention horizontally and vertically to capture the non-local similarity in the spatial domain. For the latter, we design a spectral enhancement module that is capable of extracting global underlying low-rank property of spatial-spectral cubes to suppress noise, while enabling the interactions among non-overlapping spatial rectangles. Extensive experiments have been conducted on both synthetic noisy HSIs and real noisy HSIs, showing the effectiveness of our proposed method in terms of both objective metric and subjective visual quality. The code is available at https://***/MyuLi/SERT.

关键词： Low-level vision

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Visual Atoms: Pre-training vision Transformers with Sinusoidal Waves

Visual Atoms: Pre-training Vision Transformers with Sinusoid...

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

作者： Takashima, Sora Hayamizu, Ryo Inoue, Nakamasa Kataoka, Hirokatsu Yokota, Rio Natl Inst Adv Ind Sci & Technol Tokyo Japan Tokyo Inst Technol Tokyo Japan

ISBN: (纸本)9798350301298

Formula-driven supervised learning (FDSL) has been shown to be an effective method for pre-training vision transformers, where ExFractalDB-21k was shown to exceed the pre-training effect of ImageNet-21k. These studies also indicate that contours mattered more than textures when pre-training vision transformers. However, the lack of a systematic investigation as to why these contour-oriented synthetic datasets can achieve the same accuracy as real datasets leaves much room for skepticism. In the present work, we develop a novel methodology based on circular harmonics for systematically investigating the design space of contour-oriented synthetic datasets. This allows us to efficiently search the optimal range of FDSL parameters and maximize the variety of synthetic images in the dataset, which we found to be a critical factor. When the resulting new dataset VisualAtom-21k is used for pre-training ViTBase, the top-1 accuracy reached 83.7% when fine-tuning on ImageNet-1k. This is only 0.5% difference from the top-1 accuracy (84.2%) achieved by the JFT-300M pre-training, even though the scale of images is 1/14. Unlike JFT-300M which is a static dataset, the quality of synthetic datasets will continue to improve, and the current work is a testament to this possibility. FDSL is also free of the common issues associated with real images, e.g. privacy/copyright issues, labeling costs/errors, and ethical biases.

关键词： Datasets and evaluation

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