GenAI4ELab/papercli-papers huggingface dataset:
Improving Image Restoration by Revisiting Global Information Aggregation
Authors: Xiaojie Chu; Liangyu Chen; Chengpeng Chen; Xin Lu
Venue: ECCV 2022 (main)

Abstract: "Global operations, such as global average pooling, are widely used in top-performance image restorers. They aggregate global information from input features along entire spatial dimensions but behave differently during training and inference in image restoration tasks: they are based on different regions, namely the cropped patches (from images) and the full-resolution images. This paper revisits global information aggregation and finds that the image-based features during inference have a different distribution than the patch-based features during training. This train-test inconsistency negatively impacts the performance of models, which is severely overlooked by previous works. To reduce the inconsistency and improve test-time performance, we propose a simple method called Test-time Local Converter (TLC). Our TLC converts global operations to local ones only during inference so that they aggregate features within local spatial regions rather than the entire large images. The proposed method can be applied to various global modules (e.g., normalization, channel and spatial attention) with negligible costs. Without the need for any fine-tuning, TLC improves state-of-the-art results on several image restoration tasks, including single-image motion deblurring, video deblurring, defocus deblurring, and image denoising. In particular, with TLC, our Restormer-Local improves the state-of-the-art result in single image deblurring from 32.92 dB to 33.57 dB on GoPro dataset. The code is available at https://github.com/megvii-research/tlc."
Data Association between Event Streams and Intensity Frames under Diverse Baselines
Authors: Dehao Zhang; Qiankun Ding; Peiqi Duan; Chu Zhou; Boxin Shi
Venue: ECCV 2022 (main)

Abstract: "This paper proposes a learning-based framework to associate event streams and intensity frames under diverse camera baselines, to simultaneously benefit to camera pose estimation under large baseline and depth estimation under small baseline. Based on the observation that event streams are globally sparse (a small percentage of pixels in global frames are triggered with events) and locally dense (a large percentage of pixels in local patches are triggered with events) in the spatial domain, we put forward a two-stage architecture for matching feature maps. LSparse-Net uses a large receptive field to find sparse matches while SDense-Net uses a small receptive field to find dense matches. Both two stages apply transformer modules with self-attention layers and cross-attention layers to effectively process multi-resolution features from the feature pyramid network backbone. Experimental results on public datasets show systematic performance improvement for both tasks compared to state-of-the-art methods."
D2HNet: Joint Denoising and Deblurring with Hierarchical Network for Robust Night Image Restoration
Authors: Yuzhi Zhao; Yongzhe Xu; Qiong Yan; Dingdong Yang; Xuehui Wang; Lai-Man Po
Venue: ECCV 2022 (main)

Abstract: "Night imaging with modern smartphone cameras is troublesome due to low photon count and unavoidable noise in the imaging system. Directly adjusting exposure time and ISO ratings cannot obtain sharp and noise-free images at the same time in low-light conditions. Though many methods have been proposed to enhance noisy or blurry night images, their performances on real-world night photos are still unsatisfactory due to two main reasons: 1) Limited information in a single image and 2) Domain gap between synthetic training images and real-world photos (e.g., differences in blur area and resolution). To exploit the information from successive long- and short-exposure images, we propose a learning-based pipeline to fuse them. A D2HNet framework is developed to recover a high-quality image by deblurring and enhancing a long-exposure image under the guidance of a short-exposure image. To shrink the domain gap, we leverage a two-phase DeblurNet-EnhanceNet architecture, which performs accurate blur removal on a fixed low resolution so that it is able to handle large ranges of blur in different resolution inputs. In addition, we synthesize a D2-Dataset from HD videos and experiment on it. The results on the validation set and real photos demonstrate our methods achieve better visual quality and state-of-the-art quantitative scores. The D2HNet codes and D2-Dataset can be found at https://github.com/zhaoyuzhi/D2HNet."
Learning Graph Neural Networks for Image Style Transfer
Authors: Yongcheng Jing; Yining Mao; Yiding Yang; Yibing Zhan; Mingli Song; Xinchao Wang; Dacheng Tao
Venue: ECCV 2022 (main)

Abstract: "State-of-the-art parametric and non-parametric style transfer approaches are prone to either distorted local style patterns due to global statistics alignment, or unpleasing artifacts resulting from patch mismatching. In this paper, we study a novel semi-parametric neural style transfer framework that alleviates the deficiency of both parametric and non-parametric stylization. The core idea of our approach is to establish accurate and fine-grained content-style correspondences using graph neural networks (GNNs). To this end, we develop an elaborated GNN model with content and style local patches as the graph vertices. The style transfer procedure is then modeled as the attention-based heterogeneous message passing between the style and content nodes in a learnable manner, leading to adaptive many-to-one style-content correlations at the local patch level. In addition, an elaborated deformable graph convolutional operation is introduced for cross-scale style-content matching. Experimental results demonstrate that the proposed semi-parametric image stylization approach yields encouraging results on the challenging style patterns, preserving both global appearance and exquisite details. Furthermore, by controlling the number of edges at the inference stage, the proposed method also triggers novel functionalities like diversified patch-based stylization with a single model."
DeepPS2: Revisiting Photometric Stereo Using Two Differently Illuminated Images
Authors: Ashish Tiwari; Shanmuganathan Raman
Venue: ECCV 2022 (main)

Abstract: "Estimating 3D surface normals through photometric stereo has been of great interest in computer vision research. Despite the success of existing traditional and deep learning-based methods, it is still challenging due to: (i) the requirement of three or more differently illuminated images, (ii) the inability to model unknown general reflectance, and (iii) the requirement of accurate 3D ground truth surface normals and known lighting information for training. In this work, we attempt to address an under-explored problem of photometric stereo using just two differently illuminated images, referred to as the PS2 problem. It is an intermediate case between a single image-based reconstruction method like Shape from Shading (SfS) and the traditional Photometric Stereo (PS), which requires three or more images. We propose an inverse rendering-based deep learning framework, called DeepPS2, that jointly performs surface normal, albedo, lighting estimation, and image relighting in a completely self-supervised manner with no requirement of ground truth data. We demonstrate how image relighting in conjunction with image reconstruction enhances the lighting estimation in a self-supervised setting."
Instance Contour Adjustment via Structure-Driven CNN
Authors: Shuchen Weng; Yi Wei; Ming-Ching Chang; Boxin Shi
Venue: ECCV 2022 (main)

Abstract: "Instance contour adjustment is desirable in image editing, which allows the contour of an instance in a photo to be either dilated or eroded via user sketching. This imposes several requirements for a favorable method in order to generate meaningful textures while preserving clear user-desired contours. Due to the ignorance of these requirements, the off-the-shelf image editing methods herein are unsuited. Therefore, we propose a specialized two-stage method. The first stage extracts the structural cues from the input image, and completes the missing structural cues for the adjusted area. The second stage is a structure-driven CNN which generates image textures following the guidance of the completed structural cues. In the structure-driven CNN, we redesign the context sampling strategy of the convolution operation and attention mechanism such that they can estimate and rank the relevance of the contexts based on the structural cues, and sample the top-ranked contexts regardless of their distribution on the image plane. Thus, the meaningfulness of image textures with clear and user-desired contours are guaranteed by the structure-driven CNN. In addition, our method does not require any semantic label as input, which thus ensures its well generalization capability. We evaluate our method against several baselines adapted from the related tasks, and the experimental results demonstrate its effectiveness."
Synthesizing Light Field Video from Monocular Video
Authors: Shrisudhan Govindarajan; Prasan Shedligeri; Sarah; Kaushik Mitra
Venue: ECCV 2022 (main)

Abstract: "The hardware challenges associated with light-field(LF) imaging has made it difficult for consumers to access its benefits like applications in post-capture focus and aperture control. Learning-based techniques which solve the ill-posed problem of LF reconstruction from sparse (1, 2 or 4) views have significantly reduced the requirement for complex hardware. LF video reconstruction from sparse views poses a special challenge as acquiring ground-truth for training these models is hard. Hence, we propose a self-supervised learning-based algorithm for LF video reconstruction from monocular videos. We use self-supervised geometric, photometric and temporal consistency constraints inspired from a recent self-supervised technique for LF video reconstruction from stereo video. Additionally, we propose three key techniques that are relevant to our monocular video input. We propose an explicit disocclusion handling technique that encourages the network to inpaint disoccluded regions in a LF frame, using information from adjacent input temporal frames. This is crucial for a self-supervised technique as a single input frame does not contain any information about the disoccluded regions. We also propose an adaptive low-rank representation that provides a significant boost in performance by tailoring the representation to each input scene. Finally, we also propose a novel refinement block that is able to exploit the available LF image data using supervised learning to further refine the reconstruction quality. Our qualitative and quantitative analysis demonstrates the significance of each of the proposed building blocks and also the superior results compared to previous state-of-the-art monocular LF reconstruction techniques. We further validate our algorithm by reconstructing LF videos from monocular videos acquired using a commercial GoPro camera."
Human-Centric Image Cropping with Partition-Aware and Content-Preserving Features
Authors: Bo Zhang; Li Niu; Xing Zhao; Liqing Zhang
Venue: ECCV 2022 (main)

Abstract: "Image cropping aims to find visually appealing crops in an image, which is an important yet challenging task. In this paper, we consider a specific and practical application: human-centric image cropping, which focuses on the depiction of a person. To this end, we propose a human-centric image cropping method with two novel feature designs for the candidate crop: partition-aware feature and content-preserving feature. For partition-aware feature, we divide the whole image into nine partitions based on the human bounding box and treat different partitions in a candidate crop differently conditioned on the human information. For content-preserving feature, we predict a heatmap indicating the important content to be included in a good crop, and extract the geometric relation between the heatmap and a candidate crop. Extensive experiments demonstrate that our method can perform favorably against state-of-the-art image cropping methods on human-centric image cropping task. Code is available at https://github.com/bcmi/Human-Centric-Image-Cropping."
DeMFI: Deep Joint Deblurring and Multi-Frame Interpolation with Flow-Guided Attentive Correlation and Recursive Boosting
Authors: Jihyong Oh; Munchurl Kim
Venue: ECCV 2022 (main)

Abstract: "We propose a novel joint deblurring and multi-frame interpolation (DeMFI) framework in a two-stage manner, called DeMFINet, which converts blurry videos of lower-frame-rate to sharp videos at higher-frame-rate based on flow-guided attentive-correlation-based feature bolstering (FAC-FB) module and recursive boosting (RB), in terms of multi-frame interpolation (MFI). Its baseline version performs featureflow-based warping with FAC-FB module to obtain a sharp-interpolated frame as well to deblur two center-input frames. Its extended version further improves the joint performance based on pixel-flow-based warping with GRU-based RB. Our FAC-FB module effectively gathers the distributed blurry pixel information over blurry input frames in featuredomain to improve the joint performances. RB trained with recursive boosting loss enables DeMFI-Net to adequately select smaller RB iterations for a faster runtime during inference, even after the training is finished. As a result, our DeMFI-Net achieves state-of-the-art (SOTA) performances for diverse datasets with significant margins compared to recent joint methods. All source codes, including pretrained DeMFI-Net, are publicly available at https://github.com/JihyongOh/DeMFI."
Neural Image Representations for Multi-Image Fusion and Layer Separation
Authors: Seonghyeon Nam; Marcus A. Brubaker; Michael S. Brown
Venue: ECCV 2022 (main)

Abstract: "We propose a framework for aligning and fusing multiple images into a single view using neural image representations (NIRs), also known as implicit or coordinate-based neural representations. Our framework targets burst images that exhibit camera ego motion and potential changes in the scene. We describe different strategies for alignment depending on the nature of the scene motion---namely, perspective planar (i.e., homography), optical flow with minimal scene change, and optical flow with notable occlusion and disocclusion. With the neural image representation, our framework effectively combines multiple inputs into a single canonical view without the need for selecting one of the images as a reference frame. We demonstrate how to use this multi-frame fusion framework for various layer separation tasks."
Bringing Rolling Shutter Images Alive with Dual Reversed Distortion
Authors: Zhihang Zhong; Mingdeng Cao; Xiao Sun; Zhirong Wu; Zhongyi Zhou; Yinqiang Zheng; Stephen Lin; Imari Sato
Venue: ECCV 2022 (main)

Abstract: "Rolling shutter (RS) distortion can be interpreted as the result of picking a row of pixels from instant global shutter (GS) frames over time during the exposure of the RS camera. This means that the information of each instant GS frame is partially, yet sequentially, embedded into the row-dependent distortion. Inspired by this fact, we address the challenging task of reversing this process, i.e., extracting undistorted GS frames from images suffering from RS distortion. However, since RS distortion is coupled with other factors such as readout settings and the relative velocity of scene elements to the camera, models that only exploit the geometric correlation between temporally adjacent images suffer from poor generality in processing data with different readout settings and dynamic scenes with both camera motion and object motion. In this paper, instead of two consecutive frames, we propose to exploit a pair of images captured by dual RS cameras with reversed RS directions for this highly challenging task. Grounded on the symmetric and complementary nature of dual reversed distortion, we develop a novel end-to-end model, IFED, to generate dual optical flow sequence through iterative learning of the velocity field during the RS time. Extensive experimental results demonstrate that IFED is superior to naive cascade schemes, as well as the state-of-the-art which utilizes adjacent RS images. Most importantly, although it is trained on a synthetic dataset, IFED is shown to be effective at retrieving GS frame sequences from real-world RS distorted images of dynamic scenes. Code is available at https://github.com/zzh-tech/Dual-Reversed-RS."
FILM: Frame Interpolation for Large Motion
Authors: Fitsum Reda; Janne Kontkanen; Eric Tabellion; Deqing Sun; Caroline Pantofaru; Brian Curless
Venue: ECCV 2022 (main)

Abstract: We present a frame interpolation algorithm that synthesizes an engaging slow-motion video from near-duplicate photos which often exhibit large scene motion. Near-duplicates interpolation is an interesting new application, but large motion poses challenges to existing methods. To address this issue, we adapt a feature extractor that shares weights across the scales, and present a "scale-agnostic" motion estimator. It relies on the intuition that large motion at finer scales should be similar to small motion at coarser scales, which boosts the number of available pixels for large motion supervision. To inpaint wide disocclusions caused by large motion and synthesize crisp frames, we propose to optimize our network with the Gram matrix loss that measures the correlation difference between features. To simplify the training process, we further propose a unified single-network approach that removes the reliance on additional optical-flow or depth network and is trainable from frame triplets alone. Our approach outperforms state-of-the-art methods on the Xiph large motion benchmark while performing favorably on Vimeo-90K, Middlebury and UCF101. Codes and pre-trained models are available at https://film-net.github.io.
Video Interpolation by Event-Driven Anisotropic Adjustment of Optical Flow
Authors: Song Wu; Kaichao You; Weihua He; Chen Yang; Yang Tian; Yaoyuan Wang; Ziyang Zhang; Jianxing Liao
Venue: ECCV 2022 (main)

Abstract: "Video frame interpolation is a challenging task due to the ever-changing real-world scene. Previous methods often calculate the bi-directional optical flows and then predict the intermediate optical flows under the linear motion assumptions, leading to isotropic intermediate flow generation. Follow-up research obtained anisotropic adjustment through estimated higher-order motion information with extra frames. Based on the motion assumptions, their methods are hard to model the complicated motion in real scenes. In this paper, we propose an end-to-end training method A^2OF for video frame interpolation with event-driven Anisotropic Adjustment of Optical Flows. Specifically, we use events to generate optical flow distribution masks for the intermediate optical flow, which can model the complicated motion between two frames. Our proposed method outperforms the previous methods in video frame interpolation, taking supervised event-based video interpolation to a higher stage."
EvAC3D: From Event-Based Apparent Contours to 3D Models via Continuous Visual Hulls
Authors: Ziyun Wang; Kenneth Chaney; Kostas Daniilidis
Venue: ECCV 2022 (main)

Abstract: "3D reconstruction from multiple views is a successful computer vision field with multiple deployments in applications. State of the art is based on traditional RGB frames that enable optimization of photo-consistency cross views. In this paper, we study the problem of 3D reconstruction from event-cameras, motivated by the advantages of event-based cameras in terms of low power and latency as well as by the biological evidence that eyes in nature capture the same data and still perceive well 3D shape. The foundation of our hypothesis that 3D-reconstruction is feasible using events lies in the information contained in the occluding contours and in the continuous scene acquisition with events. We propose Apparent Contour Events (ACE), a novel event-based representation that defines the geometry of the apparent contour of an object. We represent ACE by a spatially and temporally continuous implicit function defined in the event x-y-t space. Furthermore, we design a novel continuous Voxel Carving algorithm enabled by the high temporal resolution of the Apparent Contour Events. To evaluate the performance of the method, we collect MOEC-3D, a 3D event dataset of a set of common real-world objects. We demonstrate EvAC3D’s ability to reconstruct high-fidelity mesh surfaces from real event sequences while allowing the refinement of the 3D reconstruction for each individual event."
DCCF: Deep Comprehensible Color Filter Learning Framework for High-Resolution Image Harmonization
Authors: Ben Xue; Shenghui Ran; Quan Chen; Rongfei Jia; Binqiang Zhao; Xing Tang
Venue: ECCV 2022 (main)

Abstract: "Image color harmonization algorithm aims to automatically match the color distribution of foreground and background images captured in different conditions. Previous deep learning based models neglect two issues that are critical for practical applications, namely high resolution (HR) image processing and model comprehensibility. In this paper, we propose a novel Deep Comprehensible Color Filter (DCCF) learning framework for high-resolution image harmonization. Specifically, DCCF first downsamples the original input image to its low-resolution (LR) counter-part, then learns four human comprehensible neural filters (i.e. hue, saturation, value and attentive rendering filters) in an end-to-end manner, finally applies these filters to the original input image to get the harmonized result. Benefiting from the comprehensible neural filters, we could provide a simple yet efficient handler for users to cooperate with deep model to get the desired results with very little effort when necessary. Extensive experiments demonstrate the effectiveness of DCCF learning framework and it outperforms state-of-the-art post-processing method on iHarmony4 dataset on images’ full-resolutions by achieving 7.63% and 1.69% relative improvements on MSE and PSNR respectively. Our code is available at https://github.com/rockeyben/DCCF."
SelectionConv: Convolutional Neural Networks for Non-Rectilinear Image Data
Authors: David Hart; Michael Whitney; Bryan Morse
Venue: ECCV 2022 (main)

Abstract: "Convolutional Neural Networks have revolutionized vision applications. There are image domains and representations, however, that cannot be handled by standard CNNs (e.g., spherical images, superpixels). Such data are usually processed using networks and algorithms specialized for each type. In this work, we show that it may not always be necessary to use specialized neural networks to operate on such spaces. Instead, we introduce a new structured graph convolution operator that can copy 2D convolution weights, transferring the capabilities of already trained traditional CNNs to our new graph network. This network can then operate on any data that can be represented as a positional graph. By converting non-rectilinear data to a graph, we can apply these convolutions on these irregular image domains without requiring training on large domain-specific datasets."