GenAI4ELab/papercli-papers huggingface dataset:
Metric Learning Based Interactive Modulation for Real-World Super-Resolution
Authors: Chong Mou; Yanze Wu; Xintao Wang; Chao Dong; Jian Zhang; Ying Shan
Venue: ECCV 2022 (main)

Abstract: "Interactive image restoration aims to restore images by adjusting several controlling coefficients, which determine the restoration strength. Existing methods are restricted in learning the controllable functions under the supervision of known degradation types and levels. They usually suffer from a severe performance drop when the real degradation is different from their assumptions. Such a limitation is due to the complexity of real-world degradations, which can not provide explicit supervision to the interactive modulation during training. However, how to realize the interactive modulation in real-world super-resolution has not yet been studied. In this work, we present a Metric Learning based Interactive Modulation for Real-World \Super-Resolution (MM-RealSR). Specifically, we propose an unsupervised degradation estimation strategy to estimate the degradation level in real-world scenarios. Instead of using known degradation levels as explicit supervision to the interactive mechanism, we propose a metric learning strategy to map the unquantifiable degradation levels in real-world scenarios to a metric space, which is trained in an unsupervised manner. Moreover, we introduce an anchor point strategy in the metric learning process to normalize the distribution of metric space. Extensive experiments demonstrate that the proposed MM-RealSR achieves excellent modulation and restoration performance in real-world super-resolution. Codes are available at https://github.com/TencentARC/MM-RealSR."
Dynamic Dual Trainable Bounds for Ultra-Low Precision Super-Resolution Networks
Authors: Yunshan Zhong; Mingbao Lin; Xunchao Li; Ke Li; Yunhang Shen; Fei Chao; Yongjian Wu; Rongrong Ji
Venue: ECCV 2022 (main)

Abstract: "Light-weight super-resolution (SR) models have received considerable attention for their serviceability in mobile devices. Many efforts employ network quantization to compress SR models. However, these methods suffer from severe performance degradation when quantizing the SR models to ultra-low precision (e.g., 2-bit and 3-bit) with the low-cost layer-wise quantizer. In this paper, we identify that the performance drop comes from the contradiction between the layer-wise symmetric quantizer and the highly asymmetric activation distribution in SR models. This discrepancy leads to either a waste on the quantization levels or detail loss in reconstructed images. Therefore, we propose a novel activation quantizer, referred to as Dynamic Dual Trainable Bounds (DDTB), to accommodate the asymmetry of the activations. Specifically, DDTB innovates in: 1) A layer-wise quantizer with trainable upper and lower bounds to tackle the highly asymmetric activations. 2) A dynamic gate controller to adaptively adjust the upper and lower bounds at runtime to overcome the drastically varying activation ranges over different samples. To reduce the extra overhead, the dynamic gate controller is quantized to 2-bit and applied to only part of the SR networks according to the introduced dynamic intensity. Extensive experiments demonstrate that our DDTB exhibits significant performance improvements in ultra-low precision. For example, our DDTB achieves a 0.70dB PSNR increase on Urban100 benchmark when quantizing EDSR to 2-bit and scaling up output images to x4."
OSFormer: One-Stage Camouflaged Instance Segmentation with Transformers
Authors: Jialun Pei; Tianyang Cheng; Deng-Ping Fan; He Tang; Chuanbo Chen; Luc Van Gool
Venue: ECCV 2022 (main)

Abstract: "We present OSFormer, the first one-stage transformer framework for camouflaged instance segmentation (CIS). OSFormer is based on two key designs. First, we design a location-sensing transformer (LST) to obtain the location label and instance-aware parameters by introducing the location-guided queries and the blend-convolution feed-forward network. Second, we develop a coarse-to-fine fusion (CFF) to merge diverse context information from the LST encoder and CNN backbone. Coupling these two components enables OSFormer to efficiently blend local features and long-range context dependencies for predicting camouflaged instances. Compared with two-stage frameworks, our OSFormer reaches 41% AP and achieves good convergence efficiency without requiring enormous training data, i.e., only 3,040 samples under 60 epochs. Code link: https://github.com/PJLallen/OSFormer."
Highly Accurate Dichotomous Image Segmentation
Authors: Xuebin Qin; Hang Dai; Xiaobin Hu; Deng-Ping Fan; Ling Shao; Luc Van Gool
Venue: ECCV 2022 (main)

Abstract: "We present a systematic study on a new task called dichotomous image segmentation (DIS), which aims to segment highly accurate objects from natural images. To this end, we collected the first large-scale DIS dataset, called DIS5K, which contains 5,470 high-resolution (e.g., 2K, 4K or larger) images covering camouflaged, salient, or meticulous objects in various backgrounds. DIS is annotated with extremely fine-grained labels. Besides, we introduce a simple intermediate supervision baseline (IS-Net) using both feature-level and mask-level guidance for DIS model training. IS-Net outperforms various cutting-edge baselines on the proposed DIS5K, making it a general self-learned supervision network that can facilitate future research in DIS. Further, we design a new metric called human correction efforts (HCE) which approximates the number of mouse clicking operations required to correct the false positives and false negatives. HCE is utilized to measure the gap between models and real-world applications and thus can complement existing metrics. Finally, we conduct the largest-scale benchmark, evaluating 16 representative segmentation models, providing a more insightful discussion regarding object complexities, and showing several potential applications (e.g., background removal, art design, 3D reconstruction). Hoping these efforts can open up promising directions for both academic and industries. Project page: https://xuebinqin.github.io/dis/index.html."
Boosting Supervised Dehazing Methods via Bi-Level Patch Reweighting
Authors: Xingyu Jiang; Hongkun Dou; Chengwei Fu; Bingquan Dai; Tianrun Xu; Yue Deng
Venue: ECCV 2022 (main)

Abstract: "Natural images can suffer from non-uniform haze distributions in different regions. However, this important fact is hardly considered in existing supervised dehazing methods, in which all training patches are accounted for equally in the loss design. These supervised methods may fail in making promising recoveries on some regions contaminated by heavy hazes. Therefore, for a more reasonable dehazing losses design, the varying importance of different training patches should be taken into account. Such rationale is exactly in line with the process of human learning that difficult concepts always require more practice in learning. To this end, we propose a bi-level dehazing (BILD) framework by designing an internal loop for weighted supervised dehazing and an external loop for training patch reweighting. With simple derivations, we show the gradients of BILD exhibit natural connections with policy gradient and can thus explain the BILD objective by the rewarding mechanism in reinforcement learning. The BILD is not a new dehazing method per se, it is better recognized as a flexible framework that can seamlessly work with general supervised dehazing approaches for their performance boosting."
Flow-Guided Transformer for Video Inpainting
Authors: Kaidong Zhang; Jingjing Fu; Dong Liu
Venue: ECCV 2022 (main)

Abstract: "We propose a flow-guided transformer, which innovatively leverage the motion discrepancy exposed by optical flows to instruct the attention retrieval in transformer for high fidelity video inpainting. More specially, we design a novel flow completion network to complete the corrupted flows by exploiting the relevant flow features in a local temporal window. With the completed flows, we propagate the content across video frames, and adopt the flow-guided transformer to synthesize the rest corrupted regions. We decouple transformers along temporal and spatial dimension, so that we can easily integrate the locally relevant completed flows to instruct spatial attention only. Furthermore, we design a flow-reweight module to precisely control the impact of completed flows on each spatial transformer. For the sake of efficiency, we introduce window partition strategy to both spatial and temporal transformers. Especially in spatial transformer, we design a dual perspective spatial MHSA, which integrates the global tokens to the window-based attention. Extensive experiments demonstrate the effectiveness of the proposed method qualitatively and quantitatively. Codes are available at https://github.com/hitachinsk/FGT."
Shift-tolerant Perceptual Similarity Metric
Authors: Abhijay Ghildyal; Feng Liu
Venue: ECCV 2022 (main)

Abstract: "Existing perceptual similarity metrics assume an image and its reference are well aligned. As a result, these metrics are often sensitive to a small alignment error that is imperceptible to the human eyes. This paper studies the effect of small misalignment, specifically a small shift between the input and reference image, on existing metrics, and accordingly develops a shift-tolerant similarity metric. This paper builds upon LPIPS, a widely used learned perceptual similarity metric, and explores architectural design considerations to make it robust against imperceptible misalignment. Specifically, we study a wide spectrum of neural network elements, such as anti-aliasing filtering, pooling, striding, padding, and skip connection, and discuss their roles in making a robust metric. Based on our studies, we develop a new deep neural network-based perceptual similarity metric. Our experiments show that our metric is tolerant to imperceptible shifts while being consistent with the human similarity judgment. Code is available at https://tinyurl.com/5n85r28r."
Perception-Distortion Balanced ADMM Optimization for Single-Image Super-Resolution
Authors: Yuehan Zhang; Bo Ji; Jia Hao; Angela Yao
Venue: ECCV 2022 (main)

Abstract: "In image super-resolution, both pixel-wise accuracy and perceptual fidelity are desirable. However, most deep learning methods only achieve high performance in one aspect due to the perception-distortion trade-off, and works that successfully balance the trade-off rely on fusing results from separately trained models with ad-hoc post-processing. In this paper, we propose a novel super-resolution model with a low-frequency constraint (LFc-SR), which balances the objective and perceptual quality through a single model and yields super-resolved images with high PSNR and perceptual scores. We further introduce an ADMM-based alternating optimization method for the non-trivial learning of the constrained model. Experiments showed that our method, without cumbersome post-processing procedures, achieved the state-of-the-art performance. The code is available at https://github.com/Yuehan717/PDASR."
VQFR: Blind Face Restoration with Vector-Quantized Dictionary and Parallel Decoder
Authors: Yuchao Gu; Xintao Wang; Liangbin Xie; Chao Dong; Gen Li; Ying Shan; Ming-Ming Cheng
Venue: ECCV 2022 (main)

Abstract: "Although generative facial prior and geometric prior have recently demonstrated high-quality results for blind face restoration, producing fine-grained facial details faithful to inputs remains a challenging problem. Motivated by the classical dictionary-based methods and the recent vector quantization (VQ) technique, we propose a VQ-based face restoration method - VQFR. VQFR takes advantage of high-quality low-level feature banks extracted from high-quality faces and can thus help recover realistic facial details. However, the simple application of the VQ codebook cannot achieve good results with faithful details and identity preservation. Therefore, we further introduce two special network designs. 1). We first investigate the compression patch size in the VQ codebook and find that the VQ codebook designed with a proper compression patch size is crucial to balance the quality and fidelity. 2). To further fuse low-level features from inputs while not “contaminating” the realistic details generated from the VQ codebook, we proposed a parallel decoder consisting of a texture decoder and a main decoder. Those two decoders then interact with a texture warping module with deformable convolution. Equipped with the VQ codebook as a facial detail dictionary and the parallel decoder design, the proposed VQFR can largely enhance the restored quality of facial details while keeping the fidelity to previous methods."
Uncertainty Learning in Kernel Estimation for Multi-stage Blind Image Super-Resolution
Authors: Zhenxuan Fang; Weisheng Dong; Xin Li; Jinjian Wu; Leida Li; Guangming Shi
Venue: ECCV 2022 (main)

Abstract: "Conventional wisdom in blind super-resolution (SR) first estimates the unknown degradation from the low-resolution image and then exploits the degradation information for image reconstruction. Such sequential approaches suffer from two fundamental weaknesses - i.e., the lack of robustness (the performance drops when the estimated degradation is inaccurate) and the lack of transparency (network architectures are heuristic without incorporating domain knowledge). To address these issues, we propose a joint Maximum a Posterior (MAP) approach for estimating the unknown kernel and high-resolution image simultaneously. Our method first introduces uncertainty learning in the latent space when estimating the blur kernel, aiming at improving the robustness to the estimation error. Then we propose a novel SR network by unfolding the joint MAP estimator with a learned Laplacian Scale Mixture (LSM) prior and the estimated kernel. We have also developed a novel approach of estimating both the scale prior coefficient and the local means of the LSM model through a deep convolutional neural network (DCNN). All parameters of the MAP estimation algorithm and the DCNN parameters are jointly optimized through end-to-end training. Extensive experiments on both synthetic and real-world images show that our method achieves state-of-the-art performance for the task of blind image SR."
Learning Spatio-Temporal Downsampling for Effective Video Upscaling
Authors: Xiaoyu Xiang; Yapeng Tian; Vijay Rengarajan; Lucas D. Young; Bo Zhu; Rakesh Ranjan
Venue: ECCV 2022 (main)

Abstract: "Downsampling is one of the most basic image processing operations. Improper spatio-temporal downsampling applied on videos can cause aliasing issues such as moiré patterns in space and the wagon-wheel effect in time. Consequently, the inverse task of upscaling a low-resolution, low frame-rate video in space and time becomes a challenging ill-posed problem due to information loss and aliasing artifacts. In this paper, we aim to solve the space-time aliasing problem by learning a spatio-temporal downsampler. Towards this goal, we propose a neural network framework that jointly learns spatio-temporal downsampling and upsampling. It enables the downsampler to retain the key patterns of the original video and maximizes the reconstruction performance of the upsampler. To make the downsamping results compatible with popular image and video storage formats, the downsampling results are encoded to uint8 with a differentiable quantization layer. To fully utilize the space-time correspondences, we propose two novel modules for explicit temporal propagation and space-time feature rearrangement. Experimental results show that our proposed method significantly boosts the space-time reconstruction quality by preserving spatial textures and motion patterns in both downsampling and upscaling. Moreover, our framework enables a variety of applications, including arbitrary video resampling, blurry frame reconstruction, and efficient video storage."
Learning Local Implicit Fourier Representation for Image Warping
Authors: Jaewon Lee; Kwang Pyo Choi; Kyong Hwan Jin
Venue: ECCV 2022 (main)

Abstract: "Image warping aims to reshape images defined on rectangular grids into arbitrary shapes. Recently, implicit neural functions have shown remarkable performances in representing images in a continuous manner. However, a standalone multi-layer perceptron suffers from learning high-frequency Fourier coefficients. In this paper, we propose a local texture estimator for image warping (LTEW) followed by an implicit neural representation to deform images into continuous shapes. Local textures estimated from a deep super-resolution (SR) backbone are multiplied by locally-varying Jacobian matrices of a coordinate transformation to predict Fourier responses of a warped image. Our LTEW-based neural function outperforms existing warping methods for asymmetric-scale SR and homography transform. Furthermore, our algorithm well generalizes arbitrary coordinate transformations, such as homography transform with a large magnification factor and equirectangular projection (ERP) perspective transform, which are not provided in training."
SepLUT: Separable Image-Adaptive Lookup Tables for Real-Time Image Enhancement
Authors: Canqian Yang; Meiguang Jin; Yi Xu; Rui Zhang; Ying Chen; Huaida Liu
Venue: ECCV 2022 (main)

Abstract: "Image-adaptive lookup tables (LUTs) have achieved great success in real-time image enhancement tasks due to their high efficiency for modeling color transforms. However, they embed the complete transform, including the color component-independent and the component-correlated parts, into only a single type of LUTs, either 1D or 3D, in a coupled manner. This scheme raises a dilemma of improving model expressiveness or efficiency due to two factors. On the one hand, the 1D LUTs provide high computational efficiency but lack the critical capability of color components interaction. On the other, the 3D LUTs present enhanced component-correlated transform capability but suffer from heavy memory footprint, high training difficulty, and limited cell utilization. Inspired by the conventional divide-and-conquer practice in the image signal processor, we present SepLUT (separable image-adaptive lookup table) to tackle the above limitations. Specifically, we separate a single color transform into a cascade of component-independent and component-correlated sub-transforms instantiated as 1D and 3D LUTs, respectively. In this way, the capabilities of two sub-transforms can facilitate each other, where the 3D LUT complements the ability to mix up color components, and the 1D LUT redistributes the input colors to increase the cell utilization of the 3D LUT and thus enable the use of a more lightweight 3D LUT. Experiments demonstrate that the proposed method presents enhanced performance on photo retouching benchmark datasets than the current state-of-the-art and achieves real-time processing on both GPUs and CPUs."
Blind Image Decomposition
Authors: Junlin Han; Weihao Li; Pengfei Fang; Chunyi Sun; Jie Hong; Mohammad Ali Armin; Lars Petersson; Hongdong Li
Venue: ECCV 2022 (main)

Abstract: "We propose and study a novel task named Blind Image Decomposition (BID), which requires separating a superimposed image into constituent underlying images in a blind setting, that is, both the source components involved in mixing as well as the mixing mechanism are unknown. For example, rain may consist of multiple components, such as rain streaks, raindrops, snow, and haze. Rainy images can be treated as an arbitrary combination of these components, some of them or all of them. How to decompose superimposed images, like rainy images, into distinct source components is a crucial step toward real-world vision systems. To facilitate research on this new task, we construct multiple benchmark datasets, including mixed image decomposition across multiple domains, real-scenario deraining, and joint shadow/reflection/watermark removal. Moreover, we propose a simple yet general Blind Image Decomposition Network (BIDeN) to serve as a strong baseline for future work. Experimental results demonstrate the tenability of our benchmarks and the effectiveness of BIDeN."
MuLUT: Cooperating Multiple Look-Up Tables for Efficient Image Super-Resolution
Authors: Jiacheng Li; Chang Chen; Zhen Cheng; Zhiwei Xiong
Venue: ECCV 2022 (main)

Abstract: "The high-resolution screen of edge devices stimulates a strong demand for efficient image super-resolution (SR). An emerging research, SR-LUT, responds to this demand by marrying the look-up table (LUT) with learning-based SR methods. However, the size of a single LUT grows exponentially with the increase of its indexing capacity. Consequently, the receptive field of a single LUT is restricted, resulting in inferior performance. To address this issue, we extend SR-LUT by enabling the cooperation of Multiple LUTs, termed MuLUT. Firstly, we devise two novel complementary indexing patterns and construct multiple LUTs in parallel. Secondly, we propose a re-indexing mechanism to enable the hierarchical indexing between multiple LUTs. In these two ways, the total size of MuLUT is linear to its indexing capacity, yielding a practical method to obtain superior performance. We examine the advantage of MuLUT on five SR benchmarks. MuLUT achieves a significant improvement over SR-LUT, up to 1.1dB PSNR, while preserving its efficiency. Moreover, we extend MuLUT to address demosaicing of Bayer-patterned images, surpassing SR-LUT on two benchmarks by a large margin."
Learning Spatiotemporal Frequency-Transformer for Compressed Video Super-Resolution
Authors: Zhongwei Qiu; Huan Yang; Jianlong Fu; Dongmei Fu
Venue: ECCV 2022 (main)

Abstract: "Compressed video super-resolution (VSR) aims to restore high-resolution frames from compressed low-resolution counterparts. Most recent VSR approaches often enhance an input frame by “borrowing’’ relevant textures from neighboring video frames. Although some progress has been made, there are grand challenges to effectively extract and transfer high-quality textures from compressed videos where most frames are usually highly degraded. In this paper, we propose a novel Frequency-Transformer for compressed video super-resolution (FTVSR) that conducts self-attention over a joint space-time-frequency domain. First, we divide a video frame into patches, and transform each patch into DCT spectral maps in which each channel represents a frequency band. Such a design enables a fine-grained level self-attention on each frequency band, so that real visual texture can be distinguished from artifacts, and further utilized for video frame restoration. Second, we study different self-attention schemes, and discover that a “divided attention” which conducts a joint space-frequency attention before applying temporal attention on each frequency band, leads to the best video enhancement quality. Experimental results on two widely-used video super-resolution benchmarks show that FTVSR outperforms state-of-the-art approaches on both uncompressed and compressed videos with clear visual margins. Code are available at https://github.com/researchmm/FTVSR."