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- A Point Set Generation Network for 3D Object Reconstruction from a Single Image2,785
This paper addresses the problem of 3D reconstruction from a single image, generating a straight-forward form of output unorthordox, and designs architecture, loss function and learning paradigm that are novel and effective, capable of predicting multiple plausible 3D point clouds from an input image.
- DFANet: Deep Feature Aggregation for Real-Time Semantic Segmentation716
This paper introduces an extremely efficient CNN architecture named DFANet for semantic segmentation under resource constraints that substantially reduces the number of parameters, but still obtains sufficient receptive field and enhances the model learning ability, which strikes a balance between the speed and segmentation performance.
- PVN3D: A Deep Point-Wise 3D Keypoints Voting Network for 6DoF Pose Estimation613
A deep Hough voting network is proposed to detect 3D keypoints of objects and then estimate the 6D pose parameters within a least-squares fitting manner, which is a natural extension of 2D-keypoint approaches that successfully work on RGB based 6DoF estimation.
- Practical Stereo Matching via Cascaded Recurrent Network with Adaptive Correlation429
A hierarchical network with recurrent refinement to update disparities in a coarse-to-fine manner, as well as a stacked cascaded architecture for inference and a new synthetic dataset with special attention to difficult cases for better generalizing to real-world scenes are introduced.
- Low-Light Image Enhancement with Wavelet-Based Diffusion Models385
This work presents a wavelet-based conditional diffusion model (WCDM) that leverages the generative power of diffusion models to produce results with satisfactory perceptual fidelity and takes advantage of the strengths of wavelet transformation to greatly accelerate inference and reduce computational resource usage without sacrificing information.
- FFB6D: A Full Flow Bidirectional Fusion Network for 6D Pose Estimation376
This work proposes FFB6D, a Full Flow Bidirectional fusion network designed for 6D pose estimation from a single RGBD image, which learns to combine appearance and geometry information for representation learning as well as output representation selection.
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- NBNet: Noise Basis Learning for Image Denoising with Subspace Projection238
NBNet is introduced, a novel framework for image denoising designed to train a network that can separate signal and noise by learning a set of reconstruction basis in the feature space, and SSA, a non-local attention module designed to explicitly learn the basis generation as well as subspace projection.
- Deep Constrained Least Squares for Blind Image Super-Resolution143
This paper reformulate the degradation model such that the deblurred kernel estimation can be transferred into the low resolution space and introduces a dynamic deep linear filter module that can adaptively generate deblurring kernel weights conditional on the input and yields more robust kernel estimation.
- FS6D: Few-Shot 6D Pose Estimation of Novel Objects140
This work studies a new open set problem; the few-shot 6D object poses estimation: estimating the 6D pose of an unknown object by a few support views without extra training and proposes a large-scale RGBD photorealistic dataset (ShapeNet6D) for network pre-training.
- Learning Face Hallucination in the Wild138
A new method of face hallucination is presented, which can consistently improve the resolution of face images even with large appearance variations, based on a novel network architecture called Bi-channel Convolutional Neural Network (Bi-channel CNN).
- Disentangled Image Matting128
This paper proposes AdaMatting, a new end-to-end matting framework that disentangles this problem into two sub-tasks: trimap adaptation and alpha estimation, which achieves the state-of-the-art performance on Adobe Composition-1k dataset both qualitatively and quantitatively.
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- ADNet: Attention-guided Deformable Convolutional Network for High Dynamic Range Imaging116
This paper presents an attention-guided deformable convolutional network for hand-held multi-frame high dynamic range (HDR) imaging, namely ADNet, and proposes to align the gamma-corrected images in the feature-level with a Pyramid, Cascading and Deformable alignment module.
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- Approaching human level facial landmark localization by deep learning110
The solution to the 300 Faces in the Wild Facial Landmark Localization Challenge is presented, and how to achieve very competitive localization performance with a simple deep learning based system is demonstrated.
- Deep Fusion Network for Image Completion106
A fusion block is introduced to generate a flexible alpha composition map for combining known and unknown regions and provides a smooth fusion between restored and existing content, and an attention map to make network focus more on the unknown pixels.
- UPFlow: Upsampling Pyramid for Unsupervised Optical Flow Learning96
This work designs a self-guided upsample module to tackle the interpolation blur problem caused by bilinear upsampling between pyramid levels, and proposes a pyramid distillation loss to add supervision for intermediate levels via distilling the finest flow as pseudo labels.
- Motion Basis Learning for Unsupervised Deep Homography Estimation with Subspace Projection79
A new framework for unsupervised deep homography estimation is introduced and results show that this approach outperforms the state-of-the-art on the homography benchmark datasets both qualitatively and quantitatively.
- Learning Raw Image Denoising With Bayer Pattern Unification and Bayer Preserving Augmentation78
A Bayer pattern unification (BayerUnify) method to unify different Bayer patterns is proposed to fully utilize a heterogeneous dataset to train a single denoising model instead of training one model for each pattern.
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- BSRT: Improving Burst Super-Resolution with Swin Transformer and Flow-Guided Deformable Alignment61
This work combines optical flows and deformable convolutions, hence the BSRT can handle misalignment and aggregate the potential texture information in multi-frames more efficiently and wins the championship in the NTIRE2022 Burst Super-Resolution Challenge.
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- NTIRE 2022 Challenge on High Dynamic Range Imaging: Methods and Results45
This paper reviews the challenge on constrained high dynamic range (HDR) imaging that was part of the New Trends in Image Restoration and Enhancement (NTIRE) workshop, held in conjunction with CVPR 2022, and the competition set-up, datasets, proposed methods and their results.
- GAFlow: Incorporating Gaussian Attention into Optical Flow44
This paper introduces a novel Gaussian-Constrained Layer (GCL) which can be easily plugged into existing Transformer blocks to highlight the local neighborhood that contains fine-grained structural information and provides a newGaussian-Guided Attention Module (GGAM) which not only inherits properties from Gaussian distribution to instinctively revolve around the neighbor fields of each point but also is empowered to put the emphasis on contextually related regions during matching.
- NTIRE 2021 Challenge on Burst Super-Resolution: Methods and Results43
The NTIRE2021 challenge on burst super-resolution was to generate a clean RGB image with 4 times higher resolution given a RAW noisy burst as input, and the top-performing methods set a new state-of-the-art for the burstsuper-resolution task.
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- Learning Compact Face Representation41
This paper presents a deep learning framework which optimizes the compactness and discriminative ability jointly and shows that traditional face analysis tasks (e.g. gender analysis) can be effectively solved by a Look-Up-Table approach given a large-scale face data set.
- Fast Camera Image Denoising on Mobile GPUs with Deep Learning, Mobile AI 2021 Challenge: Report39
The first Mobile AI challenge is introduced, where the target is to develop an end-to-end deep learning-based image denoising solution that can demonstrate high efficiency on smartphone GPUs.
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- MIPI 2023 Challenge on Nighttime Flare Removal: Methods and Results18
The developed solutions in this challenge achieved state-of-the-art performance on Nighttime Flare Removal and a detailed description of all models developed is provided in this paper.
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- MIPI 2023 Challenge on RGBW Remosaic: Methods and Results8
This paper summarizes and reviews the RGBW Joint Remosaic and Denoise track on MIPI 2023, and describes the top three models developed in this challenge.
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- MIPI 2023 Challenge on RGBW Fusion: Methods and Results3
This paper summarizes and reviews the RGBW Joint Fusion and Denoise track on MIPI 2023 and describes the models developed in this challenge, including PSNR, SSIM, LPIPS, and KLD.
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Publication data from OpenAlex, with missing venues and authors filled in from Crossref; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-13. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
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