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Works411 from public data
- Image Super-Resolution Using Deep Convolutional Networks10,021
This work proposes a deep learning method for single image super-resolution (SR) that directly learns an end-to-end mapping between the low/high-resolution images, represented as a deep convolutional neural network (CNN) that takes the low-resolution image as the input and outputs the high-resolution one.
- Learning a Deep Convolutional Network for Image Super-Resolution5,839
This work proposes a deep learning method for single image super-resolution (SR) that directly learns an end-to-end mapping between the low/high-resolution images and shows that traditional sparse-coding-based SR methods can also be viewed as a deep convolutional network.
- ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks4,943
This work thoroughly study three key components of SRGAN – network architecture, adversarial loss and perceptual loss, and improves each of them to derive an Enhanced SRGAN (ESRGAN), which achieves consistently better visual quality with more realistic and natural textures than SRGAN.
- Learning to Prompt for Vision-Language Models4,550
Context Optimization (CoOp) is proposed, a simple approach specifically for adapting CLIP-like vision-language models for downstream image recognition that achieves superb domain generalization performance compared with the zero-shot model using hand-crafted prompts.
- Accelerating the Super-Resolution Convolutional Neural Network3,558
This paper aims at accelerating the current SRCNN, and proposes a compact hourglass-shape CNN structure for faster and better SR, and presents the parameter settings that can achieve real-time performance on a generic CPU while still maintaining good performance.
- Conditional Prompt Learning for Vision-Language Models2,499
Conditional Context Optimization (CoCoOp), which extends CoOp by further learning a lightweight neural network to generate for each image an input-conditional token (vector), and yields stronger domain generalization performance as well.
- Zero-Reference Deep Curve Estimation for Low-Light Image Enhancement2,451
A novel method, Zero-Reference Deep Curve Estimation (Zero-DCE), which formulates light enhancement as a task of image-specific curve estimation with a deep network and shows that it generalizes well to diverse lighting conditions.
- 1,955
- WIDER FACE: A Face Detection Benchmark1,837
There is a gap between current face detection performance and the real world requirements, and the WIDER FACE dataset, which is 10 times larger than existing datasets is introduced, which contains rich annotations, including occlusions, poses, event categories, and face bounding boxes.
- Domain Generalization: A Survey1,753
For the first time, a comprehensive literature review in DG is provided to summarize the developments over the past decade and conduct a thorough review into existing methods and theories.
- Hybrid Task Cascade for Instance Segmentation1,565
This work proposes a new framework, Hybrid Task Cascade (HTC), which differs in two important aspects: (1) instead of performing cascaded refinement on these two tasks separately, it interweaves them for a joint multi-stage processing; (2) it adopts a fully convolutional branch to provide spatial context, which can help distinguishing hard foreground from cluttered background.
- Facial Landmark Detection by Deep Multi-task Learning1,547
A novel tasks-constrained deep model is formulated, with task-wise early stopping to facilitate learning convergence and reduces model complexity drastically compared to the state-of-the-art method based on cascaded deep model.
- 1,456
- EDVR: Video Restoration With Enhanced Deformable Convolutional Networks1,341
This work proposes a novel Video Restoration framework with Enhanced Deformable convolutions, termed EDVR, and proposes a Temporal and Spatial Attention (TSA) fusion module, in which attention is applied both temporally and spatially, so as to emphasize important features for subsequent restoration.
- Recovering Realistic Texture in Image Super-Resolution by Deep Spatial Feature Transform1,224
It is shown that it is possible to recover textures faithful to semantic classes in a single network conditioned on semantic segmentation probability maps through a novel Spatial Feature Transform (SFT) layer that generates affine transformation parameters for spatial-wise feature modulation.
- PSANet: Point-wise Spatial Attention Network for Scene Parsing1,191
The point-wise spatial attention network (PSANet) is proposed to relax the local neighborhood constraint and achieves top performance on various competitive scene parsing datasets, including ADE20K, PASCAL VOC 2012 and Cityscapes, demonstrating its effectiveness and generality.
- 1,102
- CARAFE: Content-Aware ReAssembly of FEatures1,010
Comprehensive evaluations on standard benchmarks in object detection, instance/semantic segmentation and inpainting show CARAFE shows consistent and substantial gains across all the tasks, and has great potential to serve as a strong building block for future research.
- Compression Artifacts Reduction by a Deep Convolutional Network894
A compact and efficient network for seamless attenuation of different compression artifacts is formulated and it is demonstrated that a deeper model can be effectively trained with the features learned in a shallow network.
- A large-scale car dataset for fine-grained categorization and verification882
This paper presents an on-going effort in collecting a large-scale dataset, “CompCars”, that covers not only different car views, but also their different internal and external parts, and rich attributes, and demonstrates a few important applications exploiting the dataset.
- Extract Free Dense Labels from CLIP856
The finding suggests that MaskCLIP can serve as a new reliable source of supervision for dense prediction tasks to achieve annotation-free segmentation in pixel-level dense prediction, specifically in semantic segmentation.
- Learning Lightweight Lane Detection CNNs by Self Attention Distillation780
It is observed that attention maps extracted from a model trained to a reasonable level would encode rich contextual information that can be used as a form of ‘free’ supervision for further representation learning through performing top- down and layer-wise attention distillation within the net- work itself.
- 780
- 722
- Exploiting Diffusion Prior for Real-World Image Super-Resolution715
A novel approach to leverage prior knowledge encapsulated in pre-trained text-to-image diffusion models for blind super-resolution by employing the time-aware encoder can achieve promising restoration results without altering the pre-trained synthesis model, thereby preserving the generative prior and minimizing training cost.
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 2026-10-14. 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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