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- Proceedings of the 29th ACM International Conference on Multimedia922
A robust deep learning framework for real-world SR that randomly erases potential adversarial noises in the frequency domain of input images or features and is more insensitive to adversarial attacks and presents more stable SR results than existing models and defenses is proposed.
- Component Divide-and-Conquer for Real-World Image Super-Resolution419
This paper proposes a Component Divide-and-Conquer model and a Gradient-Weighted loss for SR, exploring the utility of guiding SR model with low-level image components, and proposes a feasible way to balance the difficulties of image components for SR.
- Min-Entropy Latent Model for Weakly Supervised Object Detection232
A min-entropy latent model (MELM) is proposed for weakly supervised object detection, unified with feature learning and optimized with a recurrent learning algorithm, which progressively transfers the weak supervision to object locations.
- Identity-Preserving Talking Face Generation with Landmark and Appearance Priors160
A novel Transformer-based landmark generator is devised to infer lip and jaw landmarks from the audio and a video rendering model is built to translate the generated landmarks into face images, which helps generate realistic and identity-preserving visual content.
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- 3D Human Pose Machines with Self-supervised Learning97
A simple yet effective self-supervised correction mechanism to learn all intrinsic structures of human poses from abundant images and develops a 3D human pose machine, which jointly integrates the 2D spatial relationship, temporal smoothness of predictions and 3D geometric knowledge.
- NTIRE 2024 Challenge on Image Super-Resolution (×4): Methods and Results83
This paper reviews the NTIRE 2024 challenge on image super-resolution (×4), highlighting the solutions proposed and the outcomes obtained, and offers a comprehensive overview of current trends in this field.
- Trash to Treasure: Harvesting OOD Data with Cross-Modal Matching for Open-Set Semi-Supervised Learning81
This work proposes a novel cross-modal matching strategy to detect OOD samples that substantially lifts the performance on open-set SSL and outperforms the state-of-the-art by a large margin.
- Out-of-Candidate Rectification for Weakly Supervised Semantic Segmentation73
A group ranking-based Out-of-Candidate Rectification (OCR) mechanism in a plug-and-play fashion that can achieve remarkable performance gains on both Pascal VOC and MS COCO datasets with negligible extra training overhead, which jus-tifies the effectiveness and generality of OCR.
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- Image Restoration Through Generalized Ornstein-Uhlenbeck Bridge58
The Generalized Ornstein-Uhlenbeck Bridge (GOUB) model is introduced, leveraging the natural mean-reverting property of the generalized OU process and further eliminating the variance of its steady-state distribution through the Doob's h-transform enabling the recovery of high-quality images from low-quality ones.
- IDF-CR: Iterative Diffusion Process for Divide-and-Conquer Cloud Removal in Remote-Sensing Images57
This work presents an iterative diffusion process for cloud removal (IDF-CR), which exhibits strong generative capabilities to achieve component divide-and-conquer (CDC) cloud removal and refine the stable diffusion by implementing ControlNet.
- SAM-COD+: SAM-Guided Unified Framework for Weakly-Supervised Camouflaged Object Detection52
This paper proposes a unified COD framework, termed SAM-COD, which is capable of supporting arbitrary weakly-supervised labels and employs a prompt adapter to handle scribbles as prompts based on SAM, and introduces response filter and semantic matcher modules to improve the quality of the masks obtained by SAM under COD prompts.
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- Pedestrian Detection with Deep Convolutional Neural Network44
This paper proposes to cascade simple Aggregated Channel Features (ACF) and rich Deep Convolutional Neural Network (DCNN) features for efficient and effective pedestrian detection in complex scenes.
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- AIM 2020 Challenge on Real Image Super-Resolution: Methods and Results43
This paper introduces the real image Super-Resolution challenge that was part of the Advances in Image Manipulation (AIM) workshop, held in conjunction with ECCV 2020, and gauges the state-of-the-art approaches for real image SR in terms of PSNR and SSIM.
- NTIRE 2022 Challenge on Super-Resolution and Quality Enhancement of Compressed Video: Dataset, Methods and Results41
This paper reviews the NTIRE 2022 Challenge on Super-Resolution and Quality Enhancement of Compressed Video and proposed the LDV 2.0 dataset, which includes theLDV dataset (240 videos) and 95 additional videos.
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- Towards Real-World Burst Image Super-Resolution: Benchmark and Method34
A large-scale real-world burst super-resolution dataset, i.e., RealBSR, is established to explore the faithful reconstruction of image details from multiple frames and a Federated Burst Affinity network (FBAnet) is introduced to investigate non-trivial pixel-wise displacements among images under real- world image degradation.
- Masked Images Are Counterfactual Samples for Robust Fine-Tuning27
A novel fine-tuning method is proposed, which uses masked images as counterfactual samples that help improve the robustness of the fine- Tuning model, surpassing previous methods on the OOD performance.
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- Dual Adversarial Adaptation for Cross-Device Real-World Image Super-Resolution25
This paper proposes an unsupervised domain adaptation mechanism for real-world SR, named Dual ADversarial Adaptation (DADA), which only requires LR images in the target domain with available real paired data from a source camera, and improves the model transferability from the source domain to the target.
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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