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- Neighbor2Neighbor: Self-Supervised Denoising from Single Noisy Images489
The proposed Neighbor2Neighbor framework is able to enjoy the progress of state-of-the-art supervised denoising networks in network architecture design and avoids heavy dependence on the assumption of the noise distribution.
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- Video Super-Resolution with Recurrent Structure-Detail Network269
A novel recurrent video super-resolution method which is both effective and efficient in exploiting previous frames to super-resolve the current frame by dividing the input into structure and detail components which are fed to a recurrent unit composed of several proposed two-stream structure-detail blocks.
- Video Super-Resolution With Temporal Group Attention199
This work proposes a novel method that can effectively incorporate temporal information in a hierarchical way and achieves favorable performance against state-of-the-art methods on several benchmark datasets.
- Efficient Residual Dense Block Search for Image Super-Resolution152
An efficient residual dense block search algorithm with multiple objectives to hunt for fast, lightweight and accurate networks for image super-resolution models achieves better performance than the state-of-the-art methods with limited number of parameters and FLOPs.
- Exemplar Guided Unsupervised Image-to-Image Translation.146
The Exemplar Guided & Semantically Consistent Image-to-image Translation (EGSC-IT) network is proposed which conditions the translation process on an exemplar image in the target domain and introduces the concept of feature masks that provide coarse semantic guidance without requiring the use of any semantic labels.
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- AdaInt: Learning Adaptive Intervals for 3D Lookup Tables on Real-time Image Enhancement121
The proposed AdaInt (Adaptive Intervals Learning) is a novel mechanism to achieve a more flexible sampling point allocation by adaptively learning the non-uniform sampling intervals in the 3D color space and is implemented as a compact and efficient plug-and-play module for a 3D LUT-based method.
- Motion Deblurring with Real Events120
An end-to-end learning framework for event-based motion deblurring in a self-supervised manner, where real-world events are exploited to alleviate the performance degradation caused by data inconsistency, and a piecewise linear motion model is proposed to take into account motion non-linearities.
- Co-Evolutionary Compression for Unpaired Image Translation93
A novel co-evolutionary approach for reducing their memory usage and FLOPs simultaneously and synergistically optimized for investigating the most important convolution filters iteratively is developed.
- CineMaster: A 3D-Aware and Controllable Framework for Cinematic Text-to-Video Generation81
To overcome the scarcity of in-the-wild datasets with 3D object motion and camera pose annotations, this work carefully establish an automated data annotation pipeline that extracts 3D bounding boxes and camera trajectories from large-scale video data.
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- Look Back and Forth: Video Super-Resolution with Explicit Temporal Difference Modeling62
This paper proposes to compute the temporal difference between frames and divide those pixels into two subsets according to the level of difference and demonstrates the effectiveness of the proposed method and its favorable performance against state-of-the-art methods.
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- Unsupervised Image Super-Resolution with an Indirect Supervised Path57
A novel framework which is composed of two stages: unsupervised image translation between real LR and synthetic LR images; and supervised super-resolution from approximated real LR images to the paired HR images, which achieves very good performance on datasets of NTIRE 2017, NTIRE 2018 and NTIRE 2020.
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- GM-NeRF: Learning Generalizable Model-Based Neural Radiance Fields from Multi-View Images35
This work introduces an effective generalizable framework Generalizable Model-based Neural Radiance Fields (GM-NeRF) to synthesize free-viewpoint images and proposes a geometry-guided attention mechanism to register the appearance code from multi-view 2D images to a geometry proxy which can alleviate the misalignment between inaccurate geometry prior and pixel space.
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- CharacterFactory: Sampling Consistent Characters With GANs for Diffusion Models25
This work proposes CharacterFactory, a framework that allows sampling new characters with consistent identities in the latent space of GANs for diffusion models, and considers the word embeddings of celeb names as ground truths for the identity-consistent generation task and trains a GAN model to learn the mapping from a latent space to the celeb embedding space.
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- Perception-Oriented Bidirectional Attention Network for Image Super-Resolution Quality Assessment17
The Perception-oriented Bidirectional Attention Network (PBAN) for image SR FR-IQA is proposed, which is composed of three modules: an image encoder module, a perception-oriented bidirectional attention (PBA) module, and a quality prediction module that outperforms state-of-the-art quality assessment methods.
- MoTrans: Customized Motion Transfer with Text-driven Video Diffusion Models15
This work introduces a multimodal large language model (MLLM)-based recaptioner to expand the initial prompt to focus more on appearance and an appearance injection module to adapt appearance prior from video frames to the motion modeling process.
- Customizing Text-to-Image Generation with Inverted Interaction14
This work considers a novel concept customization task, that is, capturing the interaction between subjects in exemplar images and transferring the learned concept of interaction to achieve customized text-to-image generation.
Publication data from OpenAlex; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-15. 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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