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- VBench: Comprehensive Benchmark Suite for Video Generative Models2,062
VBench is a comprehensive benchmark suite that dissects “video generation quality” into specific, hierarchical, and disentangled dimensions, each with tailored prompts and evaluation methods, and investi-gate the gaps between video and image generation models.
- LaVie: High-Quality Video Generation with Cascaded Latent Diffusion Models426
The incorporation of simple temporal self-attentions, coupled with rotary positional encoding, adequately captures the temporal correlations inherent in video data and proves that the process of joint image-video fine-tuning plays a pivotal role in producing high-quality and creative outcomes.
- SinSR: Diffusion-Based Image Super-Resolution in a Single Step378
This work first derive a deterministic sampling process from the most recent state-of-the-art (SOTA) method for accelerating diffusion-based SR, which can be distilled into a student model that performs SR within only one inference step, resulting in a remarkable up to × 10 speedup for inference.
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- Attention-GAN for Object Transfiguration in Wild Images193
Experimental results demonstrate the necessity of investigating attention in object transfiguration, and that the proposed algorithm can learn accurate attention to improve quality of generated images.
- A feasibility study on an automated method to generate patient‐specific dose distributions for radiotherapy using deep learning191
The proposed system with radiation geometry added to the input is a promising method to generate patient‐specific dose distributions for radiotherapy and can be applied to obtain the dose distributions slice‐by‐slice for planning quality assurance and for guiding automated planning.
- DG-Font: Deformable Generative Networks for Unsupervised Font Generation165
A feature deformation skip connection (FDSC) is introduced which predicts pairs of displacement maps and employs the predicted maps to apply deformable convolution to the low-level feature maps from the content encoder.
- Fully automatic and robust segmentation of the clinical target volume for radiotherapy of breast cancer using big data and deep learning159
The proposed DD-ResNet method could segment the CTV accurately with acceptable time consumption and could improve the consistency of target delineation and streamline radiotherapy workflows.
- Diff-Font: Diffusion Model for Robust One-Shot Font Generation105
Diff-Font is the first work to utilize a diffusion model for font generation tasks, and outperforms prior font generation methods in both high-fidelity font style replication and the generation of intricate characters.
- Urban and agriculturally influenced water contribute differently to the spread of antibiotic resistance genes in a mega-city river network91
The widespread of water borne antibiotic resistance genes (ARGs) represents a growing threat to the health of millions of people and the amelioration of river water quality does not reduce ARGs, but may affect their distributional patterns in the river network in Shanghai.
- EpiDiff: Enhancing Multi-View Synthesis via Localized Epipolar-Constrained Diffusion90
The proposed EpiDiff is a localized interactive multiview diffusion model to insert a lightweight epipolar attention block into the frozen diffusion model, leveraging epipolar constraints to enable cross-view interaction among feature maps of neighboring views.
- Deep RNNs for video denoising68
A deep architecture based on Recurrent Neural Network (RNN) based on a patch-based end-to-end mapping between the clean and noisy video sequences that is able to compete with some existing video denoising methods.
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- Vchitect-2.0: Parallel Transformer for Scaling Up Video Diffusion Models61
This work presents Vchitect-2.0, a parallel transformer architecture designed to scale up video diffusion models for large-scale text-to-video generation, and proposes a Memory-efficient Training framework that incorporates hybrid parallelism and other memory reduction techniques, enabling efficient training of long video sequences on distributed systems.
- A deep learning model to predict dose–volume histograms of organs at risk in radiotherapy treatment plans34
The prediction accuracy of the CResDevNet model was evaluated using a planning database of nasopharyngeal cancer cases and shown to be more accurate than the UNet model, and prediction accuracy was also higher for larger-volume OARs.
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Publication data from OpenAlex; 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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