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- Learning Salient Boundary Feature for Anchor-free Temporal Action Localization329
This paper proposes the first purely anchor-free temporal localization method, which is both efficient and effective, and beats all anchor-based and actionness-guided methods with a remarkable margin on THUMOS14, achieving state-of-the-art results, and comparable ones on ActivityNet v1.3.
- Fast Learning of Temporal Action Proposal via Dense Boundary Generator228
An efficient and unified framework to generate temporal action proposals named Dense Boundary Generator (DBG), which draws inspiration from boundary-sensitive methods and implements boundary classification and action completeness regression for densely distributed proposals.
- NTIRE 2022 Challenge on Efficient Super-Resolution: Methods and Results94
The NTIRE 2022 challenge was to super-resolve an input image with a magnification factor of ×4 based on pairs of low and corresponding high resolution images and the aim was to design a network for single image super-resolution that achieved improvement of efficiency measured according to several metrics.
- Sonic: Shifting Focus to Global Audio Perception in Portrait Animation87
A novel audio-driven paradigm is proposed, dubbed as Sonic, to shift focus on the exploration of global audio perception and outperform existing SOTA methodologies in terms of video quality, temporally consistency, lip synchronization precision, and motion diversity.
- Cycle-IR: Deep Cyclic Image Retargeting52
This paper proposes a novel deep cyclic image retargeting approach, called Cycle-IR, to firstly implement image Retargeting with a single deep model, without relying on any explicit user annotations, built on the reverse mapping from the retargeted images to the given images.
- Frame and Feature-Context Video Super-Resolution43
A fully end-to-end trainable frame and feature-context video super-resolution (FFCVSR) network that takes full advantage of the inter-frame information from multiple LR frames and the context information from previously predicted HR frames, producing temporally consistent highquality results while maintaining real-time speed.
- NTIRE 2021 Challenge on Video Super-Resolution36
This paper presents evaluation results from two competition tracks as well as the proposed solutions to the NTIRE 2021 Challenge on Video Super-Resolution, and develops conventional video SR methods focusing on the restoration quality.
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- RealTalk: Real-time and Realistic Audio-driven Face Generation with 3D Facial Prior-guided Identity Alignment Network16
A novel generalized audio-driven framework RealTalk, which consists of an audio-to-expression transformer and a high-fidelity expression-to-face renderer, and a lightweight facial identity alignment (FIA) module which includes a lip-shape control structure and a face texture reference structure is proposed.
- Joint Learning Content and Degradation Aware Feature for Blind Super-Resolution16
The proposed Content and Degradation aware SR Network dubbed CDSR outperforms the existing UDP models and achieves competitive performance on PSNR and SSIM even compared with the state-of-the-art SKP methods.
- Disentangle Identity, Cooperate Emotion: Correlation-Aware Emotional Talking Portrait Generation15
Qualitative results and user studies further confirm the DICE-Talk method's ability to generate identity-preserving portraits with rich, correlated emotional expressions that naturally adapt to unseen identities, outperforming state-of-the-art approaches in emotion accuracy while maintaining competitive lip-sync performance.
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- SVP: Style-Enhanced Vivid Portrait Talking Head Diffusion Model3
A novel framework named Style-Enhanced Vivid Portrait (SVP) is proposed which fully leverages style-related information in THG and generates diverse, vivid, and high-quality videos with flexible control over intrinsic styles, outperforming existing state-of-the-art methods.
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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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