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Works29 from public data
- 368
- NTIRE 2020 Challenge on Real-World Image Super-Resolution: Methods and Results191
The NTIRE 2020 challenge addresses the real world setting, where paired true high and low-resolution images are unavailable, and the ultimate goal is to achieve the best perceptual quality, evaluated using a human study.
- 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.
- HunyuanPortrait: Implicit Condition Control for Enhanced Portrait Animation68
HuyuanPortrait is introduced, a diffusion-based condition control method that employs implicit representations for highly controllable and lifelike portrait animation and exhibits strong generalization performance, which can effectively disentangle appearance and motion under different image styles.
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- ColorFormer: Image Colorization via Color Memory Assisted Hybrid-Attention Transformer57
Experimental results show that the proposed automatic image colorization method via color memory assisted hybrid-attention transformer can generate more realistic and semantically matched color images compared with state-of-the-art methods.
- 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.
- AE TextSpotter: Learning Visual and Linguistic Representation for Ambiguous Text Spotting28
This work proposes a novel text spotter, named Ambiguity Eliminating Text Spotter (AE TextSpotter), which learns both visual and linguistic features to significantly reduce ambiguity in text detection, and is the first time to improve text detection by using a language model.
- UniM-OV3D: Uni-Modality Open-Vocabulary 3D Scene Understanding with Fine-Grained Feature Representation22
A unified multimodal 3D open-vocabulary scene understanding network, namely UniM-OV3D, aligning point clouds with image, language and depth and facilitating the learning of coarse-to-fine point-semantic representations from captions is proposed.
- Efficient Reinforcement Learning for StarCraft by Abstract Forward Models and Transfer Learning19
The discovery that an abstract forward model [thought-game (TG)] combined with transfer learning is an effective way to accelerate reinforcement learning (RL) is presented and it is shown that the TG method is not as restrictive as it was thought to be.
- 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.
- Med-CMR: A Fine-Grained Benchmark Integrating Visual Evidence and Clinical Logic for Medical Complex Multimodal Reasoning15
Med-CMR is presented, a fine-grained Medical Complex Multimodal Reasoning benchmark that provides a stress test for visual-reasoning integration and rare-case robustness in medical MLLMs, and a rigorous yardstick for future clinical systems.
- 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.
- CASR: a context-aware residual network for single-image super-resolution14
A lightweight context-aware deep residual network named as CASR network is proposed, which appropriately encodes channel and spatial attention information to construct context- aware feature map for single-image super-resolution.
- Frequency Consistent Adaptation for Real World Super Resolution13
A novel Frequency Consistent Adaptation (FCA) that ensures the frequency domain consistency when applying existing Super-Resolution methods to the real scene and improves the performance of the SR model under real-world setting achieving state-of-the-art results with high fidelity and plausible perception.
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- Align and Surpass Human Camouflaged Perception: Visual Refocus Reinforcement Fine-Tuning3
A visual refocus reinforcement framework via the policy optimization algorithm is proposed to encourage multi-modal models to think and refocus more before answering, and achieve excellent reasoning abilities to align and even surpass human camouflaged perception systems.
- 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.
- Efficient Reinforcement Learning with a Thought-Game for StarCraft3
The background knowledge to reinforcement learning in the form of a thought-game is incorporated and the trained agent can achieve a 100\% win-rate on the map \textit{Simple64} against the most difficult non-cheating built-in bot (level-7), and the training is 100 times faster than the previous ones under the same computational resource.
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- MIMAFace: Face Animation via Motion-Identity Modulated Appearance Feature Learning2
This work meticulously examines the essential appearance features in the facial animation tasks, and introduces a Motion-Identity Modulated Appearance Learning Module (MIA) that modulates CLIP features at both motion and identity levels, and designs an Inter-clip Affinity Learning Module (ICA) to model temporal relationships across clips.
- NuNext: Reframing Nucleus Detection as Next-Point Detection1
This work reformulate nucleus detection as next-point prediction, wherein a multimodal large language model is developed to directly output foreground nucleus centroids from the input image.
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 2026-10-12. 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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