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- MSR-GCN: Multi-Scale Residual Graph Convolution Networks for Human Motion Prediction279
A novel Multi-Scale Residual Graph Convolution Network (MSR-GCN) for human pose prediction task in the manner of end-to-end is proposed and experimental results demonstrate that the method outperforms the state-of-the-art approaches.
- From Synthetic to Real: Image Dehazing Collaborating with Unlabeled Real Data231
This work develops a disentangled image dehazing network (DID-Net), which disentangles the feature representations into three component maps, respecting the physical model of a haze process, and encourages the coarse predictions and refinements of each disentangling component to be consistent between the student and teacher networks by using a consistency loss on unlabeled real data.
- Dual Illumination Estimation for Robust Exposure Correction176
This paper presents a novel automatic exposure correction method, able to robustly produce high‐quality results for images of various exposure conditions, and demonstrates the effectiveness of the proposed approach and its superiority over the state‐of‐the‐art methods and popularautomatic exposure correction tools.
- Progressively Generating Better Initial Guesses Towards Next Stages for High-Quality Human Motion Prediction167
This paper presents a high-quality human motion pre-diction method that accurately predicts future human poses given observed ones and proposes a novel two-stage prediction frame-work, including an init-prediction network that just computes the good guess and then a formal-predictions network that predicts the target future poses based on the guess.
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- Analysis of positive and negative emotions in natural scene using brain activity and GIST88
A novel emotion understanding system based on brain activity and ''GIST'' to categorize emotions reflected by natural scenes and achieve interaction with a human in terms of emotional sharing through the EEG signals is proposed.
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- Rule-based extraction of experimental evidence in the biomedical domain72
The winning system is a Rule-based Information Extraction (IE) system that combines pattern matching, Natural Language Processing (NLP) tools, semantic constraints based on the domain and the specific task, and a post-processing stage for making the final curation decisionbased on the various evidence found within the document.
- Diverse Human Motion Prediction via Gumbel-Softmax Sampling from an Auxiliary Space68
This paper proposes a novel sampling strategy for sampling very diverse results from an imbalanced multimodal distribution learned by a deep generative model, which incorporates a Gumbel-Softmax coefficient matrix sampling method and an aggressive diversity promoting hinge loss function.
- Specular Highlight Removal for Real‐world Images60
This paper designs an optimization framework for simultaneously estimating the diffuse and specular highlight images from a single image and recovers the diffuse components of those regions with specular highlights by encouraging the encoding coefficients sparseness using L0 norm.
- NTIRE 2021 Challenge on Perceptual Image Quality Assessment55
The NTIRE 2021 challenge on perceptual image quality assessment (IQA), held in conjunction with the New Trends in Image Restoration and Enhancement workshop (NTIRE) workshop at CVPR 2021, achieved much better results than existing IQA methods, while the winning method can demonstrate state-of-the-art performance.
- Monte Carlo denoising via auxiliary feature guided self-attention49
A self-attention based MC denoising deep learning network based on the fact that self-Attention is essentially non-local means filtering in the embedding space which makes it inherently very suitable for theDenoising task.
- Learning to Detect Specular Highlights from Real-world Images44
This paper presents a large-scale real-world highlight dataset containing a rich variety of material categories, with diverse highlight shapes and appearances, and develops a deep learning-based specular highlight detection network (SHDNet) leveraging multi-scale context contrasted features to accurately detect specular highlights of varying scales.
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- DBDNet: Learning Bi-directional Dynamics for Early Action Prediction37
A novel encoder-decoder architecture is proposed for integrating the tasks of synthesizing future motions from observed videos and reconstructing observed motions from synthesized future motions in an unified framework, which can capture the bi-directional dynamics depicted in partial videos along the temporal (past-to-future) direction and reverse chronological direction.
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- Deep Camouflage Images31
A novel neural style transfer approach that adopts the visual perception mechanism to create camouflage images, which allows us to hide objects more effectively while producing natural-looking results.
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- A domain-general perspective on the role of the basal ganglia in language and music: Benefits of music therapy for the treatment of aphasia28
The results show that rhythm processing plays a key role in Melodic Intonation Therapy in the rehabilitation of non-fluent aphasia patients with lesions on the basal ganglia, and provides support for taking advantage of rhythm as an important point in music therapy in clinical studies.
- Pyramid Texture Filtering26
This work shows that the approach is effective to separate structure from texture of different scales, local contrasts, and forms, without degrading structures or introducing visual artifacts, and demonstrates the applicability of the method on various applications including detail enhancement, image abstraction, HDR tone mapping, inverse halftoning, and LDR image enhancement.
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 2026-10-15. 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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