Is this you? Claim this profile to correct it, add a bio and choose the work people see first.
Claim this profileAcademic lineage
Possible advisorsa guess from early papers, not confirmed
- Suggested from co-authorship
Is this you? Claim this profile to confirm or dismiss it.
Works321 from public data
- 496
- PHASEN: A Phase-and-Harmonics-Aware Speech Enhancement Network412
This paper proposes a phase-and-harmonics-aware deep neural network (DNN), named PHASEN, which has the ability to handle detailed phase patterns and to utilize harmonic patterns, and outperforms previous methods by a large margin on four metrics.
- 339
- 278
- SPM-Tracker: Series-Parallel Matching for Real-Time Visual Object Tracking263
The greatest challenge facing visual object tracking is the simultaneous requirements on robustness and discrimination power, and a SiamFC-based tracker, named SPM-Tracker, is proposed, to address the two requirements in two separate matching stages.
- Deep Fourier-Based Exposure Correction Network with Spatial-Frequency Interaction247
A deep Fourier-based Exposure Correction Network (FECNet) consisting of an amplitude subnetwork and a phase sub-network to progressively reconstruct the representation of lightness and structure components is proposed.
- Dual-camera design for coded aperture snapshot spectral imaging209
A beam splitter is placed in front of the objective lens of CASSI, which allows the same scene to be simultaneously captured by a grayscale camera, which greatly eases the reconstruction problem and yields high-quality 3D spectral data.
- 204
- Tracking by Instance Detection: A Meta-Learning Approach193
This work proposes a principled three-step approach to build a high-performance tracker based on two modern detectors RetinaNet and FCOS, and finds that model-agnostic meta-learning (MAML) offers a strategy to initialize the detector that satisfies the authors' needs.
- Camera Lens Super-Resolution170
This paper investigates SR from the perspective of camera lenses, named as CameraSR, which aims to alleviate the intrinsic tradeoff between resolution (R) and field-of-view (V) in realistic imaging systems and quantitatively analyzes the performance of commonly-used synthetic degradation models.
- 160
- 159
- 155
- NTIRE 2018 Challenge on Spectral Reconstruction from RGB Images135
This paper reviews the first challenge on spectral image reconstruction from RGB images, i.e., the recovery of whole-scene hyperspectral (HS) information from a 3-channel RGB image.
- Progressive Retinex125
A novel progressive Retinex framework is presented, in which illumination and noise of low-light image are perceived in a mutually reinforced manner, leading to noise reduction low- light enhancement results.
- GET: Group Event Transformer for Event-Based Vision117
A novel Group-based vision Transformer backbone for Event-based vision, called Group Event Transformer (GET), which decouples temporal-polarity information from spatial information throughout the feature extraction process, and demonstrates that GET outperforms other state-of-the-art methods.
- 110
- Deep Boosting for Image Denoising110
A novel deep boosting framework (DBF) for denoising, which integrates several convolutional networks in a feed-forward fashion, and introduces the concept of dense connection that overcomes the vanishing of gradients during training.
- 107
- 105
- 105
- 103
- Neural Degradation Representation Learning for All-in-One Image Restoration98
This paper proposes to learn a neural degradation representation (NDR) that captures the underlying characteristics of various degradations that adaptively decomposes different types of degradations, similar to a neural dictionary that represents basic degradation components.
- Recurrent Dynamic Embedding for Video Object Segmentation91
This paper proposes a Recurrent Dynamic Embedding (RDE) to build a memory bank of constant size, explicitly generated and update by the proposed Spatio-temporal Aggregation Module (SAM), which exploits the cue of historical information.
- Towards Generalizable Tumor Synthesis88
It is ascertained that generative AI models, e.g., Diffusion Models, can create realistic tumors generalized to a range of organs even when trained on a limited number of tumor examples from only one organ, and it is shown that AI models trained on these synthetic tumors can be generalized to detect and segment real tumors from CT volumes.
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 2026-10-13. 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.
Report an error
Wrong papers, two people merged into one, or a profile that should not be here? Tell us and we will fix or hide it.