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
- Alex Chichung KotSuggested from co-authorship
Is this you? Claim this profile to confirm or dismiss it.
Works74 from public data
- Low-Light Image Enhancement with Normalizing Flow650
This paper investigates to model this one-to-many relationship via a proposed normalizing flow model of an invertible network that takes the low-light images/features as the condition and learns to map the distribution of normally exposed images into a Gaussian distribution.
- 209
- CRRN: Multi-scale Guided Concurrent Reflection Removal Network137
This paper proposes the Concurrent Reflection Removal Network (CRRN), a network that integrates image appearance information and multi-scale gradient information with human perception inspired loss function, and is trained on a new dataset with 3250 reflection images taken under diverse real-world scenes.
- 135
- 100
- Learning Meta Pattern for Face Anti-Spoofing92
A learnable network to extract Meta Pattern (MP) and a two-stream network to hierarchically fuse the input RGB image and the extracted MP by using the proposed Hierarchical Fusion Module (HFM) are proposed.
- 82
- 79
- CopyRNeRF: Protecting the CopyRight of Neural Radiance Fields60
This paper proposes to protect the copyright of NeRF models by replacing the original color representation in NeRF with a watermarked color representation, and a distortion-resistant rendering scheme is designed to guarantee robust message extraction in 2D renderings of NeRF.
- 48
- Benchmarking Single-Image Reflection Removal Algorithms46
This paper presents a SIngle-image Reflection Removal Plus dataset, an up-to-date taxonomy for existing methods, a benchmark dataset, and the unified benchmarking evaluations for state-of-the-art (especially learning-based) methods.
- 43
- Heterogeneous Transfer Learning via Deep Matrix Completion with Adversarial Kernel Embedding42
This paper proposes a new HTL method based on a deep matrix completion framework, where kernel embedding of distributions is trained in an adversarial manner for learning heterogeneous features across domains.
- 41
- The Age of Synthetic Realities: Challenges and Opportunities40
The crucial need for the development of forensic techniques capable of identifying harmful synthetic creations and distinguishing them from reality is highlighted, especially important in scenarios involving the creation and dissemination of fake news, disinformation, and misinformation.
- Face Image Reflection Removal38
The face image reflection removal problem is solved by incorporating inpainting ideas into a guided reflection removal framework, which takes two images as the input and considers various face-specific priors.
- IDET: Iterative Difference-Enhanced Transformers for High-Quality Change Detection34
This paper proposes a novel module called the iterative difference-enhanced transformers (IDET), which consists of three transformers for extracting long-range information from the bi-temporal images, and one for enhancing the feature difference.
- 33
- 32
- Unsupervised Domain Adaptation in the Wild via Disentangling Representation Learning31
This paper proposes to learn a disentangled latent representation based on implicit autoencoders that is transferable across domains and demonstrates the effectiveness of this proposed method on digit recognition, object recognition and semantic segmentation.
- 29
- 29
- Open-Set Deepfake Detection: A Parameter-Efficient Adaptation Method With Forgery Style Mixture27
This work develops a parameter-efficient ViT-based detection model that includes lightweight forgery feature extraction modules and enables the model to extract global and local forgery clues simultaneously, representing an important step toward open-set Deepfake detection in the wild.
- 23
- The NeRF Signature: Codebook-Aided Watermarking for Neural Radiance Fields20
This paper proposes NeRF Signature, a novel watermarking method for NeRF that employs a Codebook-aided Signature Embedding that does not alter the model structure, thereby maintaining imperceptibility and enhancing robustness at the model level.
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.
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.