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- Self-supervised Learning of Adversarial Example: Towards Good Generalizations for Deepfake Detection346
This work addresses the generalizable deepfake detection from a simple principle: a generalizable representation should be sensitive to diverse types of forgeries and synthesize augmented forgeries with a pool of forgery configurations and strengthen the “sensitivity” to the forgeries by enforcing the model to predict the forgery configuration.
- 223
- Perceiving and Modeling Density for Image Dehazing156
A novel Separable Hybrid Attention (SHA) module is proposed to perceive haze density by capturing positional-sensitive features in the orthogonal directions to achieve this goal and a density encoding matrix is proposed to model the uneven distribution of the haze explicitly.
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- AIM 2020 Challenge on Efficient Super-Resolution: Methods and Results101
The AIM 2020 challenge on efficient single image super-resolution was to super-resolve an input image with a magnification factor x4 based on a set of prior examples of low and corresponding high resolution images with focus on the proposed solutions and results.
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- HotRing: A Hotspot-Aware In-Memory Key-Value Store.80
This paper proposes a novel hotspot-aware KVS, named HotRing1, that is optimized for massively concurrent accesses to a small portion of items, and comprehensively adopts lock-free structures in its design, so that massively concurrent requests can better leverage multi-core architectures.
- Context-sensitive ranking64
This work presents context-sensitive ranking, a ranking framework that integrates structured queries and relevance ranking, and leverages and extends the materialized view research in OLAP to deliver algorithms and data structures that evaluate context- sensitive ranking efficiently.
- Self-Calibrated Efficient Transformer for Lightweight Super-Resolution61
This work presents a lightweight Self-Calibrated Efficient Transformer (SCET) network, which employs an efficient transformer to help the network obtain similar features over long distances and thus recover sufficient texture details.
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- Enhanced Sparse Model for Blind Deblurring43
Comprehensive evaluations on public datasets and real-world images demonstrate the superiority of the proposed method against state-of-the-art methods in terms of both speed and accuracy.
- A reversible data hiding method for H.264 with Shamir’s (t, n)-threshold secret sharing43
The experimental results show that this new robust reversible data hiding algorithm can get more robustness, effectively avert intra-frame distortion drift and get good visual quality.
- Deep Richardson–Lucy Deconvolution for Low-Light Image Deblurring39
A data-driven approach to model the saturated pixels by a learned latent map is developed and performs favorably against state-of-the-art algorithms both quantitatively and qualitatively on synthetic and real-world images.
- Thunder crystal: a novel crowdsourcing-based content distribution platform36
This work implements a crowdsourcing-based content distribution system, Thunder Crystal, which utilizes agents' upload bandwidth to amplify the content distribution capacity, and observes that agents are well motivated to keep online most of the time and amplify thecontent distribution capacity by 10~20 times.
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- Cross-View Hierarchy Network for Stereo Image Super-Resolution31
A cross-hierarchy information mining block (CHIMB) is designed that leverages channel attention and large kernel convolution attention to extract both global and local features from the intra-view, enabling the efficient restoration of accurate texture details.
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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 2026-10-14. 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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