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Works154 from public data
- MobileFaceNets: Efficient CNNs for Accurate Real-Time Face Verification on Mobile Devices771
A class of extremely efficient CNN models, MobileFaceNets, which use less than 1 million parameters and are specifically tailored for high-accuracy real-time face verification on mobile and embedded devices, achieve significantly improved efficiency over previous state-of-the-art mobile CNNs.
- TimeTraveler: Reinforcement Learning for Temporal Knowledge Graph Forecasting242
This work proposes the first reinforcement learning method for forecasting that defines a relative time encoding function to capture the timespan information, and proposes a novel representation method for unseen entities to improve the inductive inference ability of the model.
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- Learning Neural Ordinary Equations for Forecasting Future Links on Temporal Knowledge Graphs184
A novel continuum model is proposed by extending the idea of neural ordinary differential equations to multi-relational graph convolutional networks and encodes both temporal and structural information into continuous-time dynamic embeddings, showing its superior performance on the future link forecasting task.
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- DyERNIE: Dynamic Evolution of Riemannian Manifold Embeddings for Temporal Knowledge Graph Completion104
Dy- ERNIE is proposed, a non-Euclidean embedding approach that learns evolving entity representations in a product of Riemannian manifolds, where the composed spaces are estimated from the sectional curvatures of underlying data.
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- Ultra-dense GAN for satellite imagery super-resolution89
An ultra-dense GAN (udGAN) is proposed for image SR, where the internal layout of the residual block is reformed into a two-dimensional matrix topology, which can provide additional diagonal connections so that the model can still accomplish enough pathways with fewer layers.
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- Unsupervised Image Super-Resolution with an Indirect Supervised Path57
A novel framework which is composed of two stages: unsupervised image translation between real LR and synthetic LR images; and supervised super-resolution from approximated real LR images to the paired HR images, which achieves very good performance on datasets of NTIRE 2017, NTIRE 2018 and NTIRE 2020.
- SCEdit: Efficient and Controllable Image Diffusion Generation via Skip Connection Editing47
An efficient generative tuning framework, dubbed SCEdit, which integrates and edits Skip Connection using a lightweight tuning module named SC-Tuner, and allows for straightforward extension to controllable image syn-thesis by injecting different conditions with Controllable SC-Tuner.
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- When Face Recognition Meets Occlusion: A New Benchmark44
A simulated occlusion face recognition dataset that combines original normal face images and occluded face images to form the final dataset, termed as Webface-OCC, which significantly outperforms the state-of-the-arts face recognition models trained on existing datasets.
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- Time-dependent Entity Embedding is not All You Need: A Re-evaluation of Temporal Knowledge Graph Completion Models under a Unified Framework38
This work provides the first unified open-source framework for temporal KG completion models with full composability, where temporal embeddings, score functions, loss functions, regularizers, and the explicit modeling of reciprocal relations can be combined arbitrarily.
- Video Similarity and Alignment Learning on Partial Video Copy Detection37
The Video Similarity and Alignment Learning (VSAL) approach, which jointly models spatial similarity, temporal similarity and partial alignment, achieves the state-of-the-art F1-score on VCDB core dataset and constructs a new benchmark of partial video copy detection and localization.
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- A Simple But Powerful Graph Encoder for Temporal Knowledge Graph Completion35
This paper proposes a simple but powerful graph encoder for TKGC, called TARGCN, which extensively explores every entity's temporal context for learning contextualized representations and finds that instead of adopting various kinds of complex modules, it is more beneficial to efficiently capture the temporal contexts of entities.
- Can Multimodal Large Language Models Truly Perform Multimodal In-Context Learning?33
A simple yet effective approach is proposed, termed Mixed Modality In-Context Example Selection (MMICES), which considers both visual and language modalities when selecting demos, which revealed that multimodal ICL is predominantly driven by the textual content whereas the visual information in the demos has little influence.
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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 2026-10-16. 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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