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- EnlightenGAN: Deep Light Enhancement Without Paired Supervision2,636
This paper proposes a highly effective unsupervised generative adversarial network, dubbed EnlightenGAN, that can be trained without low/normal-light image pairs, yet proves to generalize very well on various real-world test images.
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- Joint Service Caching and Task Offloading for Mobile Edge Computing in Dense Networks588
An efficient online algorithm, called OREO, is proposed, which jointly optimizes dynamic service caching and task offloading to address a number of key challenges in MEC systems, including service heterogeneity, unknown system dynamics, spatial demand coupling and decentralized coordination.
- Jointly Attentive Spatial-Temporal Pooling Networks for Video-Based Person Re-identification346
This work presents a novel joint Spatial and Temporal Attention Pooling Network (ASTPN) for video-based person re-identification, which enables the feature extractor to be aware of the current input video sequences, in a way that interdependency from the matching items can directly influence the computation of each other's representation.
- Masked Diffusion Transformer is a Strong Image Synthesizer329
A Masked Diffusion Transformer (MDT) is proposed that introduces a mask latent modeling scheme to explicitly enhance the DPMs’ ability to contextual relation learning among object semantic parts in an image.
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- Client Selection for Federated Learning With Non-IID Data in Mobile Edge Computing230
This paper proposes an efficient FL algorithm, named CSFedAvg, in which the clients with lower degree of non-IID data will be chosen to train the models with higher frequency, and conducts simulations using publicly-available datasets to train deep neural networks.
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- CLU-CNNs: Object detection for medical images203
A domain adaptation framework called CLU-CNNs is proposed, which is designed for medical images and uses ANCF and BN-IN Net to improve domain adaptation capability without specific domain adaptation training.
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- Context-aware Biaffine Localizing Network for Temporal Sentence Grounding195
A novel localization framework that scores all pairs of start and end indices within the video simultaneously with a biaffine mechanism that significantly outperforms state-of-thearts on three public datasets, demonstrating the effectiveness of the proposed localization framework.
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- Jointly Cross- and Self-Modal Graph Attention Network for Query-Based Moment Localization152
A novel Cross- and Self-Modal Graph Attention Network (CSMGAN) is proposed that recasts this task as a process of iterative messages passing over a joint graph that is able to effectively capture high-order interactions between two modalities, thus enabling a further precise localization.
- GuardHealth: Blockchain empowered secure data management and Graph Convolutional Network enabled anomaly detection in smart healthcare142
This paper proposes GuardHealth: an efficient, secure and decentralized Blockchain system for data privacy preserving and sharing, which prevents data sharing without permission and is applicable for smart healthcare system.
- A Survey of Attacks on Large Vision–Language Models: Resources, Advances, and Future Trends130
This article provides a comprehensive review of the various forms of existing LVLM attacks, and systematically review the development of LVLM attack methods, such as adversarial attacks that manipulate model outputs, jailbreak attacks that exploit model vulnerabilities for unauthorized actions, and data poisoning that affects model training.
- Video Graph Transformer for Video Question Answering121
This paper proposes a Video Graph Transformer model for Video Quetion Answering (VideoQA), and shows that VGT can achieve much better performances on VideoQA tasks that challenge dynamic relation reasoning than prior arts in the pretraining-free scenario.
- Robust discrete code modeling for supervised hashing118
This work proposes a novel supervised hashing approach, termed as Robust Discrete Code Modeling (RDCM), which directly learns high-quality discretebinary codes and hash functions by effectively suppressing the influence of unreliable binary codes and potentially noisily-labeled samples.
- Spatio–Temporal Edge Service Placement: A Bandit Learning Approach116
This paper proposes SEEN, a Spatial-temporal Edge sErvice placemeNt algorithm, a novel combinatorial contextual bandit learning problem of an ASP under a limited budget, and proves that the algorithm achieves a sublinear regret bound when it is compared with an Oracle algorithm that knows the exact benefit information.
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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 2026-10-12. 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.
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