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Works137 from public data
- Scheduling Policies for Federated Learning in Wireless Networks671
An analytical model is developed to characterize the performance of federated learning in wireless networks and shows that running FL with PF outperforms RS and RR if the network is operating under a high signal-to-interference-plus-noise ratio (SINR) threshold, while RR is more preferable when the SINR threshold is low.
- An Unsupervised Sentence Embedding Method by Mutual Information Maximization197
Experimental results show that the proposed lightweight extension on top of BERT significantly outperforms other unsupervised sentence embedding baselines on common semantic textual similarity (STS) tasks and downstream supervised tasks, and achieves performance competitive with supervised methods on various tasks.
- Hulu-Med: A Transparent Generalist Model towards Holistic Medical Vision-Language Understanding113
Hulu-Med is introduced, a transparent, generalist medical Vision-Language Model (VLM) designed to unify language-only, 2D/3D vision-language, and video understanding within a single architecture, and outperforms proprietary systems such as GPT-4o on 16 benchmarks.
- Fast Model Debias with Machine Unlearning110
This work proposes a fast model debiasing framework (FMD) which offers an efficient approach to identify, evaluate and remove biases inherent in trained models and designs a machine unlearning-based strategy to efficiently and effectively remove the bias in a trained model with a small counterfactual dataset.
- SynCamMaster: Synchronizing Multi-Camera Video Generation from Diverse Viewpoints104
This work proposes a plug-and-play module that enhances a pre-trained text-to-video model for multi-camera video generation, ensuring consistent content across different viewpoints, and introduces a multi-view synchronization module to maintain appearance and geometry consistency across these viewpoints.
- Med-MoE: Mixture of Domain-Specific Experts for Lightweight Medical Vision-Language Models103
Comprehensive experiments on both open- and close-end medical question answering and image classification tasks across datasets such as VQA-RAD, SLAKE and Path-VQA demonstrate that the proposed Med-MoE model can achieve performance superior to or on par with state-of-the-art baselines, while only requiring approximately 30\%-50\% of activated model parameters.
- Deep learning-enabled 3D multimodal fusion of cone-beam CT and intraoral mesh scans for clinically applicable tooth-bone reconstruction85
Deep Dental Multimodal Fusion is presented, an automatic multimodal framework that reconstructs 3D tooth-bone structures using CBCT and IOS using a novel pixel representation learning architectures, prior knowledge-guided losses, and geometry-based 3D fusion techniques.
- Track without Appearance: Learn Box and Tracklet Embedding with Local and Global Motion Patterns for Vehicle Tracking75
A novel approach is proposed that tackles the association issue for long-term tracking with the exclusive fully-exploited motion information with the proposed reconstruct-to-embed strategy based on deep graph convolutional neural networks (GCN).
- TEaR: Improving LLM-based Machine Translation with Systematic Self-Refinement63
A systematic LLM-based self-refinement translation framework, named TEaR, which exhibits superior systematicity and interpretability, and conducts cross-model correction experiments to investigate the potential relationship between the translation and evaluation capabilities of general-purpose LLMs.
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- UniEdit: A Unified Tuning-Free Framework for Video Motion and Appearance Editing61
UniEdit is presented, a tuning-free framework that supports both video motion and appearance editing by harnessing the power of a pre-trained text-to-video generator within an inversion-then-generation framework and introduces auxiliary motion-reference and reconstruction branches to produce text-guided motion and source features respectively.
- Comprehensive assessment of mRNA isoform detection methods for long-read sequencing data60
IsoQuant is demonstrated as a highly effective tool for isoform detection with LRS, with Bambu and StringTie2 also exhibiting strong performance and these results offer valuable guidance for future research on alternative splicing analysis and the ongoing improvement of tools for isoform detection using LRS data.
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- A ChatGPT Aided Explainable Framework for Zero-Shot Medical Image Diagnosis52
This paper proposes a novel CLIP-based zero-shot medical image classification framework supplemented with ChatGPT for explainable diagnosis, mimicking the diagnostic process performed by human experts.
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- FairMT-Bench: Benchmarking Fairness for Multi-turn Dialogue in Conversational LLMs50
A comprehensive fairness benchmark for LLMs in multi-turn dialogue scenarios, based on a task taxonomy targeting LLM fairness capabilities across three stages: context understanding, user interaction, and instruction trade-offs, is proposed.
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- DIVOTrack: A Novel Dataset and Baseline Method for Cross-View Multi-Object Tracking in DIVerse Open Scenes49
The DIVOTrack is introduced: a novel baseline cross-view tracking method with a unified joint detection and cross-view tracking framework named CrossMOT, which learns object detection, single-view association, and cross-view matching with an all-in-one embedding model.
- Bootstrapped Unsupervised Sentence Representation Learning46
A new framework with a two-branch Siamese Network which maximizes the similarity between two augmented views of each sentence is proposed which significantly outperforms other state-of-the-art unsupervised methods on semantic textual similarity (STS) and classification tasks.
- Fed-GraB: Federated Long-tailed Learning with Self-Adjusting Gradient Balancer41
Using a Self-adjusting Gradient Balancer module that re-weights clients' gradients in a closed-loop manner, based on the feedback of global long-tailed distribution evaluated by a Direct Prior Analyzer module, clients can effectively alleviate the distribution drift caused by data heterogeneity during the model training process and obtain a global model with better performance on the minority classes while maintaining the performance of the majority classes.
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- PAD: Personalized Alignment of LLMs at Decoding-Time36
Personalized Alignment at Decoding-time (PAD) is presented, a novel framework designed to align LLM outputs with diverse personalized preferences during the inference phase, eliminating the need for additional training.
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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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