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- Towards Blind Bitstream-corrupted Video Recovery: A Visual Foundation Model-driven Framework6
This paper proposes the first blind bitstream-corrupted video recovery framework that integrates visual foundation models with recovery model, which is adapted to different types of corruption and bitstream-level prompts and introduces a novel Corruption-aware Feature Completion (CFC) module.
- Brain tumor diagnosis in MRI scans images using Residual/Shuffle Network optimized by augmented Falcon Finch optimization6
A modified metaheuristic algorithm named Augmented Falcon Finch Optimization (AFFO) is introduced to enhance the proposed network for brain tumor classification, using bio-inspired principles to effectively search for the best hyperparameter configurations, thereby enhancing the reliability and accuracy of the deep learning model.
- Interactive Machine Learning on Edge Devices With User-in-the-Loop Sample Recommendation6
This paper proposes a method for efficient model personalization on a small interactive object recognition camera device by combining sample recommendations with an IML workflow and shows that the feedback design achieves more efficient model training while improving system usability through a systematic evaluation and user study using a prototype device.
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- From Semantics, Scene to Instance-awareness: Distilling Foundation Model for Open-vocabulary Grounded Situation Recognition3
This paper proposes Multimodal Interactive Prompt Distillation (MIPD), a novel framework that distills enriched multimodal knowledge from the foundation model, enabling the student Ov-GSR model to recognize unseen situations and be better aware of rare situations.
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- From Semantics, Scene to Instance-awareness: Distilling Foundation Model for Grounded Open-vocabulary Situation Recognition1
This paper proposes Multimodal Interactive Prompt Distillation (MIPD), a novel framework that distills enriched multimodal knowledge from the foundation model, enabling the student Ov-GSR model to recognize unseen situations and be better aware of rare situations.
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- Research on Chinese named entity recognition based on multi-feature fusion using transformer1
A multi-feature fusion model based on Transformer is proposed that adds a new type of feature input: vectors obtained by re-weighting different words through adjusting lexical weights, which reduces the impact of incorrect lexical information, thereby enhancing model performance.
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Publication data from OpenAlex; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-11. 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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