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Found 4 papers, 4 papers with code
The Victim and The Beneficiary: Exploiting a Poisoned Model to Train a Clean Model on Poisoned Data
This inspires us to propose a novel dual-network training framework: The Victim and The Beneficiary (V&B), which exploits a poisoned model to train a clean model without extra benign samples.
CuriousLLM: Elevating Multi-Document QA with Reasoning-Infused Knowledge Graph Prompting
In the field of Question Answering (QA), unifying large language models (LLMs) with external databases has shown great success.
Bounding and Filling: A Fast and Flexible Framework for Image Captioning
Several models adopted a non-autoregressive manner to speed up the process.
Beyond Generic: Enhancing Image Captioning with Real-World Knowledge using Vision-Language Pre-Training Model
Considering that Vision-Language Pre-Training (VLP) models master massive such knowledge from large-scale web-harvested data, it is promising to utilize the generalizability of VLP models to incorporate knowledge into image descriptions.
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