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Jiannan Guo

3 accepted papers

2025

TeamLoRA: Boosting Low-Rank Adaptation with Expert Collaboration and Competition

ACL 2025long

While Parameter-Efficient Fine-Tuning (PEFT) methods like Low-Rank Adaptation (LoRA) effectively address resource constraints during fine-tuning, their performance often falls short, especially in multidimensional task scenarios. To address this issue, one straightforward solution is to introduce ta…

2022

MAGIC: Multimodal relAtional Graph adversarIal inferenCe for Diverse and Unpaired Text-Based Image Captioning

AAAI 2022technical

Text-based image captioning (TextCap) requires simultaneous comprehension of visual content and reading the text of images to generate a natural language description. Although a task can teach machines to understand the complex human environment further given that text is omnipresent in our daily su…

2021

Semi-Supervised Active Learning for Semi-Supervised Models: Exploit Adversarial Examples With Graph-Based Virtual Labels

ICCV 2021poster

The performance of computer vision models significantly improves with more labeled data. However, the acquisition of labeled data is limited by the high cost. To mitigate the reliance on large labeled datasets, active learning (AL) and semi-supervised learning (SSL) are frequently adopted. Although…

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