Papers by Ser-Nam Lim
Cross-Modal Retrieval Augmentation for Multi-Modal Classification (2021.findings-emnlp)
Copied to clipboard
| Challenge: | Recent advances in using retrieval components over external knowledge sources have shown impressive results for a variety of downstream tasks in natural language processing. |
| Approach: | They propose a retrieval-augmented multi-modal transformer architecture for embedding images and captions in the same space. |
| Outcome: | The proposed approach improves visual question answering over strong baselines and hot-swapping indices. |
BOOKAGENT: Orchestrating Safety-Aware Visual Narratives via Multi-Agent Cognitive Calibration (2026.findings-acl)
Copied to clipboard
| Challenge: | Existing work on illustrated storybooks decomposes this task into separate stages, limiting multi-modal grounding . et al. proposes a safety-aware multi-agent collaboration framework for high-quality, safety-conscious visual narratives . |
| Approach: | They propose a safety-aware multi-agent collaboration framework for illustrated storybooks . the framework jointly plans, scripts, illustrates, and globally corrects inconsistencies . |
| Outcome: | a novel framework outperforms existing methods in safety and coherence, and improves visual consistency . the framework is available on github at https://github.com/bogao-code/BookAgent/main . |
When in Doubt: Improving Classification Performance with Alternating Normalization (2021.findings-emnlp)
Copied to clipboard
| Challenge: | a classifier that uses a nonparametric post-processing step for classification suffers when given examples that are close to its decision boundary. |
| Approach: | They propose a nonparametric post-processing step that re-adjusts predicted class probability distributions using high-confidence validation examples. |
| Outcome: | The proposed method improves classifier accuracy on difficult examples. |
Scaling Up Temporal Domain Generalization via Temporal Experts Averaging (2025.emnlp-main)
Copied to clipboard
Aoming Liu, Kevin Miller, Venkatesh Saligrama, Kate Saenko, Boqing Gong, Ser-Nam Lim, Bryan A. Plummer
| Challenge: | Temporal Domain Generalization (TDG) aims to generalize across temporal distribution shifts, e.g., lexical change over time. |
| Approach: | They propose a framework that updates the entire model using weight averaging to maximize generalization potential while minimizing computational costs. |
| Outcome: | The proposed framework outperforms previous methods by up to 69% while being up to 60x more efficient. |