Guangyu Yang, Weizhe Lin, Jinghong Chen, Jingbiao Mei, Bill Byrne
We lifted 4 functions out of this paper's own repositories and ran 2 of them in a sandbox. "Ran" means the function executed on a synthesized input and returned a value. It is not a reproduction of the paper's results.
| Repository | Role | Ran |
|---|---|---|
| JingbiaoMei/RGCL | — | 2 of 4 |
| Function | Status | Where it lives |
|---|---|---|
| compute_l2 | Ran | JingbiaoMei/RGCL/src/model/loss.py code served (permissive licence) · get_code("b79d1b8058e01331") |
| sparse_retrieve_hard_negatives_pseudo_positive | Ran | JingbiaoMei/RGCL/src/model/loss.py code served (permissive licence) · get_code("a20178fd77f8b5a5") |
| compute_loss | Not yet run | JingbiaoMei/RGCL/src/model/loss.py code served (permissive licence) · get_code("e29c3311f99491d1") |
| dense_retrieve_hard_negatives_pseudo_positive | Not yet run | JingbiaoMei/RGCL/src/model/loss.py code served (permissive licence) · get_code("20d79017dbff7d0c") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Hateful memes have become a significant concern on the Internet, necessitating robust automated detection systems. While Large Multimodal Models (LMMs) have shown promise in hateful meme detection, they face notable challenges like sub-optimal performance and limited out-of-domain generalization capabilities. Recent studies further reveal the limitations of both supervised fine-tuning (SFT) and in-context learning when applied to LMMs in this setting. To address these issues, we propose a robust adaptation framework for hateful meme detection that enhances in-domain accuracy and cross-domain generalization while preserving the general vision-language capabilities of LMMs. Analysis reveals that our approach achieves improved robustness under adversarial attacks compared to SFT models. Experiments on six meme classification datasets show that our approach achieves state-of-theart performance, outperforming larger agentic systems. Moreover, our method generates higher-quality rationales for explaining hateful content compared to standard SFT, enhancing model interpretability. Code available at https://github.com/JingbiaoMei/RGCL This paper contains content for demonstration purposes that may be disturbing for some readers.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2502.13061")
get_code_for_paper("2502.13061")
have("2502.13061")
Connect an agent — have() is free.