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 |
|---|---|---|
| infaaa/evolver | canonical | 2 of 4 |
| Function | Status | Where it lives |
|---|---|---|
| build_transform | Ran | infaaa/evolver/src/utils.py code served (permissive licence) · get_code("f0a261e12d1e7736") |
| get_distmat | Ran | infaaa/evolver/src/rank.py code served (permissive licence) · get_code("245b9652c8b2dd89") |
| load_extract_model | Not yet run | infaaa/evolver/src/utils.py code served (permissive licence) · get_code("1b231fdd9787c5ce") |
| load_inference_model | Not yet run | infaaa/evolver/src/utils.py code served (permissive licence) · get_code("3cdad7c1b75569c6") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Recent advances show that two-stream approaches have achieved outstanding performance in hateful meme detection. However, hateful memes constantly evolve as new memes emerge by fusing progressive cultural ideas, making existing methods obsolete or ineffective. In this work, we explore the potential of Large Multimodal Models (LMMs) for hateful meme detection. To this end, we propose Evolver, which incorporates LMMs via Chain-of-Evolution (CoE) Prompting, by integrating the evolution attribute and in-context information of memes. Specifically, Evolver simulates the evolving and expressing process of memes and reasons through LMMs in a step-by-step manner. First, an evolutionary pair mining module retrieves the top-k most similar memes in the external curated meme set with the input meme. Second, an evolutionary information extractor is designed to summarize the semantic regularities between the paired memes for prompting. Finally, a contextual relevance amplifier enhances the in-context hatefulness information to boost the search for evolutionary processes. Extensive experiments on public FHM, MAMI, and HarM datasets show that CoE prompting can be incorporated into existing LMMs to improve their performance. More encouragingly, it can serve as an interpretive tool to promote the understanding of the evolution of social memes. [Homepage] (https://github.com/inFaaa/Evolver)
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2407.21004")
get_code_for_paper("2407.21004")
have("2407.21004")
Connect an agent — have() is free.