Ying Jin, Chun Yuan, Jiaqi Wang, Dachuan Shi, Chaofan Tao, Zhendong Yang
We lifted 2 functions out of this paper's own repositories and ran 0 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 |
|---|---|---|
| sdc17/crossget | — | 0 of 1 |
| sdc17/upop | — | 0 of 1 |
| Function | Status | Where it lives |
|---|---|---|
| TokenReduction | Not yet run | sdc17/crossget/LLaVA/llava/model/reducer.py code served (permissive licence) · get_code("c5ba355bd1c09ea4") |
| update_alpha_parameters | Not yet run | sdc17/upop/compress_retrieval.py code served (permissive licence) · get_code("f1ad8af135d8bc70") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Real-world data contains a vast amount of multimodal information, among which vision and language are the two most representative modalities. Moreover, increasingly heavier models, e.g., Transformers, have attracted the attention of researchers to model compression. However, how to compress multimodal models, especially visonlanguage Transformers, is still under-explored. This paper proposes the Unified and Progressive Pruning (UPop) as a universal vison-language Transformer compression framework, which incorporates 1) unifiedly searching multimodal subnets in a continuous optimization space from the original model, which enables automatic assignment of pruning ratios among compressible modalities and structures; 2) progressively searching and retraining the subnet, which maintains convergence between the search and retrain to attain higher compression ratios. Experiments on various tasks, datasets, and model architectures demonstrate the effectiveness and versatility of the proposed UPop framework. The code is available at https://github.com/sdc17/UPop.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2301.13741")
get_code_for_paper("2301.13741")
have("2301.13741")
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