Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Efficient Large Multi-modal Models via Visual Context Compression
Authors: Jieneng Chen, Luoxin Ye, Ju He, Zhaoyang Wang, Daniel Khashabi, Alan L. Yuille
NeurIPS 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments demonstrate that our approach enhances the performance of MLLMs in both image-language and video-language understanding, while also significantly cutting training costs and improving inference efficiency. |
| Researcher Affiliation | Academia | Jieneng Chen , Luoxin Ye , Ju He , Zhao-Yang Wang, Daniel Khashabi , Alan Yuille Johns Hopkins University |
| Pseudocode | No | The paper does not include any figure, block, or section labeled 'Pseudocode', 'Algorithm', or 'Algorithm X', nor does it present structured steps for a method or procedure formatted like code. |
| Open Source Code | Yes | Website https://beckschen.github.io/llavolta.html Code https://github.com/Beckschen/LLa Volta |
| Open Datasets | Yes | We adopt thirteen benchmarks specifically designed for MLLM evaluation, including GQA [20], MM-Vet [50], Science QA (SQA)[31], MME[13], Text VQA [39], POPE [24], MMBench [30], MMBench-CN [30], VQA-v2 [14], LLa VA-Bench-in-the-Wild (LLa VAW ) [28], Vis Wiz [15], SEED-Image [22] and MMMU [52]. |
| Dataset Splits | Yes | We follow LLa VA-1.5 [27] to perform data preparation and training schedule for pretraining and instruction tuning. We adopt thirteen benchmarks specifically designed for MLLM evaluation, including GQA [20], MM-Vet [50], Science QA (SQA)[31], MME[13], Text VQA [39], POPE [24], MMBench [30], MMBench-CN [30], VQA-v2 [14], LLa VA-Bench-in-the-Wild (LLa VAW ) [28], Vis Wiz [15], SEED-Image [22] and MMMU [52]. |
| Hardware Specification | Yes | We conduct all the experiments with the machine of 8 Nvidia RTX 6000 Ada. |
| Software Dependencies | No | The paper mentions 'Vicuna-v1.5-7B [10]' and 'LLa MA2 codebase [43]' and 'Deep Speed Ze RO-3', but does not provide specific version numbers for these or other general software dependencies (e.g., Python, PyTorch libraries) that would ensure reproducibility. |
| Experiment Setup | Yes | We adopt the Vicuna-v1.5-7B [10] as the language model, leveraging the LLa MA2 codebase [43]. We leverage the pre-trained CLIP Vi T-L/14 [12, 36] with an input resolution of 336 × 336, resulting in 576 visual tokens. We employ the LLa VA framework [27] to connect the frozen CLIP vision encoder and the Vicuna LLMs. Along with the projector, we train the entire LLM instead of parameterefficient finetuning. We follow LLa VA-1.5 [27] to perform data preparation and training schedule for pretraining and instruction tuning. |