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..
Instruction-Guided Visual Masking
Authors: Jinliang Zheng, Jianxiong Li, Sijie Cheng, Yinan Zheng, Jiaming Li, Jihao Liu, Yu Liu, Jingjing Liu, Xianyuan Zhan
NeurIPS 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results on generic multimodal tasks such as VQA and embodied robotic control demonstrate the versatility of IVM, which as a plug-and-play tool, significantly boosts the performance of diverse multimodal models, yielding new state-of-the-art results across challenging multimodal benchmarks. |
| Researcher Affiliation | Collaboration | 1 AIR, Tsinghua University, 2 Sensetime Research 3 MMLab, CUHK, 4 Shanghai AI Lab |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Code, model and data are available at https://github.com/2toinf/IVM. |
| Open Datasets | Yes | We collect 250K labeled VG data from multiple sources including VG caption [36], Flickr30K [63], VSR [3], Open Image [30], and Ref Co Co [64, 37]... We sample a 700K subset from LLa VA-Instruction-tuning [41] for VQA-type data, and a 50K subset from Open X [54] for robotics data. |
| Dataset Splits | Yes | We reported the accuracy (IOU-50%) on the validation split in Table 7. |
| Hardware Specification | Yes | We adopt 8 NVIDIA 80G A100 GPUs and take 4 days to train our IVM model... The training can be completed on 2 NVIDIA RTX4090 GPU in 17h. |
| Software Dependencies | No | The paper mentions software components like 'deepspeed [4] engine' and 'optimizer Adam W [43]', as well as models and architectures like 'ResNet50 [22]' and 'T5 [48]'. However, it does not specify version numbers for these software components or libraries, which is required for reproducibility. |
| Experiment Setup | Yes | The training scripts are based on deepspeed [4] engine and the training hyperparameters can be found in Table 4... Table 4 lists: training iteration 200K, optimizer Adam W [43], learning rate 1e-5, batch size 32, weight decay 0, optimizer momentum β1, β2=0.9, 0.95, data augmentation Random Crop Resize. Table 6 lists: Chunking size 4, Optimizer Adam W [43], Learning rate 1e-4, Lr schedule cosine annealing, Warm up steps 2000, Batch size 64, Gradient Steps 200K. |