Language Model as Visual Explainer
Authors: Xingyi Yang, Xinchao Wang
NeurIPS 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | To access the effectiveness of our approach, we introduce new benchmarks and conduct rigorous evaluations, demonstrating its plausibility, faithfulness, and stability. (Abstract) and 4 Experiment This section offers an in-depth exploration of our evaluation process for the proposed LVX framework... |
| Researcher Affiliation | Academia | Xingyi Yang Xinchao Wang National University of Singapore xyang@u.nus.edu, xinchao@nus.edu.sg |
| Pseudocode | Yes | C Pseudocode for LVX In this section, we present the pseudocode for the LVX framework, encompassing both the construction stage and the test stage. The algorithmic pipelines are outlined in Algorithm 1 and Algorithm 2. |
| Open Source Code | Yes | Question: Does the paper provide open access to the data and code, with sufficient instructions to faithfully reproduce the main experimental results, as described in supplemental material? Answer: [Yes] Justification: Code has been uploaded as supplementary material. Data will be available. (Paper Checklist Q5) |
| Open Datasets | Yes | To address this, we developed annotations for three recognized benchmarks: CIFAR10, CIFAR100 [39], and Image Net [57], termed as H-CIFAR10, H-CIFAR100, and H-Image Net. (Section 4.1) |
| Dataset Splits | Yes | The model is trained on a labeled training set Dtr = {xj, yj}M j=1, and would be evaluated a test set Dts = {xj}L j=1. (Section 2) and We report the average score across all validation samples. (Section 4.1) and To address this, we developed annotations for three recognized benchmarks: CIFAR10, CIFAR100 [39], and Image Net [57]... |
| Hardware Specification | No | The paper does not specify the exact hardware (e.g., specific GPU or CPU models, memory, or cloud instance types) used for running the experiments. |
| Software Dependencies | No | The paper mentions software packages like PyTorch, torchvision, and timm but does not provide specific version numbers for these dependencies. |
| Experiment Setup | Yes | The model is optimized with SGD for 50 epochs on the training sample, with an initial learning rate in {0.001, 0.01, 0.03} and a momentum term of 0.9. The weighting factor is set to 0.1. (Section 4.1) and In our experiment, we performed five rounds of tree refinement. (Section 3.1) |