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..
Targeted Multimodal Sentiment Classification based on Coarse-to-Fine Grained Image-Target Matching
Authors: Jianfei Yu, Jieming Wang, Rui Xia, Junjie Li
IJCAI 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on two benchmark TMSC datasets show that our model consistently outperforms the baselines, achieves state-of-the-art results, and presents interpretable visualizations. |
| Researcher Affiliation | Academia | Jianfei Yu , Jieming Wang , Rui Xia and Junjie Li School of Computer Science and Engineering, Nanjing University of Science and Technology, China EMAIL |
| Pseudocode | No | The paper does not contain any pseudocode or clearly labeled algorithm blocks. |
| Open Source Code | Yes | The source code is released at https://github.com/NUSTM/ITM. |
| Open Datasets | Yes | We construct an Image-Target Matching dataset for Image Target Relevance and Object-Target Alignment tasks. Source. Since both tasks require the annotation of targets, we construct our dataset based on a subset of one benchmark dataset for the TMSC task (i.e., TWITTER-17), which has annotated the targets by [Lu et al., 2018]. |
| Dataset Splits | Yes | Table 1: Statistic of Our Image-Target Matching Dataset. Split #Targets #Images #I-T Related #I-T Unrelated #Annotated Boxes Train 1176 600 459 717 459 Dev 588 297 254 334 254 Test 588 280 270 318 270 Total 2352 1177 983 1369 983 |
| Hardware Specification | No | The paper mentions using RoBERTa and Faster R-CNN with ResNet-101 backbone but does not specify the hardware (e.g., GPU model, CPU) used for experiments. |
| Software Dependencies | No | The paper mentions using RoBERTa, Faster R-CNN, ResNet-101, and AdamW optimizer, but it does not specify version numbers for any of these software components or other libraries. |
| Experiment Setup | Yes | Specifically, we set the batch size to 32, the training epoch to 10, and λ1 and λ2 to 1 and 0.5. The learning rates for the TMSC task and the two auxiliary tasks are set to 1e-5 and 1e-6 respectively. |