Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
InvSeg: Test-Time Prompt Inversion for Semantic Segmentation
Authors: Jiayi Lin, Jiabo Huang, Jian Hu, Shaogang Gong
AAAI 2025 | Venue PDF | LLM Run Details | Input Tokens: 15,252 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 3,686 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments show that Inv Seg achieves state-of-the-art performance on the PASCAL VOC, PASCAL Context and COCO Object datasets. ... We benchmark Inv Seg on three segmentation datasets PASCAL VOC 2012 (Everingham et al. 2010), PASCAL Context (Mottaghi et al. 2014) and COCO Object (Lin et al. 2014) for OVSS task. |
| Researcher Affiliation | Collaboration | 1Queen Mary University of London 2Sony AI |
| Pseudocode | No | The paper describes the methodology in narrative text and figures, but does not include any explicitly labeled 'Pseudocode' or 'Algorithm' blocks or figures. |
| Open Source Code | Yes | Project Page https://jylin8100.github.io/Inv Seg Project |
| Open Datasets | Yes | We benchmark Inv Seg on three segmentation datasets PASCAL VOC 2012 (Everingham et al. 2010), PASCAL Context (Mottaghi et al. 2014) and COCO Object (Lin et al. 2014), containing 20,59,80 foreground classes, respectively. |
| Dataset Splits | Yes | The experiments are performed on the validation sets, including 1449, 5105, and 5000 images. |
| Hardware Specification | Yes | We optimize each image for 15 steps on single H100 GPU, with the inference time of around 7.9 seconds per image |
| Software Dependencies | Yes | We validate our approach using Stable Diffusion v2.1 (Rombach et al. 2022) with frozen pre-trained parameters. ... To obtain category names, we follow Diff Segmenter (Wang et al. 2023) that use BLIP (Li et al. 2022) and CLIP (Radford et al. 2021) to generate the category names out of all the candidate categories in the dataset. |
| Experiment Setup | Yes | We employ the Adam optimizer with a learning rate of 0.01. The weights for LEtrp is α = 1. We optimize each image for 15 steps ... the time step for each iteration is sampled from a range [5, 300] where the model can learn a more robust prompt from different time steps. While the time step for inference is 50, falling in the range during adaption. |