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..
A Novel Unified Architecture for Low-Shot Counting by Detection and Segmentation
Authors: Jer Pelhan, Alan Lukezic, Vitjan Zavrtanik, Matej Kristan
NeurIPS 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 4 Experiments. Evaluation metrics and datasets. Standard datasets are used. The FSCD147 [19] is a detection-oriented extension of the FSC147 [24], which contains 6135 images of 147 object classes, split into 3659 training, 1286 validation, and 1190 test images. 4.1 Experimental Results. Table 1: Few-shot density-based methods (top part) and detection-based methods (bottom part) performances on the FSCD147 [19]. |
| Researcher Affiliation | Academia | Jer Pelhan, Alan Lukežiˇc, Vitjan Zavrtanik, Matej Kristan Faculty of Computer and Information Science, University of Ljubljana EMAIL |
| Pseudocode | No | The paper describes the method using detailed text and block diagrams but does not include any explicit pseudocode or algorithm blocks. |
| Open Source Code | Yes | The code is available on Git Hub. |
| Open Datasets | Yes | Standard datasets are used. The FSCD147 [19] is a detection-oriented extension of the FSC147 [24], which contains 6135 images of 147 object classes, split into 3659 training, 1286 validation, and 1190 test images. The second dataset is FSCD-LVIS [19], derived from LVIS [8] and contains 377 categories. |
| Dataset Splits | Yes | The FSCD147 [19] is a detection-oriented extension of the FSC147 [24], which contains 6135 images of 147 object classes, split into 3659 training, 1286 validation, and 1190 test images. |
| Hardware Specification | Yes | The training is done on 2 A100s GPUs with standard scale augmentation [20; 4] and zero-padding images to 1024 1024 resolution. |
| Software Dependencies | No | The paper mentions software components like the SAM backbone and Adam W optimizer but does not provide specific version numbers for these or other software dependencies. |
| Experiment Setup | Yes | Ge Co is pretrained with the classical loss [20] for initialization and is then trained for 200 epochs with the proposed dense detection loss (3) using a mini-batch size of 8, Adam W [16] optimizer, with initial learning rate set to 10 4, and weight decay of 10 4. |