A Novel Unified Architecture for Low-Shot Counting by Detection and Segmentation
Authors: Jer Pelhan, Alan Lukezic, Vitjan Zavrtanik, Matej Kristan
NeurIPS 2024 | Conference PDF | Archive PDF | Plain Text | 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 jer.pelhan@fri.uni-lj.si |
| 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. |