Attention Shifting to Pursue Optimal Representation for Adapting Multi-granularity Tasks
Authors: Gairui Bai, Wei Xi, Yihan Zhao, Xinhui Liu, Jizhong Zhao
IJCAI 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments validate Seg AS effectiveness in multi-granularity recognition of three tasks. |
| Researcher Affiliation | Academia | School of Computer Science and Technology, Xi an Jiaotong University, Xi an, China |
| Pseudocode | No | The paper describes the proposed method in text and with a diagram, but does not include any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any explicit statement about releasing open-source code or a link to a code repository. |
| Open Datasets | Yes | We evaluated the performance of Seg AS in various experiments, including occlusion recognition, object detection, and fine-grained recognition. Specifically, we performed these experiments on Image Net-100 [Russakovsky et al., 2015], Pascal VOC [Everingham et al., 2010], Place 205 [Zhou et al., 2014], COCO [Lin et al., 2014] datasets, and CUB-200-2011 [Welinder et al., 2010]. |
| Dataset Splits | No | The paper mentions fine-tuning with training sets and evaluating on testing sets, and references dataset names, but it does not provide specific percentages or counts for training, validation, or test splits, nor does it explicitly state the use of a validation set in its primary experimental setup description. |
| Hardware Specification | Yes | To verify the efficiency of our proposed method, we conducted experiments on four NVIDIA-Ge Force-RTX 3090. |
| Software Dependencies | No | The paper mentions using the 'SGD optimizer' and 'Res Net50' backbone but does not specify version numbers for any software libraries, frameworks (e.g., PyTorch, TensorFlow), or programming languages used. |
| Experiment Setup | Yes | The model was trained using SGD [Robbins and Monro, 1951] optimizer with a weight decay of 1 10 4 and momentum of 0.9. The temperature parameter τ was always set to 0.2. The total epoch was set as 200. In Image Net-1k, the number of semantic levels was defined as L = 3 and (M1, M2, M3) = (30000, 10000, 1000), details are in appendix B. |