Decompose to Generalize: Species-Generalized Animal Pose Estimation
Authors: Guangrui Li, Yifan Sun, Zongxin Yang, Yi Yang
ICLR 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results show that all these decomposition manners yield reasonable joint concepts and substantially improve cross-species generalization (and the attentionbased approach is the best). |
| Researcher Affiliation | Collaboration | Guangrui Li1,2 , Yifan Sun2, Zongxin Yang3, Yi Yang3 1Re LER, AAII, University of Technology Sydney. 2Baidu Inc. 3 CCAI, College of Computer Science and Technology, Zhejiang University. |
| Pseudocode | Yes | Algorithm A Py Torch-style pseudocode for pixel-to-concept attention |
| Open Source Code | No | The proposed method is reproducible. We have provided the Py Torch-style code for the proposed attention module, and the detailed training strategies in the main text and the appendix. However, no specific link to an open-source code repository is provided. |
| Open Datasets | Yes | We evaluate our method on two large-scale animal datasets. AP-10K (Yu et al., 2021) is a large-scale benchmark for mammal animal pose estimation... Animal Pose Dataset (Cao et al., 2019) collects and annotates 5 species... Animal Kingdom (Ng et al., 2022) is another dataset... |
| Dataset Splits | Yes | We perform domain generalization with the leave-one-out setting, i.e., selecting one species / family as the target domain and the rest as the source domains. |
| Hardware Specification | No | No specific hardware details (e.g., GPU models, CPU models, memory specifications) used for running the experiments are provided in the paper. Table 9 presents FLOPs and inference speed but does not specify the hardware used for these measurements. |
| Software Dependencies | No | The paper mentions 'Py Torch-style pseudocode' in Algorithm A, but it does not specify any software dependencies with version numbers (e.g., Python version, PyTorch version, specific library versions). |
| Experiment Setup | Yes | The batch size is set to 64, and the learning rate for the first stage and the second is set to 5 × 10−4 and 5 × 10−5, respectively. We optimize the model with Adam for 210 epochs, where the learned rate decrease (×10−1) at 170 and 200, respectively. The size of the input image is 256 × 256 and the heatmap is with size 64 × 64. The number of the concept-specific blocks is set to 2, and k is set to 3 for all transfer tasks. |