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 [1].
ODAM: Gradient-based Instance-Specific Visual Explanations for Object Detection
Authors: Chenyang ZHAO, Antoni B. Chan
ICLR 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We present a detailed analysis of the visualized explanations of detectors and carry out extensive experiments to validate the effectiveness of the proposed ODAM. |
| Researcher Affiliation | Academia | Chenyang Zhao & Antoni B. Chan Department of Computer Science City University of Hong Kong EMAIL, EMAIL |
| Pseudocode | Yes | The pseudo-code for Odam-NMS is presented in Algorithm 1. |
| Open Source Code | No | No explicit statement about providing open-source code for the methodology was found, nor a direct link to a code repository. |
| Open Datasets | Yes | Two datasets are adopted for evaluation: MS COCO (Lin et al., 2014), a standard object detection dataset, and Crowd Human (Shao et al., 2018) |
| Dataset Splits | Yes | Besides the MS COCO val set, results of the Pointing game and ODI are also reported on Crowd Human validation sets. |
| Hardware Specification | Yes | Experiments are performed using Py Torch and an RTX 3090 GPU. |
| Software Dependencies | No | The paper mentions 'Py Torch' but does not specify its version number or any other software dependencies with their specific versions. |
| Experiment Setup | Yes | For Odam-Train on MSCOCO, the detector training pipeline is totally the same as the baseline (Tian et al., 2019; Ren et al., 2015), which uses SGD as the optimizer running for 12 epochs with batchsize 16, learning rate 0.2 for two-stage Faster R-CNN and learning rate 0.1 for FCOS. For training on Crowd Human, the aspect ratios of the anchors in Faster R-CNN are set to H : W = {1, 2, 3} : 1 since the dataset contains people, and training runs for 30 epochs. Other parameters are the same as in training on MSCOCO. |