Reward Finetuning for Faster and More Accurate Unsupervised Object Discovery
Authors: Katie Luo, Zhenzhen Liu, Xiangyu Chen, Yurong You, Sagie Benaim, Cheng Perng Phoo, Mark Campbell, Wen Sun, Bharath Hariharan, Kilian Q. Weinberger
NeurIPS 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Empirically, we demonstrate that our approach is not only more accurate, but also orders of magnitudes faster to train compared to prior works on object discovery. |
| Researcher Affiliation | Academia | 1Cornell University, Ithaca, NY 2The Hebrew University of Jerusalem |
| Pseudocode | Yes | Algorithm 1 Reward-Incentivized Finetuning |
| Open Source Code | Yes | Code is available at https://github.com/katieluo88/DRIFT. |
| Open Datasets | Yes | We experimented with two different datasets: Lyft Level 5 Perception dataset [24] and Ithaca-365 dataset [12]. |
| Dataset Splits | No | The paper specifies train and test splits (e.g., "11,873 train scenes and 4,901 test scenes" for Lyft, and "57,107 scenes for training and 1,644 for testing" for Ithaca365), but does not explicitly quantify a separate validation dataset split. |
| Hardware Specification | Yes | We train DRIFT on four NVIDIA RTX A6000 GPUs, with batch size 10 per GPU. |
| Software Dependencies | No | The paper mentions using "Point RCNN [40]" and "Open PCDet [42]" but does not specify version numbers for these software components. |
| Experiment Setup | Yes | We train DRIFT with 120 epochs in Lyft and 30 epochs in Ithaca365 as the default setting... We use λshape = 1, λalign = 1, λdyn = 0.001 and λbg = 0.001. We use µscale = 0.8 and σscale = 0.2 for the alignment reward. |