THOMAS: Trajectory Heatmap Output with learned Multi-Agent Sampling
Authors: Thomas Gilles, Stefano Sabatini, Dzmitry Tsishkou, Bogdan Stanciulescu, Fabien Moutarde
ICLR 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We report our results on the Interaction multi-agent prediction challenge and rank 1st on the online test leaderboard. (Abstract) 4 EXPERIMENTS (Section title) 4.3 COMPARISON WITH STATE-OF-THE-ART (Section title) 4.4 ABLATION STUDIES (Section title) |
| Researcher Affiliation | Collaboration | 1Io V team, Paris Research Center, Huawei Technologies France 2Center for robotics, MINES Paris Tech |
| Pseudocode | No | The paper includes a detailed architecture diagram (Figure 8) but no explicitly labeled 'Pseudocode' or 'Algorithm' block. |
| Open Source Code | No | The reproducibility statement mentions the public availability of the dataset but does not state that the code for the described methodology is open-source or provide a link to it. |
| Open Datasets | Yes | We use the publicly available Interaction 1.2 dataset (Zhan et al., 2019) available at http://challenge.interaction-dataset.com/dataset/download. |
| Dataset Splits | Yes | We use the training/validation split provided in Interaction 1.2. |
| Hardware Specification | No | The paper discusses training and inference times (e.g., 'Training 7.5 hours', 'Inference 20 ms') but does not specify the hardware used (e.g., GPU model, CPU type, or memory). |
| Software Dependencies | No | The paper mentions software components like 'Adam optimizer', 'ReLU activation', and 'Layer Normalization', but does not specify version numbers for any software libraries or frameworks (e.g., PyTorch, TensorFlow, Python version). |
| Experiment Setup | Yes | We train all models with Adam optimizer and batchsize 32. We initialize the learning rate at 1e 3 and divide it by 2 at epochs 3, 6, 9 and 13, before stopping the training at epoch 16. We use Re LU activation after every linear layer unless speciļ¬ed otherwise, and Layer Normalization after every attention and graph convolution layer. |