Awesome
MT-EQA
Multi-Target Embodied Question Answering
Introduction
We present a generalization of EQA -- Multi-Target EQA (MT-EQA). Specifically, we study questions that have multiple targets in them, such as Is the dresser in the bedroom bigger than the oven in the kitchen?", where the agent has to navigate to multiple locations (
dresser in bedroom", oven in kitchen") and perform comparative reasoning (
dresser" bigger than ``oven") before it can answer a question. Such questions require the development of entirely new modules or components in the agent. To address this, we propose a modular architecture composed of a program generator, a controller, a navigator, and a VQA module. The program generator converts the given question into sequential executable sub-programs; the navigator guides the agent to multiple locations pertinent to the navigation-related sub-programs; and the controller learns to select relevant observations along its path. These observations are then fed to the VQA module to predict the answer.
Citation
@inproceedings{yu2019mteqa,
title={Multi-Target Embodied Question Answering},
author={Yu, Licheng and Chen, Xinlei and Gkioxari, Georgia and Bansal, Mohit and Berg, Tamara L and Batra, Dhruv},
booktitle={CVPR},
year={2019}
}
Data Generation
Go to eqa_data
folder and do the followings:
- How to generate question-answer pairs
- Run
./scripts/run_question_generation.sh
, this would save qas tocache/question-gen-outputs/questions_pruned_v2.json
. - Run
python question-gen/make_object_id_to_cat.py
to make mapping from hid.oid to fine_class.
- Generate graphs, connMaps, and shortest-paths
- Run
python shortest-path-gen/generate-conn-maps.py
to build graph and connMaps. This would:- save graphs to
cache/3d-graphs
. - save connMaps to
cache/target-obj-conn-maps
. - update qas to
cache/question-gen-outputs/questions_pruned_mt_with_conn.json
.
- save graphs to
- Run
python shortest-path-gen/get_best_view.py
saving best-view points and ious. Note the points information [x, 1.0, z, yaw] is in coordinate system (not grid system). - Run
python shortest-path-gen/add_program_mt.py
to add parsed programs to questions, update qas tocache/question-gen-outputs/questions_pruned_mt_with_conn_program.json
. - Run
python shortest-path-gen/generate-bestview-paths-mt.py
to compute shortest paths connecting start, best-view, end points. Note we intentionally make a faked end point for testing if agent learns to stop at right position (before the faked end point). - Run
python shortest-path-gen/filter_questions_mt.py
to filter out those questions without shortest paths or of low entropy, saving filtered questions toquestions_mt_paths_nearby_source_best_view_program.json
.
- For installing House3D
- Follow the instructions[https://github.com/facebookresearch/House3D].
Imitation Learning for Nav+Ctrl+cVQA
Go to nav_loc_vqa
folder and do the followings:
- Prepare House Data (conn-maps, graphs, shortest-paths, images, features, etc)
- Copy or Symlink
../eqa_data/cache/question-gen-outputs
,../eqa_data/cache/shortest-paths-mt
,../eqa_data/target-obj-bestview-pos
and../eqa_data/target-obj-conn-maps
to./data
- Run
./tools/generate_path_imgs.py
and./tools/generate_path_cube_imgs.py
to extract 1st-person images along paths. - Run
./tools/extract_feats.py
and./tools/extract_cube_feats.py
to extract 1st-person features along paths. - Run
./tools/compute_meta_info.py
to compute room meta info.
- Train and Eval IL
- Run
./tools/prepro_imitation_data.py
and./tools/prepro_reinforce_data.py
- Run
./tools/eval_gd_path.py
to get results on ground-truth paths.
- Evaluate RL-finetuned Model (after checking eqa_nav)
- Run
./tools/evaleqa_nr.py
Reinforcement Learning Finetuning for Navigators
Go to eqa_nav
folder and do the followings:
- Prepare House Data (conn-maps, graphs, shortest-paths, images, features, etc)
- Copy or Symlink
../eqa_data/cache/question-gen-outputs
,../eqa_data/cache/shortest-paths-mt
,../eqa_data/target-obj-bestview-pos
and../eqa_data/target-obj-conn-maps
to./data
- Copy or Symlink
../nav_loc_eqa/cache/path_feats
,../nav_loc_eqa/cache/path_images
and../nav_loc_eqa/cache/path_to_room_meta
to./cache
.
- Prepare Navigation Data
- Run
./tools/prepro_im.py
for imitation learning data. - Run
./tools/prepro_rl.py
for reinforcement finetuning data.
- Train IL-based room-navigator and object-navigator
- Run
./tools/train_im_object.py
and./tools/train_im_room.py
for imitation learning. - Run
./tools/eval_im_object.py
and./tools/eval_im_room.py
for imitation learning evaluation.
- Finetune using RL
- Run
./tools/train_rl_object.py
and./tools/train_rl_room.py
for reinforcement finetuning. - Run
./tools/eval_nav_object.py
and./tools/eval_nav_room.py
for navigation evaluation.
Contact
This project is maintained by Licheng Yu.
License
BSD