Qiuchen-Wang/NavQA_Revised

NavQA Revised

NavQA Revised is a re-annotated version of the NaVQA dataset released with NVIDIA ReMEmbR. The original annotations are distributed in remembr/data/navqa/data.csv.

This repository provides the revised annotations as JSONL files:

navqa.jsonl
navqa_over_sequence.jsonl

Each line is one question-answer example. navqa.jsonl is the primary revised annotation file. navqa_over_sequence.jsonl uses the same schema and examples, but uses the beginning of the full sequence as the query start point. In this variant, the query does not need to consider selecting a shorter time segment from within the sequence.

Dataset Details

Item	Value
Dataset type	Re-annotated navigation video question answering annotations
Language	English
Task categories	Question answering, video question answering, embodied navigation reasoning
License	Apache 2.0
Total files	2 JSONL files
Split	test only
Examples per file	210
Total rows across both configs	420
Source dataset	NaVQA from ReMEmbR
Source sequences	CODa sequence ids 0, 3, 4, 6, 16, 21, 22
Examples per sequence	30

Configurations

Config name	Data file	Split	Examples	Query start point
navqa	navqa.jsonl	test	210	Start of the selected QA time window
navqa_over_sequence	navqa_over_sequence.jsonl	test	210	Start of the full sequence

What Changed

Compared with the original NaVQA annotations, this version makes two main corrections.

1. Timezone correction

   The original timestamps are interpreted using the local timezone of the UT Austin campus where the data was collected (America/Chicago). We convert Unix timestamps to the correct local time before matching them with navigation events and before inserting them into the natural-language query.

2. 2 fps matching and direct query generation

   Instead of using a coarser caption interval, examples are matched at 2 fps. The final query text is generated directly from the corrected temporal and spatial context, including the start time, current time, and current robot pose when available.

Dataset Summary

Each file contains 210 examples:

Split by video length	Count
SHORT	70
MEDIUM	70
LONG	70

Answer types:

Type	Count
position	71
binary	67
text	33
time	30
duration	9

File Variants

• navqa.jsonl: The query start time is the start of the selected QA time window.
• navqa_over_sequence.jsonl: The query start time is the beginning of the corresponding sequence. This version treats the query as being asked over the whole sequence history, so there is no need to choose an additional temporal segment inside the sequence.

Data Format

Each JSON object has the following fields:

Field	Description
id	Unique example identifier.
seq_id	CODa sequence id associated with the example.
length_category	Video-length bucket: SHORT, MEDIUM, or LONG.
length	Duration of the query window in seconds.
start_time	Unix timestamp for the start of the query window.
end_time	Unix timestamp for the end/current time of the query window.
file_info	Source frame/window filenames used for QA and context matching.
question	Final natural-language query generated from the corrected context.
type	Expected answer type: position, binary, text, time, or duration.
answers	Structured ground-truth answer.
question_category	Original or revised semantic category when available.
answer_timestamp	Timestamp(s) of the evidence used to answer the question.
text_answer	Optional free-form answer text.
parsable_answer	Normalized answer pattern for evaluation.

Example:

{
  "id": "LONG_5a6cef0c-175d-4779-bfd4-f7b193fb9039",
  "seq_id": 0,
  "length_category": "LONG",
  "question": "You started moving at 2023-01-16 09:55:32. The current time is 2023-01-16 10:03:02 and you are located at [1.42, 2.28, -0.17].\nWhen did you leave the building?",
  "type": "time",
  "answers": {
    "text": ["4.07 minutes ago"],
    "time": 4.07
  },
  "answer_timestamp": "9:59:00",
  "parsable_answer": "[minutes] ago"
}

Loading the Data

import json

examples = []
with open("navqa.jsonl", "r", encoding="utf-8") as f:
    for line in f:
        examples.append(json.loads(line))

print(len(examples))
print(examples[0]["question"])

Intended Use

This dataset is intended for evaluating navigation-oriented video question answering systems and embodied agents that reason over long-horizon spatial and temporal memory. It is especially useful for models that need to answer:

• spatial questions that return a goal position,
• temporal questions that return a time or elapsed duration,
• descriptive questions that return text,
• binary questions about whether an event or object appears in the navigation history.

Relationship to ReMEmbR and CODa

ReMEmbR introduces NaVQA as an evaluation dataset for long-horizon spatio-temporal robot memory. The upstream evaluation instructions describe NaVQA as 210 examples built from selected CODa sequences, with 30 questions per sequence.

This repository only provides the revised QA annotations. It does not include the original CODa sensor data, images, captions, or ReMEmbR model outputs. To reconstruct the full evaluation pipeline, refer to the upstream ReMEmbR repository and its evaluation instructions.

License

This repository is released under the Apache 2.0 license. The data is derived from the NaVQA annotations.