Datasets:

kfkas
/

streamingvlm-task-aware-vqa-suite

license: cc-by-4.0
task_categories:
  - visual-question-answering
  - image-text-to-text
language:
  - en
tags:
  - streaming-vlm
  - task-aware-resolution
  - visual-token-sensitivity
  - pope
  - textvqa
  - docvqa
  - vqav2
  - gqa
  - mmbench
pretty_name: StreamingVLM Task-Aware VQA Suite

StreamingVLM Task-Aware VQA Suite

This repository collects the benchmark sources used for task-aware visual-token sensitivity experiments in StreamingVLM.

The goal is to cover four task groups:

Task group	Dataset keys	Purpose
Coarse object presence	`pope`, `repope`, `hpope`	Tests whether low token budgets preserve semantic object existence.
General scene understanding	`vqav2`, `gqa`	Tests ordinary VQA scene/action/relation understanding.
Fine-grained visual evidence	`gqa_attribute`, `mmbench`	Tests non-OCR attribute, relation, part, and perception sensitivity.
Textual fine-grained evidence	`textvqa`, `docvqa`	Tests OCR/document detail sensitivity.

Files

source_registry.jsonl: canonical source registry for every benchmark.
manifests/*.jsonl.gz: locally materialized full manifests that were already used in StreamingVLM experiments.

The local manifests include:

Manifest	Rows
`manifests/pope_coco_manifest_n9000.jsonl.gz`	9,000
`manifests/repope_train_manifest_n8185.jsonl.gz`	8,185
`manifests/hpope_train_manifest_n9904.jsonl.gz`	9,904
`manifests/textvqa_validation_manifest_n5000.jsonl.gz`	5,000
`manifests/docvqa_validation_manifest_n5349.jsonl.gz`	5,349

For VQAv2, GQA, and MMBench, this repository stores the full source references rather than rehosting the full image payloads. Those datasets are large, and the experiment runner should load them from the original Hugging Face source repositories listed in source_registry.jsonl.

500-Sample Experiment Protocol

This repository stores full dataset sources or full local manifests. Individual experiments should select 500 examples per dataset at runtime:

full source / full manifest
    -> deterministic 500-row selection
    -> resolution or token-compression sweep

Use the same selection seed across all visual-token budgets and model variants so that performance differences reflect token allocation rather than sample composition.

Recommended Dataset Mapping

Coarse/object:
  POPE, RePOPE, H-POPE

General VQA:
  VQAv2, GQA

Fine-grained object/attribute:
  GQA attribute subset, MMBench

Text/OCR:
  TextVQA, DocVQA

Notes

The manifests contain absolute local paths from the original experiment machine when images were materialized locally. Use the source_repo, source_split, image_id, and question/answer fields as the stable dataset identity. Image payloads should be resolved from the corresponding original dataset source when running on a different machine.