---
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:

```text
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

```text
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.