# Csegg Eval

> Evaluates continual learning capabilities in scene graph generation by measuring how models retain prior object-relationship knowledge while learning new tasks, handle long-tailed data distributions, and generalize to unseen objects and relationships across incremental learning scenarios. Use when the user wants to benchmark on CSEGG, or asks about evaluating this task. Reports Avg. R@20.

- Skill: `qhjqhj00/csegg-eval` (Agent Skill)
- Install (CLI): `npx skillmds add qhjqhj00/csegg-eval`
- Raw SKILL.md: https://api.skillmd.com/api/skills/qhjqhj00/csegg-eval/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Productivity
- Author: qhjqhj00 (https://skillmd.com/u/qhjqhj00)
- Updated: 2026-09-08
- Page: https://skillmd.com/skills/qhjqhj00/csegg-eval

---


# csegg-eval

> Adaptive Visual Scene Understanding: Incremental Scene Graph Generation — Khandelwal et al. (2023) (arXiv:2310.01636, 2023)

## What this evaluates

Evaluates continual learning capabilities in scene graph generation by measuring how models retain prior object-relationship knowledge while learning new tasks, handle long-tailed data distributions, and generalize to unseen objects and relationships across incremental learning scenarios.

## Datasets

- **CSEGG** — total ?; splits: train (-1), test (-1); repo https://github.com/ZhangLab-DeepNeuroCogLab/CSEGG.git

## Metrics

- `Avg. R@20` **(primary)** — range: [0, 1]
  - Average recall@20 computed across all incremental tasks and object/relationship classes. Higher is better.
- `F@20` — range: [0, 1]
  - F1-score@20 for relationship prediction averaged across tasks. Higher is better.
- `FWT@20` — range: other
  - Forward Transfer@20 measuring performance gain on new tasks compared to a baseline trained from scratch.
- `BWT@20` — range: other
  - Backward Transfer@20 measuring catastrophic forgetting by comparing final performance on old tasks to their peak performance.
- `Gen Rbbox@20` — range: [0, 1]
  - Generalization recall@20 for detecting bounding boxes of unknown objects.
- `Gen R@20` — range: [0, 1]
  - Generalization recall@20 for classifying known relationships among unknown objects.

## Input / output format

**Input**: RGB images containing scenes with objects and relationships.

**Output**: Predicted scene graphs consisting of object bounding boxes, object class labels, and relationship triples (subject-predicate-object).

## Scoring recipe

```python
# Compute per-task metrics
for task in tasks:
    preds = model(images[task])
    golds = ground_truth[task]
    R20[task] = recall_at_k(preds.objects, golds.objects, k=20)
    F20[task] = f1_score(preds.rels, golds.rels, k=20)
# Aggregate continual learning metrics
avg_r20 = mean(R20)
f20 = mean(F20)
fwt20 = (R20[last] - R20[1]) / (num_tasks - 1)
bwt20 = mean(R20[last] - R20[initial])
# Generalization metrics (Scenario 3)
gen_rbbox20 = recall_at_k(preds.bboxes, unknown_gt.bboxes, k=20)
gen_r20 = recall_at_k(preds.rels, unknown_gt.rels, k=20)
```

## Common pitfalls

- Avg.R@20 averages recall across all incremental tasks/classes, not just the current task, so it reflects retention rather than single-task performance.
- Long-tailed distributions cause severe tail-class forgetting; standard replay without sampling techniques (LVIS/BLS) underperforms significantly.
- Higher forgetting in replay methods can paradoxically improve generalization to unknown objects due to fixed output box limits, contradicting standard CL intuition.

## Evidence (verbatim from paper)

> We present Avg. R@20, F@20, FWT@20, and BWT@20 results for learning scenario 1 (S1) in Fig. 5(a)(b) and Fig.S11(a)(b). Notably, all continual learning baselines start from a similar Avg.R@20 in Task 1 and their performance drops over subsequent tasks. This implies that catastrophic forgetting about learned relationships occurs when the CSEGG models learn new relationships.

## Citation

```bibtex
@misc{khandelwal2023csegg,
  title={Adaptive Visual Scene Understanding: Incremental Scene Graph Generation},
  author={Khandelwal et al. (2023)},
  year={2023},
  note={arXiv:2310.01636}
}
```

- arXiv: 2310.01636