---
license: mit
language:
- en
tags:
- referring-image-segmentation
- reranking
- failure-detection
- multi-scale
- vision-language
- pytorch
pipeline_tag: image-segmentation
base_model:
- OpenGVLab/BEiT-3
- microsoft/swin-large-patch4-window12-384-22k
library_name: pytorch
datasets:
- refcoco
- refcocog
arxiv: 2606.22546
---

# Venice-H1: Failure-Aware Query Re-Ranking for Referring Image Segmentation

[![Paper](https://img.shields.io/badge/arXiv-2606.22546-b31b1b.svg)](https://arxiv.org/abs/2606.22546)
[![GitHub](https://img.shields.io/badge/GitHub-odaxai%2FVenice--H1-181717?logo=github)](https://github.com/odaxai/Venice-H1)
[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)

**Nicolò Savioli, Ph.D.** — OdaxAI Research  
nicolo.savioli@odaxai.com · [odaxai.com](https://odaxai.com)

---

## Architecture Overview

![Architecture Overview](figures/architecture_overview.png)

*Venice-H1 pipeline. A frozen DeRIS backbone generates N=10 candidate masks. Multi-scale grid signatures encode spatial quality. The Failure Re-Ranker gates intervention: it only overrides Query-0 when confident the default choice is wrong.*

---

## The Failure-Case Bottleneck

| | |
|---|---|
| ![Error Budget](figures/error_budget.png) | ![IoU Scatter](figures/iou_scatter_analysis.png) |
| *7–18% of samples generate 40–68% of total error* | *Failure cases form a "triangle of opportunity"* |

---

## Multi-Scale Grid Signatures

![Grid Signatures](figures/grid_signature_vis.png)

*Compact 675-dim spatial descriptors pooled at 4×4, 8×8, 16×16 grids per candidate mask.*

![Grid Cells](figures/grid_cells_detail_v2.png)

*Multi-scale grid cells inspired by entorhinal cortex representations.*

---

## Model Description

Venice-H1 is a lightweight, backbone-decoupled re-ranking module for Referring Image Segmentation (RIS). It detects when the default query selection fails and selects a better alternative using:

- **Multi-Scale Grid Signatures**: 4×4, 8×8, 16×16 spatial pooling → 675-dim descriptors
- **Failure Gate**: binary classifier predicting whether Query 0 is suboptimal
- **Gain Predictor**: IoU-regression head estimating improvement per alternative query
- **Backbone-decoupled**: works on cached features, no retraining of DeRIS required

**Architecture**: 3-layer pre-norm Transformer encoder, 8 heads, hidden_dim=512  
**Parameters**: 11,296,258 (11.3M) — matches paper exactly

---

## Results

### On failure cases (where Venice-H1 intervenes)

![Per Split Improvement](figures/per_split_improvement.png)

*Positive Δ across all 8 evaluation splits.*

| Metric | Value |
|--------|-------|
| Parameters | **11,296,258** |
| **Δ_fail (mIoU on failures)** | **+1.824** |
| **AUC (failure detection)** | **0.778** |
| **Δ_full (overall mIoU)** | **+0.039** |
| Q0 mIoU | 86.469 |
| Selected mIoU | 86.509 |
| Oracle mIoU | 89.691 |
| Harmful-switch rate | < 0.6% |

### Failure Gate Analysis

| | |
|---|---|
| ![ROC Curves](figures/roc_curves.png) | ![Coverage Risk](figures/coverage_risk.png) |
| *ROC curves across splits. AUC 0.78–0.82* | *Coverage-risk trade-off at different τ* |

---

## Qualitative Results

![Qualitative Examples](figures/qualitative_examples.jpg)

*Re-ranking on RefCOCO val. Each row: input, ground truth, default query (red, fails), Venice-H1 corrected selection (blue). Venice-H1 recovers IoU > 84% in all cases.*

---

## Ablation Study

![Ablation Study](figures/ablation_study.png)

| Configuration | Δ_fail | Gate AUC |
|---|---|---|
| BASE only (no grid) | +1.01 | 0.812 |
| 4×4 only | +1.01 | 0.821 |
| 8×8 only | +0.87 | 0.790 |
| 16×16 only | +1.00 | 0.828 |
| **BASE + all grids (ours)** | **+1.22** | **0.807** |

---

## Medical Cross-Domain Transfer

![Medical Transfer](figures/medical_cross_domain.png)

*Zero-shot transfer to MS-CXR (+1.16 mIoU) and M3D-RefSeg-2D (+0.51 mIoU) without fine-tuning.*

---

## External Dependencies

| Component | Model | Paper |
|-----------|-------|-------|
| Backbone | DeRIS-L | Dai et al. (2025) |
| Visual Encoder | Swin-Large | Liu et al. (2021) |
| Language Encoder | BEiT-3 | Wang et al. (2023) |
| Mask Generator | Mask2Former | Cheng et al. (2022) |

Venice-H1 does **not** include these weights. You need a running DeRIS-L instance to extract features.

---

## Quick Start

```python
import torch
from huggingface_hub import hf_hub_download

ckpt_path = hf_hub_download(repo_id="OdaxAI/venice-h1", filename="venice_h1_deris_l.pt")
ckpt = torch.load(ckpt_path, map_location="cpu", weights_only=False)

print("Config:", ckpt["config"])
print("Metrics:", ckpt["metrics"])
print("Parameters:", sum(v.numel() for v in ckpt["model"].values() if hasattr(v, "numel")))
```

**Reproduce paper results** (no dataset needed):

```bash
git clone https://github.com/odaxai/Venice-H1.git
cd Venice-H1 && pip install -r requirements.txt && pip install -e .
python reproduce_results.py --verify_only
```

```
── Architecture Verification ───────────────────
  Parameters : 11,296,258  ✓ MATCH

── Paper Cross-Check (RefCOCO val) ─────────────
  ✓ delta_fail : 1.8244  (paper: 1.824)
  ✓ auc_fail   : 0.7776  (paper: 0.778)
  ✓ delta_full : 0.0392  (paper: 0.039)
```

---

## Files in Repository

| File | Description |
|------|-------------|
| `venice_h1_deris_l.pt` | **Trained checkpoint** — 11.3M params, DeRIS-L backbone |
| `venice_h1_deris_l_metrics.json` | Full evaluation metrics |
| `config.yaml` | Training hyperparameters |
| `venice_h1/` | Python package (model code) |
| `train.py` | Training script |
| `evaluate.py` | Evaluation script |
| `reproduce_results.py` | One-command paper reproduction |
| `scripts/extract_features.py` | Feature extraction from DeRIS-L |

---

## Training Details

| Parameter | Value |
|-----------|-------|
| Optimizer | AdamW |
| Learning rate | 5e-4 |
| Weight decay | 1e-4 |
| Batch size | 512 |
| Epochs | 20 |
| Scheduler | Cosine + 3 epoch warmup |
| Loss: L_gate | Focal BCE (γ=2.0) |
| Loss: L_gain | Smooth-L1 (λ=5.0) |
| Mixed precision | FP16 |
| Seed | 42 |

---

## Citation

```bibtex
@article{savioli2026veniceh1,
  title   = {Venice-H1: Failure-Aware Query Re-Ranking with Multi-Scale Grid Signatures
             for Referring Image Segmentation},
  author  = {Savioli, Nicol\`{o}},
  journal = {arXiv preprint arXiv:2606.22546},
  year    = {2026},
  note    = {OdaxAI Research},
}
```

---

## License

MIT License. © 2026 OdaxAI Research. All research conducted by Nicolò Savioli, Ph.D.