(CVPR 2026) ConeSep: Cone-based Robust Noise-Unlearning Compositional Network for CIR (Model Weights)
1School of Software, Shandong University
2School of Computer Science and Technology, Harbin Institute of Technology (Shenzhen),
✉ Corresponding author
This repository hosts the official pre-trained checkpoints for **ConeSep**, a robust noise-unlearning framework that leverages geometric boundary estimation and optimal transport to solve the Noisy Triplet Correspondence (NTC) problem in Composed Image Retrieval (CIR).
---
## 📌 Model Information
### 1. Model Name
**ConeSep** (Cone-based robust noisE-unlearning comPositional network) Checkpoints.
### 2. Task Type & Applicable Tasks
- **Task Type:** Composed Image Retrieval (CIR).
- **Applicable Tasks:** Retrieving target images based on a reference image and a modification text. These weights provide unmatched robustness under varying degrees of noisy training data (Noise Triplet Correspondence).
### 3. Project Introduction
Existing Composed Image Retrieval methods struggle with the "Noisy Triplet Correspondence (NTC)" problem, leading to Modality Suppression, Negative Anchor Deficiency, and Unlearning Backlash. **ConeSep** actively perceives, structurally models, and precisely "unlearns" noise through three core modules:
- 📐 **Geometric Fidelity Quantization (GFQ):** Estimates a noise boundary using cone space geometric separability to quantify sample fidelity.
- 🛑 **Negative Boundary Learning (NBL):** Learns a "diagonal negative combination" for each query as an explicit semantic opposite-anchor.
- 🎯 **Boundary-based Targeted Unlearning (BTU):** Models noisy correction as an Optimal Transport (OT) problem to execute precise unlearning without backlash on clean samples.
### 4. Training Data Source & Hosted Weights
The models were trained on the **FashionIQ** and **CIRR** datasets across different simulated noise ratios ($N \in \{0.2, 0.5, 0.8\}$). This Hugging Face repository provides the corresponding `.pt` checkpoint files organized by dataset and noise ratio:
* 📂 `fashioniq/`
* `ConeSep-FIQ_N0.2.pt` (Trained with 20% noise)
* `ConeSep-FIQ_N0.5.pt` (Trained with 50% noise)
* `ConeSep-FIQ_N0.8.pt` (Trained with 80% noise)
* 📂 `cirr/`
* `ConeSep-CIRR_N0.2.pt` (Trained with 20% noise)
* `ConeSep-CIRR_N0.5.pt` (Trained with 50% noise)
* `ConeSep-CIRR_N0.8.pt` (Trained with 80% noise)
---
## 🚀 Usage & Basic Inference
These weights are designed to be evaluated out-of-the-box using the official [ConeSep GitHub repository](https://github.com/iLearn-Lab/CVPR26-ConeSep).
### Step 1: Prepare the Environment
Clone the GitHub repository and set up the environment:
```bash
git clone https://github.com/iLearn-Lab/CVPR26-ConeSep
cd ConeSep
conda create -n conesep python=3.8
conda activate conesep
pip install torch==2.1.0 torchvision==0.16.0 torchaudio==2.1.0 --index-url [https://download.pytorch.org/whl/cu121](https://download.pytorch.org/whl/cu121)
pip install scikit-learn==1.3.2 transformers==4.25.0 salesforce-lavis==1.0.2 timm==0.9.16
```
### Step 2: Download Model Weights
Download the specific `.pt` files you need from this Hugging Face repository and place them into a `checkpoints/` directory within your cloned repo. For example, to evaluate the CIRR model trained with 50% noise:
```text
ConeSep/
└── checkpoints/
└── cirr_noise0.5/
└── best_model.pt <-- (Rename the downloaded ConeSep-CIRR_N0.5.pt to best_model.pt)
```
### Step 3: Run Testing / Evaluation
To generate prediction files on the CIRR dataset for the [CIRR Evaluation Server](https://cirr.cecs.anu.edu.au/), run:
```bash
# Example for testing the CIRR 50% noise model
python src/cirr_test_submission.py checkpoints/cirr_noise0.5/
```
*(The script will automatically generate the required `.json` files based on the checkpoint for online evaluation.)*
---
## ⚠️ Limitations & Notes
- **Hardware Requirements:** ConeSep is built upon the BLIP-2 architecture. It is highly recommended to run inference and training on GPUs with sufficient memory (e.g., NVIDIA A40 48GB or V100 32GB).
- **Intended Use:** These weights are intended for academic research, robustness evaluation, and reproducing the results reported in the CVPR 2026 paper.
---
## 📝⭐️ Citation
If you find our framework, code, or these weights useful in your research, please consider leaving a **Star** ⭐️ on our GitHub repository and citing our CVPR 2026 paper:
```bibtex
@InProceedings{ConeSep,
title={ConeSep: Cone-based Robust Noise-Unlearning Compositional Network for Composed Image Retrieval},
author={Li, Zixu and Hu, Yupeng and Chen, Zhiwei and Zhang, Mingyu and Fu, Zhiheng and Nie, Liqiang},
booktitle={Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
year = {2026}
}
```