task_categories:
- image-segmentation
license: cc-by-nc-4.0
library_name: mmsegmentation
CorrCLIPv2: Pre-generated Masks and Prebuilt Memory Banks
This repository provides the pre-generated region masks and prebuilt memory banks for CorrCLIPv2: Harnessing Unlabeled Images for Open-Vocabulary Semantic Segmentation, published in the International Journal of Computer Vision (IJCV), 2026.
Journal version · arXiv version
The arXiv version corresponds to the initial submission, while the journal version includes additional ablation studies and re-evaluates other methods using the same models as CorrCLIPv2 for a consistent comparison.
CorrCLIPv2 augments single-image open-vocabulary segmentation with cross-image semantic support retrieved from an offline memory bank built purely from unlabeled images — no annotation, no caption, no training. Downloading the artifacts here lets you skip both the mask generation and the bank construction step.
For the code, installation instructions and evaluation scripts, see the CorrCLIPv2 README. Run commands from the CorrCLIPv2/ directory unless otherwise specified; all local paths below are relative to that directory.
Region masks
instance_mask.zip (110 MiB) — EntitySeg region masks for the ten standard benchmarks. Extract it into data/, which gives data/instance_mask/{voc,context,coco,ade,ade847,city}/ as instance_mask_path expects. With these in place, set mask_generator=None and no mask generator needs to be installed.
Prebuilt banks
Four banks are provided: coco, built from COCO train2017 and used for the main results; monet, built from the fully synthetic MONET Z-Image subset; and gpic and sa1b, built from GPIC and SA-1B images. The latter three banks are image-level disjoint from the evaluation sets. Prebuilt files support both CorrCLIPv2-B and CorrCLIPv2-L for coco and monet, and CorrCLIPv2-B for gpic and sa1b.
With DMCE enabled, one configuration needs three files: a main store matching the CLIP scale, the DFN auxiliary store, and the FAISS index. Pick a row and download those files; for a given bank, the B and L configurations share the auxiliary store and index. Sizes below are approximate binary units (GiB/MiB).
| Configuration | Files to download | Total |
|---|---|---|
CorrCLIPv2-L + coco (main results) |
coco_metaclip_l.pt 4.22 GiB · coco_dfnclip_b.pt 2.81 GiB · coco_radiov3.faiss 190 MiB | 7.21 GiB |
CorrCLIPv2-B + coco |
coco_metaclip_b.pt 2.81 GiB · coco_dfnclip_b.pt 2.81 GiB · coco_radiov3.faiss 190 MiB | 5.81 GiB |
CorrCLIPv2-L + monet (synthetic, disjoint) |
monet_metaclip_l.pt 2.15 GiB · monet_dfnclip_b.pt 1.43 GiB · monet_radiov3.faiss 108 MiB | 3.68 GiB |
CorrCLIPv2-B + monet |
monet_metaclip_b.pt 1.43 GiB · monet_dfnclip_b.pt 1.43 GiB · monet_radiov3.faiss 108 MiB | 2.97 GiB |
CorrCLIPv2-B + gpic (disjoint) |
gpic_metaclip_b.pt 2.20 GiB · gpic_dfnclip_b.pt 2.20 GiB · gpic_radiov3.faiss 154 MiB | 4.54 GiB |
CorrCLIPv2-B + sa1b (disjoint) |
sa1b_metaclip_b.pt 3.69 GiB · sa1b_dfnclip_b.pt 3.69 GiB · sa1b_radiov3.faiss 240 MiB | 7.61 GiB |
Put the .pt files in memory_bank/clip_embeddings/ and the .faiss file in memory_bank/vfm_embeddings/, keeping the filenames unchanged — the loader resolves each by name. Then set _clip_scale and _mb_bank in configs/base_config.py to match the row you chose and run as usual; vfm_type stays radiov3, the space these indexes were built in. For the no-DMCE ablation, set use_aux_model=False and skip the DFN store.
Each .pt store is an [N, D] fp16 tensor loaded fully into RAM at startup; its row index is the region id, and the FAISS index (IDMap2) returns the same ids, so search results index the stores directly.
Bank image lists
The image lists behind the disjoint banks are released for auditing — one source image path per line, the exact set each bank was built from: gpic (6.2 MiB), monet (2.8 MiB), sa1b (1.0 MiB). The coco bank needs no list: it is the full COCO train2017 image set.
Source data
The masks are derived from the public validation images of the corresponding benchmarks; the coco bank is derived from COCO train2017 and the monet bank from the MONET Z-Image synthetic subset. This repository distributes derived masks, feature stores, retrieval indexes, and source image lists, but no source images — the original datasets' own licenses and terms continue to apply to them. The disjoint-bank source images are available from SA-1B, GPIC, and MONET.
Citation
If you use CorrCLIPv2 or these artifacts in your research, please cite the journal paper:
@article{zhang2026corrclipv2,
title={CorrCLIPv2: Harnessing Unlabeled Images for Open-Vocabulary Semantic Segmentation},
author={Zhang, Dengke and Liu, Fagui and Tang, Quan and Chen, CL Philip},
journal={International Journal of Computer Vision},
volume={134},
number={11},
pages={478},
year={2026},
publisher={Springer}
}