license: apache-2.0
library_name: pytorch
pipeline_tag: image-segmentation
tags:
- semantic-segmentation
- semi-supervised-learning
- contrastive-learning
- dinov2
- pytorch
- arxiv:2607.03068
datasets:
- pascal-voc
PixCon: Clean-Positive Contrastive Learning for Foundation-Model Semi-Supervised Segmentation
PixCon model family — Pascal VOC · Cityscapes · ADE20K · 🤗 Interactive demo · 🌐 Project page · 💻 Code · 📄 Paper
TL;DR. With a DINOv2 teacher, a strict confidence threshold already retains a measured ~98%-clean pseudo-label set, so the accuracy that remains lives in how the embedding space is structured by class, not in the filter. PixCon adds a single clean-positive pixel-contrastive branch on top of a UniMatch V2 consistency backbone: a per-class memory bank that admits only labeled pixels the student already classifies correctly, giving a contamination-free positive set (ρ_F = 0) by construction, unlike prior contrastive SSSS banks (ReCo, U²PL) built from confidence-filtered pseudo-labels. It adds no inference-time parameters and needs no bank-specific threshold.
Method
| Component | Setting |
|---|---|
| Backbone | DINOv2-Base (ViT-B/14), fine-tuned end-to-end |
| Decoder | DPT-lite (4 ViT layers → coarse-to-fine pyramid) |
| Consistency | Two strong+CutMix views, complementary channel dropout (UniMatch V2) |
| Threshold | Fixed conf ≥ 0.95 |
| Auxiliary | PixCon: 1×1 projection head, per-class memory bank (256/class), clean-positive filter (labeled ∧ pred==GT), supervised InfoNCE (τ = 0.1) |
| Optimizer | AdamW, backbone LR 5e-6, decoder LR 2e-4, poly schedule |
The contribution is the clean-positive bank: every entry is a labeled pixel whose student prediction already matches the ground truth. A first-order analysis of the supervised-InfoNCE gradient shows the false-positive term scales as ρ_F/(1−ρ_F); we measure ρ_F (0.018 on Pascal, 0.106 on ADE20K) rather than assume it.
Results (honest framing)
In a compute-matched one-switch comparison against a strong DINOv2 UniMatch V2 baseline across Pascal VOC, Cityscapes, and ADE20K, PixCon matches or improves the baseline:
- Pascal-1/8: improves every seed (per-seed gain ~+0.2 mIoU, the correctness lever).
- Its three-seed mean reaches 87.90 mIoU, the published UniMatch V2-B figure. (The larger 3-seed mean gap is driven substantially by variance reduction and is reported as suggestive, not as a per-seed accuracy claim.)
- Because contamination is already rare under a foundation-model teacher, the ρ_F = 0 guarantee acts chiefly as robustness as teachers weaken; the accuracy gain comes from cleaner positive supervision.
The released checkpoint is a single representative seed (88.00 mIoU, the seed closest to the reported mean), not a best-of-seeds pick. The headline number is the three-seed mean 87.90 (per-seed: 87.60 / 88.00 / 88.10).
Usage
import torch
from torchvision import transforms as T
from PIL import Image
from model.segmentor import PixConSegmentor # from the PixCon code
from core.inference import whole_inference
# Build the architecture and load the released EMA-teacher weights.
model = PixConSegmentor(backbone='dinov2_vitb14', nclass=21, pretrained=False).eval()
sd = torch.load('pixcon_pascal_1_8.pth', map_location='cpu')
# strict=False: the contrastive proj_head is not in the eval path and is absent from the
# released weights; the backbone/decoder/segmentation head all load.
model.load_state_dict(sd, strict=False) # slim EMA-teacher state_dict
# ImageNet normalization (matches training).
norm = T.Compose([T.ToTensor(),
T.Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225))])
img = norm(Image.open('example.jpg').convert('RGB')).unsqueeze(0)
with torch.no_grad():
logits = whole_inference(model, img) # [1, 21, H, W], pads to /14 internally
pred = logits.argmax(1) # [1, H, W] class indices (Pascal VOC, 21 classes)
An interactive demo is available as a Hugging Face Space (see the paper page).
Citation
@article{tarubinga2026pixcon,
title = {PixCon: Clean-Positive Contrastive Learning for Foundation-Model
Semi-Supervised Segmentation},
author = {Tarubinga, Ebenezer},
journal = {arXiv preprint arXiv:2607.03068},
year = {2026}
}
License
Released under Apache-2.0 (confirm this is compatible with your DINOv2 / UniMatch V2 dependencies
before publishing). DINOv2 weights are loaded from facebookresearch/dinov2 at build time.