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HSP-IIT/
fsosar-strm-frdisc-ssv2-5shot

Name: fsosar-strm-frdisc-ssv2-5shot
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STRM — FR-Disc — Something-Something V2 (5-shot)

Checkpoint from A Baseline Study and Benchmark for Few-Shot Open-Set Action Recognition with Feature Residual Discrimination (Berti, Pasquale, Natale — ICPR 2026), trained with the discriminator (FR-Disc) open-set loss.

Model: STRM
Dataset: Something-Something V2
Setting: 5-way 5-shot
Open-set method: FR-Disc (feature-residual discriminator)
Checkpoint eval metrics (measured for this exact checkpoint): FS-ACC = 65.51, OS-ACC = 58.99

This configuration is not part of the results table in the paper/README (STRM's FR-Disc results were not part of the final reported benchmark); this is simply the best available checkpoint found in our training logs for this setting.

Code, other checkpoints, and the FSOS-AR benchmark splits are at hsp-iit/fsosar.

Usage

python

from huggingface_hub import hf_hub_download
import torch

ckpt_path = hf_hub_download(repo_id="HSP-IIT/fsosar-strm-frdisc-ssv2-5shot", filename="model.pt")
state_dict = torch.load(ckpt_path, map_location="cpu")

# Requires the model definition from https://github.com/hsp-iit/fsosar
from models import STRM
from utils import load_configs

config = load_configs("STRM", "Something-Something V2")
model = STRM(config, disc=True, gc=False, dp=config["dp"])
model.load_state_dict(state_dict, strict=True)
model.eval()

License

BSD 3-Clause, matching the code repository.

Citation

bibtex

@inproceedings{berti2026fsosar,
  title     = {A Baseline Study and Benchmark for Few-Shot Open-Set Action Recognition with Feature Residual Discrimination},
  author    = {Berti, Stefano and Pasquale, Giulia and Natale, Lorenzo},
  booktitle = {International Conference on Pattern Recognition (ICPR)},
  year      = {2026}
}