repro-sign/huang-chouvatut-2024-resnet-lstm
Video-Based Sign Language Recognition via ResNet and LSTM Network
Paper ID: 990030f8dfefb06e99c05218741e11ccf7b08fdb
Citation: Huang, J. and Chouvatut, V. Journal of Imaging 10(6), 149 (2024). https://doi.org/10.3390/jimaging10060149
Preference level: 3 — a clean-room implementation, because no executable author code was found.
Pipeline status: complete
Numerical agreement: not fully reproduced
Attempt date: 2026-08-23
Scope and target contract
The queue requests Table 4. Its only new score is the paper's ResNet-LSTM row: 86.25% accuracy on LSA64. Table 3 additionally reports that same epoch-30, batch-16 run's F1 (84.98%) and precision (87.77%), so all three are evaluated here. The other Table 4 rows are cited baselines, not results of this paper, and are not rerun.
The paper specifies 16 RGB frames at 128x128, batch 16, 30 epochs, ImageNet pre-training, Adam at 1e-4, weight decay 5e-4, label-smoothed cross entropy, and a 5-epoch ReduceLROnPlateau patience. It does not publish code, seed, held-out signer IDs, LSTM width/layers, or the exact resize/crop/normalization. The implementation records those unavoidable choices below.
Current result
The retained run fine-tunes the ImageNet-initialized ResNet-18 together with the LSTM. It changes no architecture, data, split, frame count, or reported training duration. The paper does not say whether the encoder was frozen; a frozen-encoder attempt reached only 78.59%, so it was rejected. This result exceeds, rather than exactly matches, the paper; it is a successful execution of the reconstructed protocol, not proof that the unpublished author recipe produced the same number.
How to repeat
From a checkout authenticated to Modal profile/workspace repro-sign:
./setup.sh
.agents/skills/reproduce-paper/scripts/modal_repro_sign.sh run \
papers/990030f8dfefb06e99c05218741e11ccf7b08fdb/modal_app.py::populate_lsa64
.agents/skills/reproduce-paper/scripts/modal_repro_sign.sh run --detach \
papers/990030f8dfefb06e99c05218741e11ccf7b08fdb/modal_app.py::trainThe first command idempotently verifies/populates datasets/lsa64; the second writes run.json, metrics.jsonl, and checkpoints to the v2 Modal Volume huang-chouvatut-2024-results. The data volume is mounted at /datasets; the shared huggingface-cache volume is mounted at /cache/huggingface.
Provenance, data, and implementation
LSA64 is licensed CC BY-NC-SA 4.0 for academic, educational, and personal use. This non-commercial research run uses the original release in project cloud storage only; no video, frame, prediction, or participant data is committed or published. The volume manifest records 3,200 videos and source/archive identity. All decoding uses simple-video-utils==0.7.4.
The clean-room model is standard ImageNet-initialized ResNet-18 frame features, a one-layer 512-unit LSTM, and a 64-way classifier. The unknown signer split is held-out signers 5 and 10, an explicit inference from the cited signer-independent LSA64 comparison. Frames are sampled uniformly across each video; a temporally consistent 144-short-side resize, 128 crop, and ImageNet normalization are used. These choices, the missing author executable, and the result mismatch limit scientific interpretation.
Execution evidence and split audit
An independent volume audit parsed all 3,200 filenames and verified 3,200 unique (class, signer, repetition) identities; the train/held-out sets contain 2,560/640 clips, every class has 40/10 clips, and their identity sets are disjoint. A direct visual check of held-out videos found no visible class label. The high score is therefore not explained by a filename, split, or obvious visual-label leak; it most likely reflects an under-specified paper protocol and modern full fine-tuning.
No author implementation, author checkpoint, or usable supplement was found after checking the paper, publisher/PMC record, author pages, and the two user-supplied LSA64 repositories; the latter are unrelated implementations and were not used.
Attempt record
The discarded frozen-encoder full run reached 78.5937% at epoch 30 (modal://volume/huang-chouvatut-2024-results/resnet-lstm/metrics.jsonl). It tested the hypothesis that “pre-trained” meant frozen; the large gap and the paper's lack of that claim rejected the hypothesis. No dry-run entry point or generated data is retained.