Sign spotting, the task of deciding whether an isolated query sign occurs inside a clip of continuous signing, enables sign language video retrieval without gloss or text annotation. We describe our system for the WSLP@EMNLP-2026 Word/Sign Presence Prediction shared task on Indian Sign Language: a lightweight (2.86M-parameter) pose-based Siamese encoder that embeds both clips in one space, forms an explicit frame-to-frame cosine similarity map, and reduces it with multiple-instance pooling over temporal windows. Training combines binary cross-entropy with a multiple-instance contrastive objective (MIL-NCE). The system reaches 72.69 accuracy and 71.95 macro-F1 on the blind test set, against 61.66 and 58.42 for the published pose baseline on the same data. Ablations identify contrastive alignment as the component that makes the architecture work: the MIL-NCE objective is what places the two clips in a comparable metric space, and training does not leave chance without it. We further document a label-balance property of the released splits that fixes 76.3% of blind test labels without a model, and recommend a change for future editions.

We address cross-signer Isolated Sign Language Recognition (ISLR) for Indian Sign Language as part of the WSLP-2026 shared task, where most target words have exactly one available training video and a system must recognize a query video performed by a signer never seen for that class during training. We approach this with a metric-learning encoder trained in two phases: pretraining on a large, multi-signer American Sign Language corpus to learn general signer-invariance, then fine-tuning on the target vocabulary using every real cross-signer recording we can responsibly source. Our preprocessing detects and extracts multiple performances of a word within a single recording as separate training samples, a technique not previously reported in SLR literature, and adapts a published missing-keypoint interpolation technique into a retrieval rather than classification pipeline. Our final system achieves 67.11% Top-1, 69.20% Top-5, and 70.34% Top-10 accuracy on the WSLP-2026 test-phase evaluation.