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The paper introduces a reverse sign language dictionary that recognizes signs from continuous signing without relying on gloss annotations. It does this by captioning sign-level video clips into free-form procedural descriptions using an open-weight vision‑language model, then retrieving the closest description from a multilingual sentence encoder’s vocabulary. Experiments on a Japanese Sign Language corpus show that fine‑tuning the captioner boosts seen‑class retrieval from 4.5% to 49% and improves unseen‑class retrieval from 11.5% to 21%, matching or surpassing traditional closed‑set classifiers where applicable.
The paper introduces a reverse sign‑language dictionary that recognizes signs from continuous signing without relying on gloss labels. It does this by first captioning a sign‑level video clip into a free‑form procedural description using an open‑weight vision‑language model, then retrieving the closest entry from a multilingual sentence encoder’s vocabulary of target descriptions. Experiments on a Japanese Sign Language dialogue corpus show that fine‑tuning the captioner boosts seen‑class retrieval from 4.5 % to 49 % and improves unseen‑class retrieval from 11.5 % to 21 %, approaching the performance of a standard closed‑set classifier while enabling open‑vocabulary recognition.
The paper introduces SignShift, a framework for visual-only sentence-level segmentation of continuous sign language videos. It uses a Temporal Difference Module that captures frame-to-frame feature variations across full-frame, facial, and hand cues, and a Segment Count Prediction module to guide boundary selection. Experiments on benchmark datasets show that SignShift outperforms existing methods, demonstrating its effectiveness for this challenging task.
SignSeek is a new method for learning transferable sign representations that enables efficient retrieval of signs from dictionaries using only a query video. It employs contrastive learning with saliency‑guided articulator masking, aligning same‑gloss signs across signers while focusing on the single most critical articulator per sign. Trained on 266K samples from multiple sign languages, SignSeek achieves state‑of‑the‑art cross‑corpus retrieval performance and zero‑shot generalisation to unseen British Sign Language, also improving isolated sign recognition and subtitle alignment.
SignBind-LLM introduces a modular framework for sign language translation that separates continuous signing, fingerspelling, and lipreading into dedicated expert streams. Each expert is pre‑trained independently on about two million pseudo‑gloss sequences, eliminating the need for manual gloss annotation. A lightweight transformer fuses the expert outputs, and a pre‑trained language model converts the fused pseudo‑glosses into fluent English, achieving state‑of‑the‑art performance on multiple benchmarks with lower training cost.
The paper introduces FS23K, a large-scale British Sign Language fingerspelling dataset created through an iterative annotation framework. It also presents a recognition model that incorporates bi‑manual interactions and mouthing cues, achieving a halved character error rate compared to previous state‑of‑the‑art methods. These results underscore the dataset’s and model’s value for advancing sign language research and automated annotation pipelines.