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.
By Santiago Poveda-Guti\'errez, Hideki Nakayama, Mayumi Bono
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.
By Sobhan Asasi, Ozge Mercanoglu Sincan, Richard Bowden
arXiv:2609.07965v1 Announce Type: cross
Abstract: Sign Language Translation has advanced with deep learning, yet evaluations remain largely signer-dependent, with overlapping signers across train/dev...
By Keren Artiaga, Sabyasachi Kamila, Haithem Afli, Conor Lynch, Mohammed Hasanuzzaman
SignMatch introduces a prototype‑structured embedding space that learns to match dictionary sign videos with continuous sign language footage based solely on visual similarity of handshape and motion. By mapping isolated dictionary exemplars into this space, the method enables direct, embedding‑based sign matching and can generalise to unseen signs using only dictionary examples. Experiments on ASL‑Citizen, ChaLearn OSLWL, and BOBSL CSLR2 benchmarks show strong cross‑dataset, cross‑task, and cross‑language performance, outperforming prior approaches on American, British, and Spanish sign languages without benchmark‑specific supervision.
By Ryan Wong, Youngjoon Jang, Liliane Momeni, G\"ul Varol, Andrew Zisserman
arXiv:2608. 09006v1 Announce Type: cross Abstract: Large Language Models (LLMs) have achieved remarkable success across a wide range of tasks.
By Shiwei Gan, Xiao Liu, Yafeng Yin, Zhiwei Jiang, Bowen Guo, Lie Xie, Sanglu Lu, Hongkai Wen
arXiv:2605. 31393v2 Announce Type: replace-cross Abstract: Sign language translation (SLT) remains constrained by the limited availability of paired sign-video/text corpora and by the heavy-tailed vocabularies typical of real-world datasets.
By Pedro Dal Bianco, Jean Paul Nunes Reinhold, Oscar Stanchi, Facundo Quiroga, Franco Ronchetti, Ulisses Brisolara Corr\^ea