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
RAIDAL is an active learning framework for continuous sign language recognition that leverages the CTC decoder’s alignment peaks to focus sample selection on gloss‑aligned regions, thereby avoiding temporal redundancy in weakly aligned videos. By restricting representation‑based scoring to these decoder‑aligned gloss areas, RAIDAL improves data efficiency across multiple datasets and architectures, especially in large‑vocabulary, budget‑limited scenarios. The method requires no extra labeling cost and its implementation is publicly available on GitHub.
By Rafael A. Diniz Augusto, Gabriel L. Oliveira, Erickson R. Nascimento
arXiv:2503. 08038v2 Announce Type: replace-cross Abstract: In this paper, we delve deeper into the Kullback-Leibler (KL) Divergence loss and mathematically prove that it is equivalent to the Decoupled Kullback-Leibler (DKL) Divergence loss that consists of (1) a weighted Mean Square Error (wMSE) loss and (2) a Cross-Entropy loss incorporating soft labels.
By Jiequan Cui, Beier Zhu, Qingshan Xu, Zhuotao Tian, Xiaojuan Qi, Bei Yu, Hanwang Zhang, Richang Hong
arXiv:2607. 18088v1 Announce Type: new Abstract: Standard evaluation of many recognition systems contains distribution shift by construction, since benchmarks place disjoint conditions in the training and test splits.
By Weijia Han, Lisha Qu
The paper compares five machine unlearning (MU) methods—NegGrad, Fine‑Tuning (FT), Random Labeling (RL), SalUn, and MUNBa—on noisy‑label correction across CIFAR‑10, CIFAR‑100, and Food‑101N. Results show that the best MU strategy depends on the noise type: FT works well for most closed‑set noise, RL and SalUn are robust and nearly match retraining accuracy under instance‑dependent noise, while MUNBa excels only under extreme symmetric noise. In open‑set noise, retraining on the cleaned data actually hurts performance, indicating that approximating retraining is not suitable in that regime, yet all MU methods still achieve near‑retraining accuracy on Food‑101N with much lower runtime.
By Jo\~ao L. P. Santana, Filipe R. Cordeiro
Continuous sign language recognition (CSLR) is a key technology for accessibility, yet its development remains limited by the high cost of annotating continuous video streams. Active learning offers a...
arXiv:2605. 20347v2 Announce Type: replace Abstract: Labeling a training set is often expensive and susceptible to errors, making the design of robust loss functions for label noise an important problem.
By Alexandre Lemire Paquin, Brahim Chaib-Draa, Philippe Gigu\`ere
arXiv:2606. 17579v1 Announce Type: cross Abstract: Adding LLM-generated node features to graph neural networks (GNNs) is widely reported to improve accuracy on standard benchmarks.
By Zhongyuan Wang, Pratyusha Vemuri
arXiv:2606. 12629v1 Announce Type: cross Abstract: We show that the standard basis of transformer hidden states already provides a training-free, architecture-general feature basis.
By Varun Reddy Nalagatla
The paper investigates how to choose the best quantized model from a family of compressed versions when target labels are scarce or unavailable. It finds that a simple rule based on minimum teacher distortion consistently selects the same eight‑bit, per‑channel, unclipped configuration, though this does not minimize empirical target cross‑entropy. The study also shows that confidence‑based estimators perform poorly in overconfident regimes, while output‑distribution estimators can outperform the teacher in some architectures, and that combining distortion with a supervised term can improve selection. Across 134 candidate families, teacher‑anchored selection reduces mean regret with very few labels, though the benefit diminishes after about 25 labels.
By Alejandro Rodriguez Dominguez, Muhammad Shahzad, Xia Hong
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
arXiv:2608.29395v1 Announce Type: new
Abstract: Vision-language models such as CLIP and SigLIP provide strong zero-shot recognition, but their predictions can degrade when deployed on target data tha...
By Pedram MohajerAnsari, Amir Salarpour, Run Wang, Mert D. Pes\'e