4 accepted papers
Current sign language translation (SLT) approaches often rely on gloss-based supervision with Connectionist Temporal Classification (CTC), limiting their ability to handle non-monotonic alignments between sign language video and spoken text. In this work, we propose a novel method combining joint CT…
Sign Language Translation (SLT) aims to convert sign language (SL) videos into spoken language text, thereby bridging the communication gap between the sign and the spoken community. While most existing works focus on translating a single SL into a single spoken language (one-to-one SLT), leveraging…
Existing arbitrary-shaped text (ArT) spotting models have used coordinate-based error (CBE) loss to optimize polygon region detection. Optimization using CBE loss requires a fine-grained ground truth (FineGT), which is a fixed-length list of polygon vertices. However, not all datasets have FineGT, a…
A transformer model is used in general tasks such as pre-trained language models and specific tasks including machine translation. Such a model mainly relies on positional encodings (PEs) to handle the sequential order of input vectors. There are variations of PEs, such as absolute and relative, and…