# T-VSE: Transformer-Based Visual Semantic Embedding Source: [T-VSE: Transformer-Based Visual Semantic Embedding]() Collection index: [llms.txt]() Data updated: 2025-10-25T22:17:13.228Z Authors: Muhammet Bastan, Arnau Ramisa, Mehmet Tek Published: 2020-05-17T23:40:33.000Z ## Abstract Transformer models have recently achieved impressive performance on NLP tasks, owing to new algorithms for self-supervised pre-training on very large text corpora. In contrast, recent literature suggests that simple average word models outperform more complicated language models, e.g., RNNs and Transformers, on cross-modal image/text search tasks on standard benchmarks, like MS COCO. In this paper, we show that dataset scale and training strategy are critical and demonstrate that transformer-based cross-modal embeddings outperform word average and RNN-based embeddings by a large margin, when trained on a large dataset of e-commerce product image-title pairs. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()