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README.md

@@ -43,3 +43,28 @@ We acknowledge certain limitations of FashionCLIP and expect that it inherits ce
 Our investingations also suggests that the data used introduces certain limitaions in FashionCLIP. From the textual modality, given that most captions dervied from the Farfetch dataset are long, we observe that FashionCLIP maybe more performant in longer queries than shorter ones. From the image modality, FashionCLIP is also biased towards standard product images (centered, white background).
 
 Model selection, i.e. selecting an appropariate stopping critera during fine-tuning, remains an open challenge. We observed that using loss on an in-domain (i.e. same distribution as test) validation dataset is a poor selection critera when out-of-domain generalization (i.e. across different datasets) is desired, even when the dataset used is relatively diverse and large.
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+
+## Citation
+```
+@Article{Chia2022,
+    title="Contrastive language and vision learning of general fashion concepts",
+    author="Chia, Patrick John
+            and Attanasio, Giuseppe
+            and Bianchi, Federico
+            and Terragni, Silvia
+            and Magalh{\~a}es, Ana Rita
+            and Goncalves, Diogo
+            and Greco, Ciro
+            and Tagliabue, Jacopo",
+    journal="Scientific Reports",
+    year="2022",
+    month="Nov",
+    day="08",
+    volume="12",
+    number="1",
+    abstract="The steady rise of online shopping goes hand in hand with the development of increasingly complex ML and NLP models. While most use cases are cast as specialized supervised learning problems, we argue that practitioners would greatly benefit from general and transferable representations of products. In this work, we build on recent developments in contrastive learning to train FashionCLIP, a CLIP-like model adapted for the fashion industry. We demonstrate the effectiveness of the representations learned by FashionCLIP with extensive tests across a variety of tasks, datasets and generalization probes. We argue that adaptations of large pre-trained models such as CLIP offer new perspectives in terms of scalability and sustainability for certain types of players in the industry. Finally, we detail the costs and environmental impact of training, and release the model weights and code as open source contribution to the community.",
+    issn="2045-2322",
+    doi="10.1038/s41598-022-23052-9",
+    url="https://doi.org/10.1038/s41598-022-23052-9"
+}