update

_bibliography/papers.bib

@@ -23,6 +23,26 @@ @string{ITSC
 @string{ARXIV = {arXiv}}
 
 
+@article{fahes2024fine,
+  title={Fine-Tuning CLIP's Last Visual Projector: A Few-Shot Cornucopia},
+  author={Fahes, Mohammad and Vu, Tuan-Hung and Bursuc, Andrei and P{\'e}rez, Patrick and de Charette, Raoul},
+  journal=ARXIV,
+  year={2024},
+  pdf={https://arxiv.org/abs/2410.05270},
+  code={https://github.com/astra-vision/ProLIP},
+  preview={2024-prolip.png}
+}
+
+@article{cao2024latteclip,
+  title={LatteCLIP: Unsupervised CLIP Fine-Tuning via LMM-Synthetic Texts},
+  author={Cao, Anh-Quan and Jaritz, Maximilian and Guillaumin, Matthieu and de Charette, Raoul and Bazzani, Loris},
+  journal=ARXIV,
+  year={2024},
+  pdf={https://arxiv.org/abs/2410.08211},
+  code={https://github.com/astra-vision/LatteCLIP},
+  preview={2024-latteclip.png}
+}
+
 @inproceedings{xia2024umbrae,
   title={UMBRAE: Unified Multimodal Decoding of Brain Signals},
   author={Xia, Weihao and de Charette, Raoul and Öztireli, Cengiz and Xue, Jing-Hao},

_data/coauthors.yml

@@ -1,3 +1,11 @@
+"Bazzani":
+  - firstname: ["Loris", "L."]
+    url: https://lorisbaz.github.io/
+
+"Guillaumin":
+  - firstname: ["Matthieu", "M."]
+    url: https://scholar.google.com/citations?user=jFdZ8s4AAAAJ
+
 "Dai":
   - firstname: ["Angela", "A."]
     url: https://www.3dunderstanding.org/

_data/repositories.yml

@@ -2,6 +2,8 @@ github_users:
   # - astra-vision
 
 github_repos:
+  - astra-vision/ProLIP
+  - astra-vision/LatteCLIP
   - weihaox/UMBRAE
   - astra-vision/MaterialPalette
   - astra-vision/PaSCo

_news/2024-09-01-summer.md

@@ -0,0 +1,7 @@
+---
+layout: post
+date: 2024-10-01 00:00:00-0400
+inline: true
+---
+
+The group organized the first [ACVSS summer school](https://www.acvss.ai) in Kenya and the [3rd WSCV workshop](https://wscv-indaba.github.io/2024) in Deep Learning Indaba in Dakar.

_news/2024-09-20-newpaper.md

@@ -0,0 +1,7 @@
+---
+layout: post
+date: 2024-10-20 00:00:00-0400
+inline: true
+---
+
+New papers building on VLMs: 📎 [ProLIP](https://arxiv.org/abs/2410.05270) shows that adequately finetuning the last CLIP layer significantly boost few-shot classification. ☕ [LatteCLIP](https://arxiv.org/abs/2410.08211) proposes an unsupervised prototype-based CLIP finetuning from only synthetic labels.