Since the acquisition and annotation of real-world data is complex, computer vision datasets only capture a fraction of our continuous world. To cope with unseen conditions, fight biases of the training data, or simply reduce our dependency on data, algorithms may be trained in a weak-/un-supervised fashion. Recently, novel avenues of research have emerged to relax supervision (less labels, less data) for example using multimodal models, generative AI, transfer learning, continual learning, etc. This lets us foresee new frontiers of computer vision, holding immense potential for the African society. This 3rd WSCV edition will gather leading computer vision figures with keynotes and lightning talks on various topics like: zero-shot training, multimodal models / foundational models, open-vocabulary, self-/un-supervised training, diffusion models, robustness and uncertainty estimation; as well as talks on african initiatives.

📢 The workshop will have a poster session showcasing participants works on computer vision.
Prize will be awarded 🏆

News 08/21: Papers submission is now closed. DLI participants can still present their DLI poster at the workshop (cf. contact at the bottom). We announced the prizes details ;-)