👨🎓 I am pleased to announce that I will defend my thesis entitled “Bias and reasoning in Visual Question Answering”, supervised by @chriswolfvision @moezbac and @antigregory, next Thursday (09/12) at 10am, Paris time.
Send me a pm if you wish to attend 😊
Our only submission to #NeurIPS2021 has been accepted. Work by @CorentK, yours truly, @antigregory, @moezbac, M. Nadri.
We transfer reasoning patterns from Oracle VQA models to noisy models, regularizing with an auxiliary loss for program prediction.
https://t.co/dBquSdkLOJ
Excited to see our paper accepted to #ieeevis 2021!
“VisQA: X-raying Vision and Language Reasoning in Transformers”, a deep dive in attention maps to grasp bias in transformers.
Demo: https://t.co/QORx1PyBuO
Paper: https://t.co/GIb7SBmRzj
Now time to write my doctoral thesis😱
Can oracles (which take GT visual input) learn better reasoning patterns in VQA? Can we visualize them? Are they linked to language/task functions? Can they be transferred to deployable models?
Answers will be given Tuesday by @CorentK#CVPR2021
12am CET = 6am EST = 3am PT
Roses are red, Violets are blue ... but should VQA expect them to?
#CVPR2021 poster presented tomorrow Tuesday by @CorentK
4am CET = 10pm EST = 7pm PT
We need to get up at 4am, so you better be there to joint us 😏
New paper: we show that transfer from Oracle input to real improves VQA reasoning when a strong link is established through program supervision: empirical results + analysis of sample complexity.
Work with @CorentK@antigregory @moezbac and M. Nadri.
https://t.co/dBquSdCndj
We introduce a new benchmark to evaluate VQA in out-of-distribution settings by reorganizing the GQA dataset, taylored for each sample (question group), targeting research in bias reduction in VQA.
Work by @CorentK, @antigregory, @moezbac and yours, truly.
https://t.co/Fji3P7jJgd
Second paper: we introduce a new semantic loss for VQA adding structure to the VQA answer space estimated from redundancy in annotations, questioning the classification approach to VQA.
Work by @CorentK, @antigregory, @moezbac and yours, truly.
https://t.co/hvPFvhdtlm
New paper from @CorentK, co-supervised with @moezbac @antigregory. We show that object-word alignment does not emerge naturally in BERT like training of transformers on vision-language task and propose weak supervision for it, improving performance:
https://t.co/rB2lI9NrZU