中科院计算所Recent advances in deep learning

Recent advances in deep learning (biased & by no means complete)

Going deeper

image from K. He’s slides

Going deeper

“D eep R es i d U a l L ea r n i n g f o r Im a g e R ec o g n i t i o n”,H e, Zh a n g,R e n,Su n.C V PR2016.

a rX iv:1512.03385. D e c. 2015

“Highway Networks”, Srivastava, Greff, Schmidhuber, arXiv:1505.00387. May 2015 “Training Very Deep Networks”, Srivastava, Greff, Schmidhuber. NIPS 2015

image from K. He’s slides

Deep residual learning

Probably the most significant Deep Learning work in 2015.

“D eep R es i d U a l L ea r n i n g f o r Im a g e R ec o g n i t i o n”.H e, Zh a n g,R e n,Su n,C V PR2016.

a rX iv:1512.03385. D e c. 2015

image from K. He’s slides

Detection ~0.3s per image

“Fa s t R-C NN”,G irs h ic k.ICCV2015

“Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks”, Ren, He, Girshick, Sun. NIPS 2015

“Y o U O n l y L oo k O n c e:U n i fied,R ea l-T i m e O b j ec t D e t ec t i o n”,R e d mo n, Divvala, Girshick, Farhadi. CVPR 2016

image from Girshick 2015.

Segmentation

“F U ll y C o n v o l U t i o n a l N e t w o r k s f o r S em a n t i c S eg m en t a t i o n”,L o n g,Sh e lh a me r, D a rr e ll.C V PR2015.(C V PR b e s t pap e r h o n o r ab le me nt io n)

“E f fi c i en t P i ec ew i s e T r a i n i n g o f D eep S t r U c t U r ed M o d el s f o r S em a n t i c S eg m en t a t i o n”,L in,Sh e n, v a n da n H e n g e l,R e id.C VP R2016.

“C o n d i t i o n a l R a n d o m F i el d s a s R ec U rr en t N EU r a l N e t w o r k s”,Z hen g e t a l.IC C V 2015.

“H i g h-p er f o r m a n c e S em a n t i c S eg m en t a t i o n U s i n g V er y D eep F U ll y C o n v o l U t i o n a l N e t w o r k s”,W u,Sh e n, v a n d e n H e n g e l.a rX iv:1604.04339.A p ril2016.

ima g e f r o m L o n g e t a l.2015

Vision & Language (c)Baidu

Visual question answering

“A s k M e A n yt h i n g:F r ee-f o r m V i s U a l Q U es t i o n A n s w er i n g B a s ed o n K n o w l ed g e f r o m E x t er n a l S o U r ces”,W u e t a l.

a rX iv:1506.01144,C V PR2016

“Wh a t V a l U e H i g h L e v el C o n c ept s i n V i s i o n t o L a n g U a g e P r o b l em s?”,W u e t a l.C V PR2016 “V Q A:

V is u a l Q u e s t io n A n s w e r in g”,A nt o l e t a l.I CC V 2015.

“Ask Your Neurons: A Neural-Based Approach to Answering Questions About Images”, Malinowski et al. ICCV 2015.

and many other papers …

Methodology (What I believe is important)