One paper has been accepted to Pattern Recognition (IF 8.518).
Authors: Moon Ye-Bin (POSTECH), Dongmin Choi (KAIST), Yongjin Kwon (ETRI), Junsik Kim (Harvard University), Tae-Hyun Oh (POSTECH)
Illustration of our ENInst (left) and the proposed enhancement methods developed by our analysis (right). 1) We train the whole network in the base training phase (gray region), 2) fine-tune the prediction heads (blue region), where the classification head for novel classes is parameterized by a linear combination of base classifiers and random vectors, named as the novel classifier composition (NCC; pink region), and its coefficients are fine-tuned with Manifold Mixup (Verma et al., 2019), 3) and then conduct inference with instance-wise mask refinement (IMR; yellow region) in a test-time optimization manner.
Label efficiency of ENInst on MS-COCO (Lin et al., 2014). Our ENInst needs much fewer clicks to achieve similar performance to fully-supervised MTFA (Ganea et al., 2021), where F denotes fully-supervised setting with mask, and W denotes weak one with bounding box for novel classes adaption.