activity
20152019
most citedCascade R-CNN: Delving into High Quality Object Detection

398 citations · 568 across the 6 of their papers we have counts for

collaborators

8 papers

cs.CV2019

NetTailor: Tuning the Architecture, Not Just the Weights

Pedro Morgado, Nuno Vasconcelos

Real-world applications of object recognition often require the solution of multiple tasks in a single platform. Under the standard paradigm of network fine-tuning, an entirely new…

cs.CV20191 cited

Efficient Multi-Domain Network Learning by Covariance Normalization

Yunsheng Li, Nuno Vasconcelos

The problem of multi-domain learning of deep networks is considered. An adaptive layer is induced per target domain and a novel procedure, denoted covariance normalization (CovNorm…

cs.CV2019102 cited

Cascade R-CNN: High Quality Object Detection and Instance Segmentation

Zhaowei Cai, Nuno Vasconcelos

In object detection, the intersection over union (IoU) threshold is frequently used to define positives/negatives. The threshold used to train a detector defines its \textit{qualit…

cs.CV2019

Semantic Fisher Scores for Task Transfer: Using Objects to Classify Scenes

Mandar Dixit, Yunsheng Li, Nuno Vasconcelos

The transfer of a neural network (CNN) trained to recognize objects to the task of scene classification is considered. A Bag-of-Semantics (BoS) representation is first induced, by…

cs.CV2019

Bidirectional Learning for Domain Adaptation of Semantic Segmentation

Yunsheng Li, Lu Yuan, Nuno Vasconcelos

Domain adaptation for semantic image segmentation is very necessary since manually labeling large datasets with pixel-level labels is expensive and time consuming. Existing domain…

cs.CV2017398 cited

Cascade R-CNN: Delving into High Quality Object Detection

Zhaowei Cai, Nuno Vasconcelos

In object detection, an intersection over union (IoU) threshold is required to define positives and negatives. An object detector, trained with low IoU threshold, e.g. 0.5, usually…