DLDENet: Deep Local Directional Embeddings with Increased Foreground Focal Loss for object detection We propose modifications to the RetinaNet architecture and Focal Loss 1 to improve the effectiveness of them in the context of object detection. We show that through normalizing the embeddings generated by each receptive field in the feature map, classifying them using cosine similarity, increasing the loss for the foreground anchors and forcing the dispersion of the classification vectors will improve the performance of the model. We test our proposal with the FlickrLogos32 2 dataset that contains 40 examples for each one of the 32 classes, achieving competitive results with respect to the stateoftheart approaches with a precision of 0.975, a recall of 0.944, an accuracy of 0.949 and improving the mAP over the dataset from 0.657 with RetinaNet 1 to 0.775 using our new architecture DLDENet.

EMLNET:An Expandable MultiLayer NETwork for Saliency Prediction Abstract   Existing advanced saliency systems have been proposed using Convolutional Neural Networks (CNNs), the prior knowledge of objectness is crucial for saliency detection. However, we show that the use of objectness may also limit the power of CNNs on the images which do not contain a salient object. Besides, one previous study has shown applying a deeper CNN model may not improve the performance, the reason behind could be due to the limited training data. In this work, we aim at investigating the effect of prior knowledge from other domains and proposing a multimodality system. Our work shows the depth of the model still plays an important role in saliency detection when the training data is large enough. To do this in a sophisticated manner can be complex, and also result in unwieldy networks or produce competing objectives that are hard to balance. For the scalability, our multimodality system is trained in an almost endtoend piecewise fashion. The encoder and decoder components are separately trained to deal with complexity tied to the computational paradigm and required space. Therefore, our system can be easily extended to include a variety of prior knowledge for saliency detection. Besides, we also propose a combined saliency loss based on modifications of Pearson correlation and normalized scanpath saliency. Our experiment shows the combined loss can train a CNN model more comprehensively for saliency detection. We denote our expandable multilayer network as EMLNET and our method achieves the stateoftheart results on the public saliency benchmarks, SALICON, MIT300 and CAT2000.