Mining Latent Classes for Few-shot Segmentation

Few-shot segmentation (FSS) aims to segment unseen classes given only a few annotated samples. Existing methods suffer the problem of feature undermining, i.e. potential novel classes are treated as background during training phase. Our method aims to alleviate this problem and enhance the feature embedding on latent novel classes. In our work, we propose a novel joint-training framework. Based on conventional episodic training on support-query pairs, we add an additional mining branch that exploits latent novel classes via transferable sub-clusters, and a new rectification technique on both background and foreground categories to enforce more stable prototypes. Over and above that, our transferable sub-cluster has the ability to leverage extra unlabeled data for further feature enhancement. Extensive experiments on two FSS benchmarks demonstrate that our method outperforms previous state-of-the-art by a large margin of 3.7% mIOU on PASCAL-5i and 7.0% mIOU on COCO-20i at the cost of 74% fewer parameters and 2.5x faster inference speed.

Improving Sequential Recommendation with Attribute-augmented Graph Neural Networks

Many practical recommender systems provide item recommendation for different users only via mining user-item interactions but totally ignoring the rich attribute information of items that users interact with. In this paper, we propose an attribute-augmented graph neural network model named Murzim. Murzim takes as input the graphs constructed from the user-item interaction sequences and corresponding item attribute sequences. By combining the GNNs with node aggregation and an attention network, Murzim can capture user preference patterns, generate embeddings for user-item interaction sequences, and then generate recommendations through next-item prediction. We conduct extensive experiments on multiple datasets. Experimental results show that Murzim outperforms several state-of-the-art methods in terms of recall and MRR, which illustrates that Murzim can make use of item attribute information to produce better recommendations. At present, Murzim has been deployed in MX Player, one of India's largest streaming platforms, and is recommending videos for tens of thousands of users.

Few-Shot Object Detection with Attention-RPN and Multi-Relation Detector

Conventional methods for object detection usually requires substantial amount of training data and to prepare such high quality training data is labor intensive. In this paper, we propose few-shot object detection which aims to detect objects of unseen class with a few training examples. Central to our method is the Attention-RPN and the multi-relation module which fully exploit the similarity between the few shot training examples and the test set to detect novel objects while suppressing the false detection in background. To train our network, we have prepared a new dataset which contains 1000 categories of varies objects with high quality annotations. To the best of our knowledge, this is also the first dataset specifically designed for few shot object detection. Once our network is trained, we can apply object detection for unseen classes without further training or fine tuning. This is also the major advantage of few shot object detection. Our method is general, and has a wide range of applications. We demonstrate the effectiveness of our method quantitatively and qualitatively on different datasets. The dataset link is: https://github.com/fanq15/Few-Shot-Object-Detection-Dataset.