腾讯 AI Lab 官网

腾讯 AI Lab 官网
论文

Multimedia Hashing and Networking

This department discusses multimedia hashing and networking. The authors summarize shallow-learning-based hashing and deep-learning-based hashing. By exploiting successful shallow-learning algorithms, state-of-the-art hashing techniques have been widely used in high-efficiency multimedia storage, indexing, and retrieval, especially in multimedia search applications on smartphone devices...

IEEE MultiMedia · 2016

Deep Learning Driven Visual Path Prediction from a Single Image

Capabilities of inference and prediction are the significant components of visual systems. Visual path prediction is an important and challenging task among them, with the goal to infer the future path of a visual object in a static scene. This task is complicated as it needs high-level semantic understandings of both the scenes and underlying motion patterns in video sequences...

IEEE Transactions on Image Processing · Dec, 2016

Hierarchical Visualization of Video Search Results for Topic-based Browsing

Existing video search engines return a ranked list of videos for each user query, which is not convenient for browsing the results of query topics that have multiple facets, such as the “early life,” “personal life,” and “presidency” of a query “Barack Obama.” ...

IEEE Transactions on Multimedia · 2016

Stochastic Gradient Made Stable: A Manifold Propagation Approach for Large-Scale Optimization

Stochastic gradient descent (SGD) holds as a classical method to build large scale machine learning models over big data. A stochastic gradient is typically calculated from a limited number of samples (known as mini-batch), so it potentially incurs a high variance and causes the estimated parameters bounce around the optimal solution...

IEEE Transactions on Knowledge and Data Engineering · 2016

Scalable Mammogram Retrieval Using Composite Anchor Graph Hashing with Iterative Quantization

Content-Based Image Retrieval (CBIR) shows great significance in clinical decision-making, which explores the visual content of medical images rather than keywords, tags, or descriptions. It provides doctors an image-guided approach to explore relevant cases that could offer doctors instructive reference...

IEEE Transactions on Circuits and Systems for Video Technology · 2016

Efficient Multi-Class Selective Sampling on Graphs

A graph-based multi-class classification problem is typically converted into a collection of binary classification tasks via the one-vs.-all strategy, and then tackled by applying proper binary classification algorithms. Unlike the onevs.-all strategy, we suggest a unified framework which operates directly on the multi-class problem without reducing it to a collection of binary tasks...

Conference on Uncertainty in Artificial Intelligence (UAI) · 2016

On Stochastic Primal-Dual Hybrid Gradient Approach for Compositely Regularized Minimization

We consider a wide spectrum of regularized stochastic minimization problems, where the regularization term is composite with a linear function. Examples of this formulation include graph-guided regularized minimization, generalized Lasso and a class of l_1 regularized problems...

European Conference on Artificial Intelligence (ECAI) · 2016

No-Reference Retargeted Image Quality Assessment Based on Pairwise Rank Learning

In this paper, we propose a novel no-reference image quality assessment method for the retargeted image based on the pairwise rank learning approach. Each retargeted image needs to be first represented as a feature vector, which not only captures the image characteristics but also is sensitive to distortions during the retargeting process...

IEEE Transactions on Multimedia · 2016