Image Description using Deep Neural Networks

Current research in computer vision and machine learning has demonstrated some great abilities at detecting and recognizing objects in natural images. Current state-of-the-art results in object detection, classification and localization in ImageNet Challenges have the validation accuracy for top 5 predictions for classification to be at 3.08% while similar classification experiments run by trained humans report an accuracy of 5.1%. While some people might argue that human accuracy is a function of training time it can be said with great confidence that automated classification models are at least as good as trained humans in classification problems. The ability of these models to analyze and describe complex images, however, is still an active area of research. Image description is a good starting point for imparting artificial intelligence to machines by allowing them to analyze and describe complex visual scenes. This thesis work introduces a generic end-to-end trainable Fusion-based Recurrent Multi-Modal (FRMM) architecture to address multi-modal applications. FRMM allows each input modality to be independent in terms of architecture, parameters and length of input sequences. FRMM image description models seamlessly blend convolutional neural network feature descriptors with sequential language data in a recurrent framework. In addition to introducing FRMMs, this work also analyzes the impact of varying activation functions and vocabulary size. For training and testing Flickr8k, Flickr30K and MSCOCO datasets have been used, demonstrating state-of-the-art description results

Learning Cross-Modal Deep Embeddings for Multi-Object Image Retrieval using Text and Sketch

In this work we introduce a cross modal image retrieval system that allows both text and sketch as input modalities for the query. A cross-modal deep network architecture is formulated to jointly model the sketch and text input modalities as well as the the image output modality, learning a common embedding between text and images and between sketches and images. In addition, an attention model is used to selectively focus the attention on the different objects of the image, allowing for retrieval with multiple objects in the query. Experiments show that the proposed method performs the best in both single and multiple object image retrieval in standard datasets.Comment: Accepted at ICPR 201

NTU RGB+D 120: A Large-Scale Benchmark for 3D Human Activity Understanding

Research on depth-based human activity analysis achieved outstanding performance and demonstrated the effectiveness of 3D representation for action recognition. The existing depth-based and RGB+D-based action recognition benchmarks have a number of limitations, including the lack of large-scale training samples, realistic number of distinct class categories, diversity in camera views, varied environmental conditions, and variety of human subjects. In this work, we introduce a large-scale dataset for RGB+D human action recognition, which is collected from 106 distinct subjects and contains more than 114 thousand video samples and 8 million frames. This dataset contains 120 different action classes including daily, mutual, and health-related activities. We evaluate the performance of a series of existing 3D activity analysis methods on this dataset, and show the advantage of applying deep learning methods for 3D-based human action recognition. Furthermore, we investigate a novel one-shot 3D activity recognition problem on our dataset, and a simple yet effective Action-Part Semantic Relevance-aware (APSR) framework is proposed for this task, which yields promising results for recognition of the novel action classes. We believe the introduction of this large-scale dataset will enable the community to apply, adapt, and develop various data-hungry learning techniques for depth-based and RGB+D-based human activity understanding. [The dataset is available at: http://rose1.ntu.edu.sg/Datasets/actionRecognition.asp]Comment: IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI

Multi-modal gated recurrent units for image description

Using a natural language sentence to describe the content of an image is a challenging but very important task. It is challenging because a description must not only capture objects contained in the image and the relationships among them, but also be relevant and grammatically correct. In this paper a multi-modal embedding model based on gated recurrent units (GRU) which can generate variable-length description for a given image. In the training step, we apply the convolutional neural network (CNN) to extract the image feature. Then the feature is imported into the multi-modal GRU as well as the corresponding sentence representations. The multi-modal GRU learns the inter-modal relations between image and sentence. And in the testing step, when an image is imported to our multi-modal GRU model, a sentence which describes the image content is generated. The experimental results demonstrate that our multi-modal GRU model obtains the state-of-the-art performance on Flickr8K, Flickr30K and MS COCO datasets.Comment: 25 pages, 7 figures, 6 tables, magazin