82 research outputs found
Deep Lifelong Cross-modal Hashing
Hashing methods have made significant progress in cross-modal retrieval tasks
with fast query speed and low storage cost. Among them, deep learning-based
hashing achieves better performance on large-scale data due to its excellent
extraction and representation ability for nonlinear heterogeneous features.
However, there are still two main challenges in catastrophic forgetting when
data with new categories arrive continuously, and time-consuming for
non-continuous hashing retrieval to retrain for updating. To this end, we, in
this paper, propose a novel deep lifelong cross-modal hashing to achieve
lifelong hashing retrieval instead of re-training hash function repeatedly when
new data arrive. Specifically, we design lifelong learning strategy to update
hash functions by directly training the incremental data instead of retraining
new hash functions using all the accumulated data, which significantly reduce
training time. Then, we propose lifelong hashing loss to enable original hash
codes participate in lifelong learning but remain invariant, and further
preserve the similarity and dis-similarity among original and incremental hash
codes to maintain performance. Additionally, considering distribution
heterogeneity when new data arriving continuously, we introduce multi-label
semantic similarity to supervise hash learning, and it has been proven that the
similarity improves performance with detailed analysis. Experimental results on
benchmark datasets show that the proposed methods achieves comparative
performance comparing with recent state-of-the-art cross-modal hashing methods,
and it yields substantial average increments over 20\% in retrieval accuracy
and almost reduces over 80\% training time when new data arrives continuously
New ideas and trends in deep multimodal content understanding: a review
The focus of this survey is on the analysis of two modalities of multimodal deep learning: image and text. Unlike classic reviews of deep learning where monomodal image classifiers such as VGG, ResNet and Inception module are central topics, this paper will examine recent multimodal deep models and structures, including auto-encoders, generative adversarial nets and their variants. These models go beyond the simple image classifiers in which they can do uni-directional (e.g. image captioning, image generation) and bi-directional (e.g. cross-modal retrieval, visual question answering) multimodal tasks. Besides, we analyze two aspects of the challenge in terms of better content understanding in deep multimodal applications. We then introduce current ideas and trends in deep multimodal feature learning, such as feature embedding approaches and objective function design, which are crucial in overcoming the aforementioned challenges. Finally, we include several promising directions for future research.Computer Systems, Imagery and Medi
Learning effective binary representation with deep hashing technique for large-scale multimedia similarity search
The explosive growth of multimedia data in modern times inspires the research of performing an efficient large-scale multimedia similarity search in the existing information retrieval systems. In the past decades, the hashing-based nearest neighbor search methods draw extensive attention in this research field. By representing the original data with compact hash code, it enables the efficient similarity retrieval by only conducting bitwise operation when computing the Hamming distance. Moreover, less memory space is required to process and store the massive amounts of features for the search engines owing to the nature of compact binary code. These advantages make hashing a competitive option in large-scale visual-related retrieval tasks. Motivated by the previous dedicated works, this thesis focuses on learning compact binary representation via hashing techniques for the large-scale multimedia similarity search tasks. Particularly, several novel frameworks are proposed for popular hashing-based applications like a local binary descriptor for patch-level matching (Chapter 3), video-to-video retrieval (Chapter 4) and cross-modality retrieval (Chapter 5). This thesis starts by addressing the problem of learning local binary descriptor for better patch/image matching performance. To this end, we propose a novel local descriptor termed Unsupervised Deep Binary Descriptor (UDBD) for the patch-level matching tasks, which learns the transformation invariant binary descriptor via embedding the original visual data and their transformed sets into a common Hamming space. By imposing a l2,1-norm regularizer on the objective function, the learned binary descriptor gains robustness against noises. Moreover, a weak bit scheme is applied to address the ambiguous matching in the local binary descriptor, where the best match is determined for each query by comparing a series of weak bits between the query instance and the candidates, thus improving the matching performance. Furthermore, Unsupervised Deep Video Hashing (UDVH) is proposed to facilitate large-scale video-to-video retrieval. To tackle the imbalanced distribution issue in the video feature, balanced rotation is developed to identify a proper projection matrix such that the information of each dimension can be balanced in the fixed-bit quantization, thus improving the retrieval performance dramatically with better code quality. To provide comprehensive insights on the proposed rotation, two different video feature learning structures: stacked LSTM units (UDVH-LSTM) and Temporal Segment Network (UDVH-TSN) are presented in Chapter 4. Lastly, we extend the research topic from single-modality to cross-modality retrieval, where Self-Supervised Deep Multimodal Hashing (SSDMH) based on matrix factorization is proposed to learn unified binary code for different modalities directly without the need for relaxation. By minimizing graph regularization loss, it is prone to produce discriminative hash code via preserving the original data structure. Moreover, Binary Gradient Descent (BGD) accelerates the discrete optimization against the bit-by-bit fashion. Besides, an unsupervised version termed Unsupervised Deep Cross-Modal Hashing (UDCMH) is proposed to tackle the large-scale cross-modality retrieval when prior knowledge is unavailable
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