7 research outputs found
Hierarchical LSTM with Adjusted Temporal Attention for Video Captioning
Recent progress has been made in using attention based encoder-decoder
framework for video captioning. However, most existing decoders apply the
attention mechanism to every generated word including both visual words (e.g.,
"gun" and "shooting") and non-visual words (e.g. "the", "a"). However, these
non-visual words can be easily predicted using natural language model without
considering visual signals or attention. Imposing attention mechanism on
non-visual words could mislead and decrease the overall performance of video
captioning. To address this issue, we propose a hierarchical LSTM with adjusted
temporal attention (hLSTMat) approach for video captioning. Specifically, the
proposed framework utilizes the temporal attention for selecting specific
frames to predict the related words, while the adjusted temporal attention is
for deciding whether to depend on the visual information or the language
context information. Also, a hierarchical LSTMs is designed to simultaneously
consider both low-level visual information and high-level language context
information to support the video caption generation. To demonstrate the
effectiveness of our proposed framework, we test our method on two prevalent
datasets: MSVD and MSR-VTT, and experimental results show that our approach
outperforms the state-of-the-art methods on both two datasets
Deep Discrete Hashing with Self-supervised Pairwise Labels
Hashing methods have been widely used for applications of large-scale image
retrieval and classification. Non-deep hashing methods using handcrafted
features have been significantly outperformed by deep hashing methods due to
their better feature representation and end-to-end learning framework. However,
the most striking successes in deep hashing have mostly involved discriminative
models, which require labels. In this paper, we propose a novel unsupervised
deep hashing method, named Deep Discrete Hashing (DDH), for large-scale image
retrieval and classification. In the proposed framework, we address two main
problems: 1) how to directly learn discrete binary codes? 2) how to equip the
binary representation with the ability of accurate image retrieval and
classification in an unsupervised way? We resolve these problems by introducing
an intermediate variable and a loss function steering the learning process,
which is based on the neighborhood structure in the original space.
Experimental results on standard datasets (CIFAR-10, NUS-WIDE, and Oxford-17)
demonstrate that our DDH significantly outperforms existing hashing methods by
large margin in terms of~mAP for image retrieval and object recognition. Code
is available at \url{https://github.com/htconquer/ddh}
Feature based-Learning with Data Increasing for video Recommendation and Computing
Image content analysis is crucial for determining the reliability of a link between two videos. Video characteristics are increasingly being used in image and video representation as custom pre-trained picture and video convolutional neural networks become generally available. People also have limited access to video editing tools for a variety of reasons, such as ownership and privacy concerns. You don't need to go back to the source video data to use the refined features again. An affine transformation, for instance, can be used to map a well-studied function onto an unfamiliar domain. To do this, we use a unique triplet failure in conjunction with the re-learning strategy. We propose a contemporary data augmentation method that may be applied to functionality on various frames for videos as an alternative to employing specific motion data. Extensive testing on the well-known Hulu content-based Video Relevance challenge demonstrates the process's efficacy and provides solid evidence of state-of-the-art performance
Discrete Multi-modal Hashing with Canonical Views for Robust Mobile Landmark Search
Mobile landmark search (MLS) recently receives increasing attention for its
great practical values. However, it still remains unsolved due to two important
challenges. One is high bandwidth consumption of query transmission, and the
other is the huge visual variations of query images sent from mobile devices.
In this paper, we propose a novel hashing scheme, named as canonical view based
discrete multi-modal hashing (CV-DMH), to handle these problems via a novel
three-stage learning procedure. First, a submodular function is designed to
measure visual representativeness and redundancy of a view set. With it,
canonical views, which capture key visual appearances of landmark with limited
redundancy, are efficiently discovered with an iterative mining strategy.
Second, multi-modal sparse coding is applied to transform visual features from
multiple modalities into an intermediate representation. It can robustly and
adaptively characterize visual contents of varied landmark images with certain
canonical views. Finally, compact binary codes are learned on intermediate
representation within a tailored discrete binary embedding model which
preserves visual relations of images measured with canonical views and removes
the involved noises. In this part, we develop a new augmented Lagrangian
multiplier (ALM) based optimization method to directly solve the discrete
binary codes. We can not only explicitly deal with the discrete constraint, but
also consider the bit-uncorrelated constraint and balance constraint together.
Experiments on real world landmark datasets demonstrate the superior
performance of CV-DMH over several state-of-the-art methods
Optimized graph learning using partial tags and multiple features for image and video annotation
In multimedia annotation, due to the time constraints and the tediousness of manual tagging, it is quite common to utilize both tagged and untagged data to improve the performance of supervised learning when only limited tagged training data are available. This is often done by adding a geometry-based regularization term in the objective function of a supervised learning model. In this case, a similarity graph is indispensable to exploit the geometrical relationships among the training data points, and the graph construction scheme essentially determines the performance of these graph-based learning algorithms. However, most of the existing works construct the graph empirically and are usually based on a single feature without using the label information. In this paper, we propose a semi-supervised annotation approach by learning an optimized graph (OGL) from multi-cues (i.e., partial tags and multiple features), which can more accurately embed the relationships among the data points. Since OGL is a transductive method and cannot deal with novel data points, we further extend our model to address the out-of-sample issue. Extensive experiments on image and video annotation show the consistent superiority of OGL over the state-of-the-art methods