• Corpus ID: 19751025

Deep Hashing with Category Mask for Fast Video Retrieval

@article{Liu2017DeepHW,
  title={Deep Hashing with Category Mask for Fast Video Retrieval},
  author={X. Liu and Lili Zhao and Dajun Ding and Yajiao Dong},
  journal={ArXiv},
  year={2017},
  volume={abs/1712.08315}
}
This paper proposes an end-to-end deep hashing framework with category mask for fast video retrieval. We train our network in a supervised way by fully exploiting inter-class diversity and intra-class identity. Classification loss is optimized to maximize inter-class diversity, while intra-pair is introduced to learn representative intra-class identity. We investigate the binary bits distribution related to categories and find out that the effectiveness of binary bits is highly related to… 

Figures and Tables from this paper

Deep Video Hashing Using 3DCNN with BERT

TLDR
A hashing model using two separated modules using a 3DCNN is proposed with a bidirectional encoder representations from transformers (BERT) layer and a hashing neural network (NN) module will learn to encode features into hash codes.

Feature Re-Learning with Data Augmentation for Video Relevance Prediction

Predicting the relevance between two given videos with respect to their visual content is a key component for content-based video recommendation and retrieval. Thanks to the increasing availability

Product Quantization Network for Fast Visual Search

TLDR
Comprehensive experiments conducted on multiple public benchmark datasets demonstrate the state-of-the-art performance of the proposed PQN, RPQN and TPQN in fast image and video retrieval.

Feature Re-Learning for Video Recommendation

  • C. Chanjal
  • Computer Science
    International Journal for Research in Applied Science and Engineering Technology
  • 2021
TLDR
This work focus on the visual contents to predict the relevance between two videos, using a standard triplet ranking loss that optimize the projection process by a novel negative-enhanced tripletranking loss and a data augmentation strategy which works directly on video features.

Convolutional Hashing for Automated Scene Matching

TLDR
A neural network is demonstrated by creating for the first time a neural network that outperforms state-of-the-art Haar wavelets and color layout descriptors at the task of automated scene matching by accurately relating distance on the manifold of network outputs to distance in Hamming space.

References

SHOWING 1-10 OF 62 REFERENCES

Deep Video Hashing

TLDR
This work fuse the temporal information across different frames within each video to learn the feature representation under two criteria: the distance between a feature pair obtained at the top layer is small if they are from the same class, and large if they is from different classes.

Unsupervised Deep Video Hashing with Balanced Rotation

TLDR
An end-to-end hashing framework, namely Unsupervised Deep Video Hashing (UDVH), is proposed, where feature extraction, balanced code learning and hash function learning are integrated and optimized in a self-taught manner.

Submodular video hashing: a unified framework towards video pooling and indexing

TLDR
A novel framework for efficient large-scale video retrieval that integrates feature pooling and hashing in a single framework, and shows that the influence maximization problem is submodular, which allows a greedy optimization method to achieve a nearly optimal solution.

Deep learning of binary hash codes for fast image retrieval

TLDR
This work proposes an effective deep learning framework to generate binary hash codes for fast image retrieval by employing a hidden layer for representing the latent concepts that dominate the class labels in convolutional neural networks.

Feature Learning Based Deep Supervised Hashing with Pairwise Labels

TLDR
Experiments show that the proposed deep pairwise-supervised hashing method (DPSH), to perform simultaneous feature learning and hashcode learning for applications with pairwise labels, can outperform other methods to achieve the state-of-the-art performance in image retrieval applications.

SuBiC: A Supervised, Structured Binary Code for Image Search

TLDR
This work proposes herein a novel method to make deep convolutional neural networks produce supervised, compact, structured binary codes for visual search that outperforms state-of-the-art compact representations based on deep hashing or structured quantization in single and cross-domain category retrieval, instance retrieval and classification.

Unsupervised t-Distributed Video Hashing and Its Deep Hashing Extension

TLDR
This paper develops a robust unsupervised training strategy for a deep hashing network by adapting the corresponding optimization objective and constructing the hash mapping function via a deep neural network.

Play and Rewind: Optimizing Binary Representations of Videos by Self-Supervised Temporal Hashing

TLDR
This paper proposes a novel unsupervised video hashing framework called Self-Supervised Temporal Hashing (SSTH) that is able to capture the temporal nature of videos in an end- to-end learning-to-hash fashion and develops a backpropagation rule that tackles the non-differentiability of BLSTM.

Simultaneous feature learning and hash coding with deep neural networks

TLDR
Extensive evaluations on several benchmark image datasets show that the proposed simultaneous feature learning and hash coding pipeline brings substantial improvements over other state-of-the-art supervised or unsupervised hashing methods.

Learning Deep Binary Descriptor with Multi-quantization

TLDR
An unsupervised feature learning method called deep binary descriptor with multi-quantization (DBD-MQ) for visual matching that applies a K-AutoEncoders (KAEs) network to jointly learn the parameters and the binarization functions under a deep learning framework so that discriminative binary descriptors can be obtained with a fine-grained multi- quantization.
...