413 research outputs found
Towards Optimal Discrete Online Hashing with Balanced Similarity
When facing large-scale image datasets, online hashing serves as a promising
solution for online retrieval and prediction tasks. It encodes the online
streaming data into compact binary codes, and simultaneously updates the hash
functions to renew codes of the existing dataset. To this end, the existing
methods update hash functions solely based on the new data batch, without
investigating the correlation between such new data and the existing dataset.
In addition, existing works update the hash functions using a relaxation
process in its corresponding approximated continuous space. And it remains as
an open problem to directly apply discrete optimizations in online hashing. In
this paper, we propose a novel supervised online hashing method, termed
Balanced Similarity for Online Discrete Hashing (BSODH), to solve the above
problems in a unified framework. BSODH employs a well-designed hashing
algorithm to preserve the similarity between the streaming data and the
existing dataset via an asymmetric graph regularization. We further identify
the "data-imbalance" problem brought by the constructed asymmetric graph, which
restricts the application of discrete optimization in our problem. Therefore, a
novel balanced similarity is further proposed, which uses two equilibrium
factors to balance the similar and dissimilar weights and eventually enables
the usage of discrete optimizations. Extensive experiments conducted on three
widely-used benchmarks demonstrate the advantages of the proposed method over
the state-of-the-art methods.Comment: 8 pages, 11 figures, conferenc
Fast and Accurate Neural Word Segmentation for Chinese
Neural models with minimal feature engineering have achieved competitive
performance against traditional methods for the task of Chinese word
segmentation. However, both training and working procedures of the current
neural models are computationally inefficient. This paper presents a greedy
neural word segmenter with balanced word and character embedding inputs to
alleviate the existing drawbacks. Our segmenter is truly end-to-end, capable of
performing segmentation much faster and even more accurate than
state-of-the-art neural models on Chinese benchmark datasets.Comment: To appear in ACL201
Dynamic Prototype Mask for Occluded Person Re-Identification
Although person re-identification has achieved an impressive improvement in
recent years, the common occlusion case caused by different obstacles is still
an unsettled issue in real application scenarios. Existing methods mainly
address this issue by employing body clues provided by an extra network to
distinguish the visible part. Nevertheless, the inevitable domain gap between
the assistant model and the ReID datasets has highly increased the difficulty
to obtain an effective and efficient model. To escape from the extra
pre-trained networks and achieve an automatic alignment in an end-to-end
trainable network, we propose a novel Dynamic Prototype Mask (DPM) based on two
self-evident prior knowledge. Specifically, we first devise a Hierarchical Mask
Generator which utilizes the hierarchical semantic to select the visible
pattern space between the high-quality holistic prototype and the feature
representation of the occluded input image. Under this condition, the occluded
representation could be well aligned in a selected subspace spontaneously.
Then, to enrich the feature representation of the high-quality holistic
prototype and provide a more complete feature space, we introduce a Head Enrich
Module to encourage different heads to aggregate different patterns
representation in the whole image. Extensive experimental evaluations conducted
on occluded and holistic person re-identification benchmarks demonstrate the
superior performance of the DPM over the state-of-the-art methods. The code is
released at https://github.com/stone96123/DPM.Comment: Accepted by ACM MM 202
CAT:Collaborative Adversarial Training
Adversarial training can improve the robustness of neural networks. Previous
methods focus on a single adversarial training strategy and do not consider the
model property trained by different strategies. By revisiting the previous
methods, we find different adversarial training methods have distinct
robustness for sample instances. For example, a sample instance can be
correctly classified by a model trained using standard adversarial training
(AT) but not by a model trained using TRADES, and vice versa. Based on this
observation, we propose a collaborative adversarial training framework to
improve the robustness of neural networks. Specifically, we use different
adversarial training methods to train robust models and let models interact
with their knowledge during the training process. Collaborative Adversarial
Training (CAT) can improve both robustness and accuracy. Extensive experiments
on various networks and datasets validate the effectiveness of our method. CAT
achieves state-of-the-art adversarial robustness without using any additional
data on CIFAR-10 under the Auto-Attack benchmark. Code is available at
https://github.com/liuxingbin/CAT.Comment: Tech repor
Latent Feature Relation Consistency for Adversarial Robustness
Deep neural networks have been applied in many computer vision tasks and
achieved state-of-the-art performance. However, misclassification will occur
when DNN predicts adversarial examples which add human-imperceptible
adversarial noise to natural examples. This limits the application of DNN in
security-critical fields. To alleviate this problem, we first conducted an
empirical analysis of the latent features of both adversarial and natural
examples and found the similarity matrix of natural examples is more compact
than those of adversarial examples. Motivated by this observation, we propose
\textbf{L}atent \textbf{F}eature \textbf{R}elation \textbf{C}onsistency
(\textbf{LFRC}), which constrains the relation of adversarial examples in
latent space to be consistent with the natural examples. Importantly, our LFRC
is orthogonal to the previous method and can be easily combined with them to
achieve further improvement. To demonstrate the effectiveness of LFRC, we
conduct extensive experiments using different neural networks on benchmark
datasets. For instance, LFRC can bring 0.78\% further improvement compared to
AT, and 1.09\% improvement compared to TRADES, against AutoAttack on CIFAR10.
Code is available at https://github.com/liuxingbin/LFRC.Comment: Tech repor
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