964 research outputs found
Deep Policy Hashing Network with Listwise Supervision
Deep-networks-based hashing has become a leading approach for large-scale
image retrieval, which learns a similarity-preserving network to map similar
images to nearby hash codes. The pairwise and triplet losses are two widely
used similarity preserving manners for deep hashing. These manners ignore the
fact that hashing is a prediction task on the list of binary codes. However,
learning deep hashing with listwise supervision is challenging in 1) how to
obtain the rank list of whole training set when the batch size of the deep
network is always small and 2) how to utilize the listwise supervision. In this
paper, we present a novel deep policy hashing architecture with two systems are
learned in parallel: a query network and a shared and slowly changing database
network. The following three steps are repeated until convergence: 1) the
database network encodes all training samples into binary codes to obtain a
whole rank list, 2) the query network is trained based on policy learning to
maximize a reward that indicates the performance of the whole ranking list of
binary codes, e.g., mean average precision (MAP), and 3) the database network
is updated as the query network. Extensive evaluations on several benchmark
datasets show that the proposed method brings substantial improvements over
state-of-the-art hashing methods.Comment: 8 pages, accepted by ACM ICM
Targeted Attack for Deep Hashing based Retrieval
The deep hashing based retrieval method is widely adopted in large-scale
image and video retrieval. However, there is little investigation on its
security. In this paper, we propose a novel method, dubbed deep hashing
targeted attack (DHTA), to study the targeted attack on such retrieval.
Specifically, we first formulate the targeted attack as a point-to-set
optimization, which minimizes the average distance between the hash code of an
adversarial example and those of a set of objects with the target label. Then
we design a novel component-voting scheme to obtain an anchor code as the
representative of the set of hash codes of objects with the target label, whose
optimality guarantee is also theoretically derived. To balance the performance
and perceptibility, we propose to minimize the Hamming distance between the
hash code of the adversarial example and the anchor code under the
restriction on the perturbation. Extensive experiments verify
that DHTA is effective in attacking both deep hashing based image retrieval and
video retrieval.Comment: Accepted by ECCV 2020 as Ora
Snap and Find: Deep Discrete Cross-domain Garment Image Retrieval
With the increasing number of online stores, there is a pressing need for
intelligent search systems to understand the item photos snapped by customers
and search against large-scale product databases to find their desired items.
However, it is challenging for conventional retrieval systems to match up the
item photos captured by customers and the ones officially released by stores,
especially for garment images. To bridge the customer- and store- provided
garment photos, existing studies have been widely exploiting the clothing
attributes (\textit{e.g.,} black) and landmarks (\textit{e.g.,} collar) to
learn a common embedding space for garment representations. Unfortunately they
omit the sequential correlation of attributes and consume large quantity of
human labors to label the landmarks. In this paper, we propose a deep
multi-task cross-domain hashing termed \textit{DMCH}, in which cross-domain
embedding and sequential attribute learning are modeled simultaneously.
Sequential attribute learning not only provides the semantic guidance for
embedding, but also generates rich attention on discriminative local details
(\textit{e.g.,} black buttons) of clothing items without requiring extra
landmark labels. This leads to promising performance and 306 boost on
efficiency when compared with the state-of-the-art models, which is
demonstrated through rigorous experiments on two public fashion datasets
Learning Large Euclidean Margin for Sketch-based Image Retrieval
This paper addresses the problem of Sketch-Based Image Retrieval (SBIR), for
which bridge the gap between the data representations of sketch images and
photo images is considered as the key. Previous works mostly focus on learning
a feature space to minimize intra-class distances for both sketches and photos.
In contrast, we propose a novel loss function, named Euclidean Margin Softmax
(EMS), that not only minimizes intra-class distances but also maximizes
inter-class distances simultaneously. It enables us to learn a feature space
with high discriminability, leading to highly accurate retrieval. In addition,
this loss function is applied to a conditional network architecture, which
could incorporate the prior knowledge of whether a sample is a sketch or a
photo. We show that the conditional information can be conveniently
incorporated to the recently proposed Squeeze and Excitation (SE) module, lead
to a conditional SE (CSE) module. Extensive experiments are conducted on two
widely used SBIR benchmark datasets. Our approach, although being very simple,
achieved new state-of-the-art on both datasets, surpassing existing methods by
a large margin.Comment: 13 pages, 6 figure
Deep Multi-Index Hashing for Person Re-Identification
Traditional person re-identification (ReID) methods typically represent
person images as real-valued features, which makes ReID inefficient when the
gallery set is extremely large. Recently, some hashing methods have been
proposed to make ReID more efficient. However, these hashing methods will
deteriorate the accuracy in general, and the efficiency of them is still not
high enough. In this paper, we propose a novel hashing method, called deep
multi-index hashing (DMIH), to improve both efficiency and accuracy for ReID.
DMIH seamlessly integrates multi-index hashing and multi-branch based networks
into the same framework. Furthermore, a novel block-wise multi-index hashing
table construction approach and a search-aware multi-index (SAMI) loss are
proposed in DMIH to improve the search efficiency. Experiments on three widely
used datasets show that DMIH can outperform other state-of-the-art baselines,
including both hashing methods and real-valued methods, in terms of both
efficiency and accuracy.Comment: 10 pages, 6 figures, 2 table
Semantic Cluster Unary Loss for Efficient Deep Hashing
Hashing method maps similar data to binary hashcodes with smaller hamming
distance, which has received a broad attention due to its low storage cost and
fast retrieval speed. With the rapid development of deep learning, deep hashing
methods have achieved promising results in efficient information retrieval.
Most of the existing deep hashing methods adopt pairwise or triplet losses to
deal with similarities underlying the data, but the training is difficult and
less efficient because data pairs and triplets are involved.
To address these issues, we propose a novel deep hashing algorithm with unary
loss which can be trained very efficiently. We first of all introduce a Unary
Upper Bound of the traditional triplet loss, thus reducing the complexity to
and bridging the classification-based unary loss and the triplet loss.
Second, we propose a novel Semantic Cluster Deep Hashing (SCDH) algorithm by
introducing a modified Unary Upper Bound loss, named Semantic Cluster Unary
Loss (SCUL). The resultant hashcodes form several compact clusters, which means
hashcodes in the same cluster have similar semantic information. We also
demonstrate that the proposed SCDH is easy to be extended to semi-supervised
settings by incorporating the state-of-the-art semi-supervised learning
algorithms. Experiments on large-scale datasets show that the proposed method
is superior to state-of-the-art hashing algorithms.Comment: 13 page
Reconfigurable Hardware Accelerators: Opportunities, Trends, and Challenges
With the emerging big data applications of Machine Learning, Speech
Recognition, Artificial Intelligence, and DNA Sequencing in recent years,
computer architecture research communities are facing the explosive scale of
various data explosion. To achieve high efficiency of data-intensive computing,
studies of heterogeneous accelerators which focus on latest applications, have
become a hot issue in computer architecture domain. At present, the
implementation of heterogeneous accelerators mainly relies on heterogeneous
computing units such as Application-specific Integrated Circuit (ASIC),
Graphics Processing Unit (GPU), and Field Programmable Gate Array (FPGA). Among
the typical heterogeneous architectures above, FPGA-based reconfigurable
accelerators have two merits as follows: First, FPGA architecture contains a
large number of reconfigurable circuits, which satisfy requirements of high
performance and low power consumption when specific applications are running.
Second, the reconfigurable architectures of employing FPGA performs prototype
systems rapidly and features excellent customizability and reconfigurability.
Nowadays, in top-tier conferences of computer architecture, emerging a batch of
accelerating works based on FPGA or other reconfigurable architectures. To
better review the related work of reconfigurable computing accelerators
recently, this survey reserves latest high-level research products of
reconfigurable accelerator architectures and algorithm applications as the
basis. In this survey, we compare hot research issues and concern domains,
furthermore, analyze and illuminate advantages, disadvantages, and challenges
of reconfigurable accelerators. In the end, we prospect the development
tendency of accelerator architectures in the future, hoping to provide a
reference for computer architecture researchers
DistillHash: Unsupervised Deep Hashing by Distilling Data Pairs
Due to the high storage and search efficiency, hashing has become prevalent
for large-scale similarity search. Particularly, deep hashing methods have
greatly improved the search performance under supervised scenarios. In
contrast, unsupervised deep hashing models can hardly achieve satisfactory
performance due to the lack of reliable supervisory similarity signals. To
address this issue, we propose a novel deep unsupervised hashing model, dubbed
DistillHash, which can learn a distilled data set consisted of data pairs,
which have confidence similarity signals. Specifically, we investigate the
relationship between the initial noisy similarity signals learned from local
structures and the semantic similarity labels assigned by a Bayes optimal
classifier. We show that under a mild assumption, some data pairs, of which
labels are consistent with those assigned by the Bayes optimal classifier, can
be potentially distilled. Inspired by this fact, we design a simple yet
effective strategy to distill data pairs automatically and further adopt a
Bayesian learning framework to learn hash functions from the distilled data
set. Extensive experimental results on three widely used benchmark datasets
show that the proposed DistillHash consistently accomplishes the
state-of-the-art search performance
Apprentice: Using Knowledge Distillation Techniques To Improve Low-Precision Network Accuracy
Deep learning networks have achieved state-of-the-art accuracies on computer
vision workloads like image classification and object detection. The performant
systems, however, typically involve big models with numerous parameters. Once
trained, a challenging aspect for such top performing models is deployment on
resource constrained inference systems - the models (often deep networks or
wide networks or both) are compute and memory intensive. Low-precision numerics
and model compression using knowledge distillation are popular techniques to
lower both the compute requirements and memory footprint of these deployed
models. In this paper, we study the combination of these two techniques and
show that the performance of low-precision networks can be significantly
improved by using knowledge distillation techniques. Our approach, Apprentice,
achieves state-of-the-art accuracies using ternary precision and 4-bit
precision for variants of ResNet architecture on ImageNet dataset. We present
three schemes using which one can apply knowledge distillation techniques to
various stages of the train-and-deploy pipeline
Semantic-Aware Knowledge Preservation for Zero-Shot Sketch-Based Image Retrieval
Sketch-based image retrieval (SBIR) is widely recognized as an important
vision problem which implies a wide range of real-world applications. Recently,
research interests arise in solving this problem under the more realistic and
challenging setting of zero-shot learning. In this paper, we investigate this
problem from the viewpoint of domain adaptation which we show is critical in
improving feature embedding in the zero-shot scenario. Based on a framework
which starts with a pre-trained model on ImageNet and fine-tunes it on the
training set of SBIR benchmark, we advocate the importance of preserving
previously acquired knowledge, e.g., the rich discriminative features learned
from ImageNet, to improve the model's transfer ability. For this purpose, we
design an approach named Semantic-Aware Knowledge prEservation (SAKE), which
fine-tunes the pre-trained model in an economical way and leverages semantic
information, e.g., inter-class relationship, to achieve the goal of knowledge
preservation. Zero-shot experiments on two extended SBIR datasets, TU-Berlin
and Sketchy, verify the superior performance of our approach. Extensive
diagnostic experiments validate that knowledge preserved benefits SBIR in
zero-shot settings, as a large fraction of the performance gain is from the
more properly structured feature embedding for photo images. Code is available
at: https://github.com/qliu24/SAKE.Comment: To appear in ICCV 201
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