2,919 research outputs found
Analysis Dictionary Learning: An Efficient and Discriminative Solution
Discriminative Dictionary Learning (DL) methods have been widely advocated
for image classification problems. To further sharpen their discriminative
capabilities, most state-of-the-art DL methods have additional constraints
included in the learning stages. These various constraints, however, lead to
additional computational complexity. We hence propose an efficient
Discriminative Convolutional Analysis Dictionary Learning (DCADL) method, as a
lower cost Discriminative DL framework, to both characterize the image
structures and refine the interclass structure representations. The proposed
DCADL jointly learns a convolutional analysis dictionary and a universal
classifier, while greatly reducing the time complexity in both training and
testing phases, and achieving a competitive accuracy, thus demonstrating great
performance in many experiments with standard databases.Comment: ICASSP 201
Towards Fully Decoupled End-to-End Person Search
End-to-end person search aims to jointly detect and re-identify a target
person in raw scene images with a unified model. The detection task unifies all
persons while the re-id task discriminates different identities, resulting in
conflict optimal objectives. Existing works proposed to decouple end-to-end
person search to alleviate such conflict. Yet these methods are still
sub-optimal on one or two of the sub-tasks due to their partially decoupled
models, which limits the overall person search performance. In this paper, we
propose to fully decouple person search towards optimal person search. A
task-incremental person search network is proposed to incrementally construct
an end-to-end model for the detection and re-id sub-task, which decouples the
model architecture for the two sub-tasks. The proposed task-incremental network
allows task-incremental training for the two conflicting tasks. This enables
independent learning for different objectives thus fully decoupled the model
for persons earch. Comprehensive experimental evaluations demonstrate the
effectiveness of the proposed fully decoupled models for end-to-end person
search.Comment: DICTA 202
Few-shot Class-incremental Learning: A Survey
Few-shot Class-Incremental Learning (FSCIL) presents a unique challenge in
machine learning, as it necessitates the continuous learning of new classes
from sparse labeled training samples without forgetting previous knowledge.
While this field has seen recent progress, it remains an active area of
exploration. This paper aims to provide a comprehensive and systematic review
of FSCIL. In our in-depth examination, we delve into various facets of FSCIL,
encompassing the problem definition, the discussion of primary challenges of
unreliable empirical risk minimization and the stability-plasticity dilemma,
general schemes, and relevant problems of incremental learning and few-shot
learning. Besides, we offer an overview of benchmark datasets and evaluation
metrics. Furthermore, we introduce the classification methods in FSCIL from
data-based, structure-based, and optimization-based approaches and the object
detection methods in FSCIL from anchor-free and anchor-based approaches. Beyond
these, we illuminate several promising research directions within FSCIL that
merit further investigation
A Unified Object Counting Network with Object Occupation Prior
The counting task, which plays a fundamental role in numerous applications
(e.g., crowd counting, traffic statistics), aims to predict the number of
objects with various densities. Existing object counting tasks are designed for
a single object class. However, it is inevitable to encounter newly coming data
with new classes in our real world. We name this scenario as \textit{evolving
object counting}. In this paper, we build the first evolving object counting
dataset and propose a unified object counting network as the first attempt to
address this task. The proposed model consists of two key components: a
class-agnostic mask module and a class-incremental module. The class-agnostic
mask module learns generic object occupation prior via predicting a
class-agnostic binary mask (e.g., 1 denotes there exists an object at the
considering position in an image and 0 otherwise). The class-incremental module
is used to handle new coming classes and provides discriminative class guidance
for density map prediction. The combined outputs of class-agnostic mask module
and image feature extractor are used to predict the final density map. When new
classes come, we first add new neural nodes into the last regression and
classification layers of class-incremental module. Then, instead of retraining
the model from scratch, we utilize knowledge distillation to help the model
remember what have already learned about previous object classes. We also
employ a support sample bank to store a small number of typical training
samples of each class, which are used to prevent the model from forgetting key
information of old data. With this design, our model can efficiently and
effectively adapt to new coming classes while keeping good performance on
already seen data without large-scale retraining. Extensive experiments on the
collected dataset demonstrate the favorable performance.Comment: Under review; The dataset and code will be available at:
https://github.com/Tanyjiang/EOC
Enhancing Face Recognition with Deep Learning Architectures: A Comprehensive Review
The progression of information discernment via facial identification and the emergence of innovative frameworks has exhibited remarkable strides in recent years. This phenomenon has been particularly pronounced within the realm of verifying individual credentials, a practice prominently harnessed by law enforcement agencies to advance the field of forensic science. A multitude of scholarly endeavors have been dedicated to the application of deep learning techniques within machine learning models. These endeavors aim to facilitate the extraction of distinctive features and subsequent classification, thereby elevating the precision of unique individual recognition. In the context of this scholarly inquiry, the focal point resides in the exploration of deep learning methodologies tailored for the realm of facial recognition and its subsequent matching processes. This exploration centers on the augmentation of accuracy through the meticulous process of training models with expansive datasets. Within the confines of this research paper, a comprehensive survey is conducted, encompassing an array of diverse strategies utilized in facial recognition. This survey, in turn, delves into the intricacies and challenges that underlie the intricate field of facial recognition within imagery analysis
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