110 research outputs found
Support Neighbor Loss for Person Re-Identification
Person re-identification (re-ID) has recently been tremendously boosted due
to the advancement of deep convolutional neural networks (CNN). The majority of
deep re-ID methods focus on designing new CNN architectures, while less
attention is paid on investigating the loss functions. Verification loss and
identification loss are two types of losses widely used to train various deep
re-ID models, both of which however have limitations. Verification loss guides
the networks to generate feature embeddings of which the intra-class variance
is decreased while the inter-class ones is enlarged. However, training networks
with verification loss tends to be of slow convergence and unstable performance
when the number of training samples is large. On the other hand, identification
loss has good separating and scalable property. But its neglect to explicitly
reduce the intra-class variance limits its performance on re-ID, because the
same person may have significant appearance disparity across different camera
views. To avoid the limitations of the two types of losses, we propose a new
loss, called support neighbor (SN) loss. Rather than being derived from data
sample pairs or triplets, SN loss is calculated based on the positive and
negative support neighbor sets of each anchor sample, which contain more
valuable contextual information and neighborhood structure that are beneficial
for more stable performance. To ensure scalability and separability, a
softmax-like function is formulated to push apart the positive and negative
support sets. To reduce intra-class variance, the distance between the anchor's
nearest positive neighbor and furthest positive sample is penalized.
Integrating SN loss on top of Resnet50, superior re-ID results to the
state-of-the-art ones are obtained on several widely used datasets.Comment: Accepted by ACM Multimedia (ACM MM) 201
Bi-Directional Generation for Unsupervised Domain Adaptation
Unsupervised domain adaptation facilitates the unlabeled target domain
relying on well-established source domain information. The conventional methods
forcefully reducing the domain discrepancy in the latent space will result in
the destruction of intrinsic data structure. To balance the mitigation of
domain gap and the preservation of the inherent structure, we propose a
Bi-Directional Generation domain adaptation model with consistent classifiers
interpolating two intermediate domains to bridge source and target domains.
Specifically, two cross-domain generators are employed to synthesize one domain
conditioned on the other. The performance of our proposed method can be further
enhanced by the consistent classifiers and the cross-domain alignment
constraints. We also design two classifiers which are jointly optimized to
maximize the consistency on target sample prediction. Extensive experiments
verify that our proposed model outperforms the state-of-the-art on standard
cross domain visual benchmarks.Comment: 9 pages, 4 figure
Mining Label Distribution Drift in Unsupervised Domain Adaptation
Unsupervised domain adaptation targets to transfer task knowledge from
labeled source domain to related yet unlabeled target domain, and is catching
extensive interests from academic and industrial areas. Although tremendous
efforts along this direction have been made to minimize the domain divergence,
unfortunately, most of existing methods only manage part of the picture by
aligning feature representations from different domains. Beyond the discrepancy
in feature space, the gap between known source label and unknown target label
distribution, recognized as label distribution drift, is another crucial factor
raising domain divergence, and has not been paid enough attention and well
explored. From this point, in this paper, we first experimentally reveal how
label distribution drift brings negative effects on current domain adaptation
methods. Next, we propose Label distribution Matching Domain Adversarial
Network (LMDAN) to handle data distribution shift and label distribution drift
jointly. In LMDAN, label distribution drift problem is addressed by the
proposed source samples weighting strategy, which select samples to contribute
to positive adaptation and avoid negative effects brought by the mismatched in
label distribution. Finally, different from general domain adaptation
experiments, we modify domain adaptation datasets to create the considerable
label distribution drift between source and target domain. Numerical results
and empirical model analysis show that LMDAN delivers superior performance
compared to other state-of-the-art domain adaptation methods under such
scenarios
Can Domain Adaptation Improve Accuracy and Fairness of Skin Lesion Classification?
Deep learning-based diagnostic system has demonstrated potential in
classifying skin cancer conditions when labeled training example are abundant.
However, skin lesion analysis often suffers from a scarcity of labeled data,
hindering the development of an accurate and reliable diagnostic system. In
this work, we leverage multiple skin lesion datasets and investigate the
feasibility of various unsupervised domain adaptation (UDA) methods in binary
and multi-class skin lesion classification. In particular, we assess three UDA
training schemes: single-, combined-, and multi-source. Our experiment results
show that UDA is effective in binary classification, with further improvement
being observed when imbalance is mitigated. In multi-class task, its
performance is less prominent, and imbalance problem again needs to be
addressed to achieve above-baseline accuracy. Through our quantitative
analysis, we find that the test error of multi-class tasks is strongly
correlated with label shift, and feature-level UDA methods have limitations
when handling imbalanced datasets. Finally, our study reveals that UDA can
effectively reduce bias against minority groups and promote fairness, even
without the explicit use of fairness-focused techniques
Development of Automated Incident Detection System Using Existing ATMS CCTV
Indiana Department of Transportation (INDOT) has over 300 digital cameras along highways in populated areas in Indiana. These cameras are used to monitor traffic conditions around the clock, all year round. Currently, the videos from these cameras are observed by human operators. The main objective of this research is to develop an automatic real-time system to monitor traffic conditions using the INDOT CCTV video feeds by a collaborative research team of the Transportation Active Safety Institute (TASI) at Indiana University-Purdue University Indianapolis (IUPUI) and the Traffic Management Center (TMC) of INDOT.
In this project, the research team developed the system architecture based on a detailed system requirement analysis. The first prototype of major system components of the system has been implemented. Specifically, the team has successfully accomplished the following: An AI based deep learning algorithm provided in YOLO3 is selected for vehicle detection which generates the best results for daytime videos. The tracking information of moving vehicles is used to derive the locations of roads and lanes. A database is designed as the center place to gather and distribute the information generated from all camera videos. The database provides all information for the traffic incident detection. A web-based Graphical User Interface (GUI) was developed. The automatic traffic incident detection will be implemented after the traffic flow information being derived accurately.
The research team is currently in the process of integrating the prototypes of all components of the system together to establish a complete system prototype
- …