270 research outputs found
Planar Object Tracking in the Wild: A Benchmark
Planar object tracking is an actively studied problem in vision-based robotic
applications. While several benchmarks have been constructed for evaluating
state-of-the-art algorithms, there is a lack of video sequences captured in the
wild rather than in constrained laboratory environment. In this paper, we
present a carefully designed planar object tracking benchmark containing 210
videos of 30 planar objects sampled in the natural environment. In particular,
for each object, we shoot seven videos involving various challenging factors,
namely scale change, rotation, perspective distortion, motion blur, occlusion,
out-of-view, and unconstrained. The ground truth is carefully annotated
semi-manually to ensure the quality. Moreover, eleven state-of-the-art
algorithms are evaluated on the benchmark using two evaluation metrics, with
detailed analysis provided for the evaluation results. We expect the proposed
benchmark to benefit future studies on planar object tracking.Comment: Accepted by ICRA 201
Distilling Causal Effect from Miscellaneous Other-Class for Continual Named Entity Recognition
Continual Learning for Named Entity Recognition (CL-NER) aims to learn a
growing number of entity types over time from a stream of data. However, simply
learning Other-Class in the same way as new entity types amplifies the
catastrophic forgetting and leads to a substantial performance drop. The main
cause behind this is that Other-Class samples usually contain old entity types,
and the old knowledge in these Other-Class samples is not preserved properly.
Thanks to the causal inference, we identify that the forgetting is caused by
the missing causal effect from the old data. To this end, we propose a unified
causal framework to retrieve the causality from both new entity types and
Other-Class. Furthermore, we apply curriculum learning to mitigate the impact
of label noise and introduce a self-adaptive weight for balancing the causal
effects between new entity types and Other-Class. Experimental results on three
benchmark datasets show that our method outperforms the state-of-the-art method
by a large margin. Moreover, our method can be combined with the existing
state-of-the-art methods to improve the performance in CL-NERComment: Accepted by EMNLP202
Association study of C-reactive protein associated gene HNF1A with ischemic stroke in Chinese population
Service differentiation in OFDM-Based IEEE 802.16 networks
IEEE 802.16 network is widely viewed as a strong candidate solution for broadband wireless access systems. Various flexible mechanisms related to QoS provisioning have been specified for uplink traffic at the medium access control (MAC) layer in the standards. Among the mechanisms, bandwidth request scheme can be used to indicate and request bandwidth demands to the base station for different services. Due to the diverse QoS requirements of the applications, service differentiation (SD) is desirable for the bandwidth request scheme. In this paper, we propose several SD approaches. The approaches are based on the contention-based bandwidth request scheme and achieved by the means of assigning different channel access parameters and/or bandwidth allocation priorities to different services. Additionally, we propose effective analytical model to study the impacts of the SD approaches, which can be used for the configuration and optimization of the SD services. It is observed from simulations that the analytical model has high accuracy. Service can be efficiently differentiated with initial backoff window in terms of throughput and channel access delay. Moreover, the service differentiation can be improved if combined with the bandwidth allocation priority approach without adverse impacts on the overall system throughput
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