259 research outputs found
Taking a look at small-scale pedestrians and occluded pedestrians
Small-scale pedestrian detection and occluded pedestrian detection are two challenging tasks. However, most state-of-the-art methods merely handle one single task each time, thus giving rise to relatively poor performance when the two tasks, in practice, are required simultaneously. In this paper, it is found that small-scale pedestrian detection and occluded pedestrian detection actually have a common problem, i.e., an inaccurate location problem. Therefore, solving this problem enables to improve the performance of both tasks. To this end, we pay more attention to the predicted bounding box with worse location precision and extract more contextual information around objects, where two modules (i.e., location bootstrap and semantic transition) are proposed. The location bootstrap is used to reweight regression loss, where the loss of the predicted bounding box far from the corresponding ground-truth is upweighted and the loss of the predicted bounding box near the corresponding ground-truth is downweighted. Additionally, the semantic transition adds more contextual information and relieves semantic inconsistency of the skip-layer fusion. Since the location bootstrap is not used at the test stage and the semantic transition is lightweight, the proposed method does not add many extra computational costs during inference. Experiments on the challenging CityPersons and Caltech datasets show that the proposed method outperforms the state-of-the-art methods on the small-scale pedestrians and occluded pedestrians (e.g., 5.20% and 4.73% improvements on the Caltech)
On simplifying WINNER II channel model for MIMO OTA performance evaluation
The development of MIMO over-the-air (OTA) test methodology is an ongoing activity in 3GPP RAN4, CTIA and EU COST Action 2100. In this paper, the focus is on the anechoic chamber approach, which uses a uniform circular array of probe antennas to replicate directional channels for the device-under- test at the array center. In particular, we study in simulation the complexity requirements of implementing the WINNER II clustered delay line (CDL) model in RF using off-the-shelf RF components. Our results reveal that a significant reduction in complexity of the CDL model can be achieved, while keeping the change in both ergodic and outage capacities under 10% relative to the full model. This suggests that it is both feasible and cost effective in implementing state-of-the-art directional channel models in RF for MIMO OTA performance evaluations
Efficient Personalized Federated Learning via Sparse Model-Adaptation
Federated Learning (FL) aims to train machine learning models for multiple
clients without sharing their own private data. Due to the heterogeneity of
clients' local data distribution, recent studies explore the personalized FL
that learns and deploys distinct local models with the help of auxiliary global
models. However, the clients can be heterogeneous in terms of not only local
data distribution, but also their computation and communication resources. The
capacity and efficiency of personalized models are restricted by the
lowest-resource clients, leading to sub-optimal performance and limited
practicality of personalized FL. To overcome these challenges, we propose a
novel approach named pFedGate for efficient personalized FL by adaptively and
efficiently learning sparse local models. With a lightweight trainable gating
layer, pFedGate enables clients to reach their full potential in model capacity
by generating different sparse models accounting for both the heterogeneous
data distributions and resource constraints. Meanwhile, the computation and
communication efficiency are both improved thanks to the adaptability between
the model sparsity and clients' resources. Further, we theoretically show that
the proposed pFedGate has superior complexity with guaranteed convergence and
generalization error. Extensive experiments show that pFedGate achieves
superior global accuracy, individual accuracy and efficiency simultaneously
over state-of-the-art methods. We also demonstrate that pFedGate performs
better than competitors in the novel clients participation and partial clients
participation scenarios, and can learn meaningful sparse local models adapted
to different data distributions.Comment: Accepted to ICML 202
Make Transformer Great Again for Time Series Forecasting: Channel Aligned Robust Dual Transformer
Recent studies have demonstrated the great power of deep learning methods,
particularly Transformer and MLP, for time series forecasting. Despite its
success in NLP and CV, many studies found that Transformer is less effective
than MLP for time series forecasting. In this work, we design a special
Transformer, i.e., channel-aligned robust dual Transformer (CARD for short),
that addresses key shortcomings of Transformer in time series forecasting.
First, CARD introduces a dual Transformer structure that allows it to capture
both temporal correlations among signals and dynamical dependence among
multiple variables over time. Second, we introduce a robust loss function for
time series forecasting to alleviate the potential overfitting issue. This new
loss function weights the importance of forecasting over a finite horizon based
on prediction uncertainties. Our evaluation of multiple long-term and
short-term forecasting datasets demonstrates that CARD significantly
outperforms state-of-the-art time series forecasting methods, including both
Transformer and MLP-based models
- …