747 research outputs found
Feature Fusion Vision Transformer for Fine-Grained Visual Categorization
The core for tackling the fine-grained visual categorization (FGVC) is to
learn subtle yet discriminative features. Most previous works achieve this by
explicitly selecting the discriminative parts or integrating the attention
mechanism via CNN-based approaches.However, these methods enhance the
computational complexity and make the modeldominated by the regions containing
the most of the objects. Recently, vision trans-former (ViT) has achieved SOTA
performance on general image recognition tasks. Theself-attention mechanism
aggregates and weights the information from all patches to the classification
token, making it perfectly suitable for FGVC. Nonetheless, the classifi-cation
token in the deep layer pays more attention to the global information, lacking
the local and low-level features that are essential for FGVC. In this work, we
proposea novel pure transformer-based framework Feature Fusion Vision
Transformer (FFVT)where we aggregate the important tokens from each transformer
layer to compensate thelocal, low-level and middle-level information. We design
a novel token selection mod-ule called mutual attention weight selection (MAWS)
to guide the network effectively and efficiently towards selecting
discriminative tokens without introducing extra param-eters. We verify the
effectiveness of FFVT on three benchmarks where FFVT achieves the
state-of-the-art performance.Comment: 9 pages, 2 figures, 3 table
Optimal Control of a Nonlinear Time-Delay System in Batch Fermentation Process
The main control goal in batch process is to get a high yield of products. In this
paper, to maximize the yield of 1,3-propanediol (1,3-PD) in bioconversion of glycerol
to 1,3-PD, we consider an optimal control problem involving a nonlinear
time-delay system. The control variables in this problem include the initial concentrations
of biomass and glycerol and the terminal time of the batch process.
By a time-scaling transformation, we transcribe the optimal control problem into
a new one with fixed terminal time, which yields a new nonlinear system with
variable time-delay. The gradients of the cost and constraint functionals with
respect to the control variables are derived using the costate method. Then, a
gradient-based optimization method is developed to solve the optimal control
problem. Numerical results show that the yield of 1,3-PD at the terminal time
is increased considerably compared with the experimental data
Flow-Guided Diffusion for Video Inpainting
Video inpainting has been challenged by complex scenarios like large
movements and low-light conditions. Current methods, including emerging
diffusion models, face limitations in quality and efficiency. This paper
introduces the Flow-Guided Diffusion model for Video Inpainting (FGDVI), a
novel approach that significantly enhances temporal consistency and inpainting
quality via reusing an off-the-shelf image generation diffusion model. We
employ optical flow for precise one-step latent propagation and introduces a
model-agnostic flow-guided latent interpolation technique. This technique
expedites denoising, seamlessly integrating with any Video Diffusion Model
(VDM) without additional training. Our FGDVI demonstrates a remarkable 10%
improvement in flow warping error E_warp over existing state-of-the-art
methods. Our comprehensive experiments validate superior performance of FGDVI,
offering a promising direction for advanced video inpainting. The code and
detailed results will be publicly available in
https://github.com/NevSNev/FGDVI
SVD-based Method for Radio Frequency Interference Suppression Applied to SAR
Synthetic aperture radar (SAR) is a special type of active microwave sensor, which has got a wide range of applications in remote sensing. However, the performance of SAR systems may be affected by radio frequency interference (RFI) in several geographic regions. A novel singular value decomposition method is proposed for radio frequency interference suppression applied to SAR. This method decomposes the singular vectors of the received signal with RFI into interference subspace and signal subspace. The orthogonality of the two subspaces is used to suppress the RFI. The point-target simulation is used to show the working principle of the proposed algorithm. The experimental results based on SAR real data are also shown to verify the proposed algorithm.Defence Science Journal, 2012, 62(2), pp.132-136, DOI:http://dx.doi.org/10.14429/dsj.62.114
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