The 3-D vision computing is a key application in unmanned systems, satellites, and planetary rovers. Learning-based light field (LF) depth estimation is one of the major research directions in 3-D vision computing. However, conventional learning-based depth estimation methods involve a large number of parameters and floating-point operations, making it challenging to achieve low-power, fast, and high-precision LF depth estimation on a field-programmable gate array (FPGA). Motivated by this issue, an FPGA-based low-bit, lightweight LF depth estimation network (L$^{3}\text {FNet}$) is proposed. First, a hardware-friendly network is designed, which has small weight parameters, low computational load, and a simple network architecture with minor accuracy loss. Second, we apply efficient hardware unit design and software-hardware collaborative dataflow architecture to construct an FPGA-based fast, low-bit acceleration engine. Experimental results show that compared with the state-of-the-art works with lower mean-square error (mse), L$^{3}\text {FNet}$can reduce the computational load by more than 109 times and weight parameters by approximately 78 times. Moreover, on the ZCU104 platform, it requires 95.65% lookup tables (LUTs), 80.67% digital signal processors (DSPs), 80.93% BlockRAM (BRAM), 58.52% LUTRAM, and 9.493-W power consumption to achieve an efficient acceleration engine with a latency as low as 272 ns. The code and model of the proposed method are available athttps://github.com/sansi-zhang/L3FNet.

Aspect-based sentiment classification (ABSC) is a crucial sub-task of fine-grained sentiment analysis, which aims to predict the sentiment polarity of the given aspects in a sentence as positive, negative, or neutral. Most existing ABSC methods are based on supervised learning. However, these methods rely heavily on fine-grained labeled training data, which can be scarce in low-resource domains, limiting their effectiveness. To overcome this challenge, we propose a low-resource cross-domain aspect-based sentiment classification (CDABSC) approach based on a pre-training and fine-tuning strategy. This approach applies the pre-training and fine-tuning strategy to an advanced deep learning method designed for ABSC, namely the attention-based encoding graph convolutional network (AEGCN) model. Specifically, a high-resource domain is selected as the source domain, and the AEGCN model is pre-trained using a large amount of fine-grained annotated data from the source domain. The optimal parameters of the model are preserved. Subsequently, a low-resource domain is used as the target domain, and the pre-trained model parameters are used as the initial parameters of the target domain model. The target domain is fine-tuned using a small amount of annotated data to adapt the parameters to the target domain model, improving the accuracy of sentiment classification in the low-resource domain. Finally, experimental validation on two domain benchmark datasets, restaurant and laptop, demonstrates significant outperformance of our approach over the baselines in CDABSC Micro-F1.