Semi-Supervised Semantic Segmentation via Marginal Contextual Information

We present a novel confidence refinement scheme that enhances pseudo-labels in semi-supervised semantic segmentation. Unlike current leading methods, which filter pixels with low-confidence predictions in isolation, our approach leverages the spatial correlation of labels in segmentation maps by grouping neighboring pixels and considering their pseudo-labels collectively. With this contextual information, our method, named S4MC, increases the amount of unlabeled data used during training while maintaining the quality of the pseudo-labels, all with negligible computational overhead. Through extensive experiments on standard benchmarks, we demonstrate that S4MC outperforms existing state-of-the-art semi-supervised learning approaches, offering a promising solution for reducing the cost of acquiring dense annotations. For example, S4MC achieves a 1.29 mIoU improvement over the prior state-of-the-art method on PASCAL VOC 12 with 366 annotated images. The code to reproduce our experiments is available at https://s4mcontext.github.io/

FBM: Fast-Bit Allocation for Mixed-Precision Quantization

Quantized neural networks are well known for reducing latency, power consumption, and model size without significant degradation in accuracy, making them highly applicable for systems with limited resources and low power requirements. Mixed precision quantization offers better utilization of customized hardware that supports arithmetic operations at different bitwidths. Existing mixed-precision schemes rely on having a high exploration space, resulting in a large carbon footprint. In addition, these bit allocation strategies mostly induce constraints on the model size rather than utilizing the performance of neural network deployment on specific hardware. Our work proposes Fast-Bit Allocation for Mixed-Precision Quantization (FBM), which finds an optimal bitwidth allocation by measuring desired behaviors through a simulation of a specific device, or even on a physical one. While dynamic transitions of bit allocation in mixed precision quantization with ultra-low bitwidth are known to suffer from performance degradation, we present a fast recovery solution from such transitions. A comprehensive evaluation of the proposed method on CIFAR-10 and ImageNet demonstrates our method's superiority over current state-of-the-art schemes in terms of the trade-off between neural network accuracy and hardware efficiency. Our source code, experimental settings and quantized models are available at https://github.com/RamorayDrake/FBM/

Bimodal Distributed Binarized Neural Networks

Binary Neural Networks (BNNs) are an extremely promising method to reduce deep neural networks' complexity and power consumption massively. Binarization techniques, however, suffer from ineligible performance degradation compared to their full-precision counterparts. Prior work mainly focused on strategies for sign function approximation during forward and backward phases to reduce the quantization error during the binarization process. In this work, we propose a Bi-Modal Distributed binarization method (\methodname{}). That imposes bi-modal distribution of the network weights by kurtosis regularization. The proposed method consists of a training scheme that we call Weight Distribution Mimicking (WDM), which efficiently imitates the full-precision network weight distribution to their binary counterpart. Preserving this distribution during binarization-aware training creates robust and informative binary feature maps and significantly reduces the generalization error of the BNN. Extensive evaluations on CIFAR-10 and ImageNet demonstrate the superiority of our method over current state-of-the-art schemes. Our source code, experimental settings, training logs, and binary models are available at \url{https://github.com/BlueAnon/BD-BNN}.