Active learning aims to reduce labeling costs by selecting only the most informative samples on a dataset. Few existing works have addressed active learning for object detection. Most of these methods are based on multiple models or are straightforward extensions of classification methods, hence estimate an image’s informativeness using only the classification head. In this paper, we propose a novel deep active learning approach for object detection. Our approach relies on mixture density networks that estimate a probabilistic distribution for each localization and classification head’s output. We explicitly estimate the aleatoric and epistemic uncertainty in a single forward pass of a single model. Our method uses a scoring function that aggregates these two types of uncertainties for both heads to obtain every image’s informativeness score. We demonstrate the efficacy of our approach in PASCAL VOC and MS-COCO datasets. Our approach outperforms single-model based methods and performs on par with multi-model based methods at a fraction of the computing cost. Code is available at https://github.com/NVlabs/AL-MDN.

The use of object detection algorithms is becoming increasingly important in autonomous vehicles, and object detection at high accuracy and a fast inference speed is essential for safe autonomous driving. A false positive (FP) from a false localization during autonomous driving can lead to fatal accidents and hinder safe and efficient driving. Therefore, a detection algorithm that can cope with mislocalizations is required in autonomous driving applications. This paper proposes a method for improving the detection accuracy while supporting a real-time operation by modeling the bounding box (bbox) of YOLOv3, which is the most representative of one-stage detectors, with a Gaussian parameter and redesigning the loss function. In addition, this paper proposes a method for predicting the localization uncertainty that indicates the reliability of bbox. By using the predicted localization uncertainty during the detection process, the proposed schemes can significantly reduce the FP and increase the true positive (TP), thereby improving the accuracy. Compared to a conventional YOLOv3, the proposed algorithm, Gaussian YOLOv3, improves the mean average precision (mAP) by 3.09 and 3.5 on the KITTI and Berkeley deep drive (BDD) datasets, respectively. Nevertheless, the proposed algorithm is capable of real-time detection at faster than 42 frames per second (fps) and shows a higher accuracy than previous approaches with a similar fps. Therefore, the proposed algorithm is the most suitable for autonomous driving applications.

In a memory system, understanding how the host is stressing the memory is important to improve memory performance. Accordingly, the need for the analysis of memory command trace, which the memory controller sends to the dynamic random access memory, has increased. However, the size of this trace is very large; consequently, a high-throughput hardware (HW) accelerator that can efficiently compress these data in real time is required. This paper proposes a high-throughput HW accelerator for lossless compression of the command trace. The proposed HW is designed in a pipeline structure to process Huffman tree generation, encoding, and stream merge. To avoid the HW cost increase owing to high-throughput processing, a Huffman tree is efficiently implemented by utilizing static random access memory-based queues and bitmaps. In addition, variable length stream merge is performed at a very low cost by reducing the HW wire width using the mathematical properties of Huffman coding and processing the metadata and the Huffman codeword using FIFO separately. Furthermore, to improve the compression efficiency of the DDR4 memory command, the proposed design includes two preprocessing operations, the “don't care bits override” and the “bits arrange,” which utilize the operating characteristics of DDR4 memory. The proposed compression architecture with such preprocessing operations achieves a high throughput of 8 GB/s with a compression ratio of 40.13% on average. Moreover, the total HW resource per throughput of the proposed architecture is superior to the previous implementations.