由于许多安全性系统(例如手术机器人和自动驾驶汽车)在不稳定的环境中运行,具有传感器噪声和不完整的数据,因此希望对象探测器将本地化不确定性考虑在内。但是,基于锚的对象检测的现有不确定性估计方法存在几个局限性。 1)它们对具有不同特征和尺度的异质对象性质的不确定性进行建模,例如位置(中心点)和尺度(宽度,高度),这可能很难估算。 2)它们将框偏移型为高斯分布,这与遵循Dirac Delta分布的地面真相边界框不兼容。 3)由于基于锚的方法对锚定超参数敏感,因此它们的定位不确定性也可能对选择超参数的选择高度敏感。为了应对这些局限性,我们提出了一种称为UAD的新定位不确定性估计方法,用于无锚对象检测。我们的方法捕获了均匀的四个方向(左,右,顶部,底部)的四个方向的不确定性,因此它可以判断哪个方向不确定,并在[0,1]中提供不确定性的定量值。为了实现这种不确定性估计,我们设计了一种新的不确定性损失,负功率对数可能性损失,以通过加权其IOU加权可能性损失来衡量本地化不确定性,从而减轻了模型错误指定问题。此外,我们提出了反映分类评分的估计不确定性的不确定性感知局灶性损失。可可数据集的实验结果表明,我们的方法在不牺牲计算效率的情况下显着提高了最高1.8点的FCO。
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Several leading methods on public benchmarks for depth-from-stereo rely on memory-demanding 4D cost volumes and computationally intensive 3D convolutions for feature matching. We suggest a new way to process the 4D cost volume where we merge two different concepts in one deeply integrated framework to achieve a symbiotic relationship. A feature matching part is responsible for identifying matching pixels pairs along the baseline while a concurrent image volume part is inspired by depth-from-mono CNNs. However, instead of predicting depth directly from image features, it provides additional context to resolve ambiguities during pixel matching. More technically, the processing of the 4D cost volume is separated into a 2D propagation and a 3D propagation part. Starting from feature maps of the left image, the 2D propagation assists the 3D propagation part of the cost volume at different layers by adding visual features to the geometric context. By combining both parts, we can safely reduce the scale of 3D convolution layers in the matching part without sacrificing accuracy. Experiments demonstrate that our end-to-end trained CNN is ranked 2nd on KITTI2012 and ETH3D benchmarks while being significantly faster than the 1st-ranked method. Furthermore, we notice that the coupling of image and matching-volume improves fine-scale details as demonstrated by our qualitative analysis.
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Uniform-precision neural network quantization has gained popularity since it simplifies densely packed arithmetic unit for high computing capability. However, it ignores heterogeneous sensitivity to the impact of quantization errors across the layers, resulting in sub-optimal inference accuracy. This work proposes a novel neural architecture search called neural channel expansion that adjusts the network structure to alleviate accuracy degradation from ultra-low uniform-precision quantization. The proposed method selectively expands channels for the quantization sensitive layers while satisfying hardware constraints (e.g., FLOPs, PARAMs). Based on in-depth analysis and experiments, we demonstrate that the proposed method can adapt several popular networks channels to achieve superior 2-bit quantization accuracy on CIFAR10 and ImageNet. In particular, we achieve the best-to-date Top-1/Top-5 accuracy for 2-bit ResNet50 with smaller FLOPs and the parameter size.
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For change detection in remote sensing, constructing a training dataset for deep learning models is difficult due to the requirements of bi-temporal supervision. To overcome this issue, single-temporal supervision which treats change labels as the difference of two semantic masks has been proposed. This novel method trains a change detector using two spatially unrelated images with corresponding semantic labels such as building. However, training on unpaired datasets could confuse the change detector in the case of pixels that are labeled unchanged but are visually significantly different. In order to maintain the visual similarity in unchanged area, in this paper, we emphasize that the change originates from the source image and show that manipulating the source image as an after-image is crucial to the performance of change detection. Extensive experiments demonstrate the importance of maintaining visual information between pre- and post-event images, and our method outperforms existing methods based on single-temporal supervision. code is available at https://github.com/seominseok0429/Self-Pair-for-Change-Detection.
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Many real-world applications of language models (LMs), such as code autocomplete and writing assistance, involve human-LM interaction, but the main LM benchmarks are non-interactive, where a system produces output without human intervention. To evaluate human-LM interaction, we develop a framework, Human-AI Language-based Interaction Evaluation (H-LINE), that expands non-interactive evaluation along three dimensions, capturing (i) the interactive process, not only the final output; (ii) the first-person subjective experience, not just a third-party assessment; and (iii) notions of preference beyond quality. We then design five tasks ranging from goal-oriented to open-ended to capture different forms of interaction. On four state-of-the-art LMs (three variants of OpenAI's GPT-3 and AI21's J1-Jumbo), we find that non-interactive performance does not always result in better human-LM interaction and that first-person and third-party metrics can diverge, suggesting the importance of examining the nuances of human-LM interaction.
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Cartoonization is a task that renders natural photos into cartoon styles. Previous deep cartoonization methods only have focused on end-to-end translation, which may hinder editability. Instead, we propose a novel solution with editing features of texture and color based on the cartoon creation process. To do that, we design a model architecture to have separate decoders, texture and color, to decouple these attributes. In the texture decoder, we propose a texture controller, which enables a user to control stroke style and abstraction to generate diverse cartoon textures. We also introduce an HSV color augmentation to induce the networks to generate diverse and controllable color translation. To the best of our knowledge, our work is the first deep approach to control the cartoonization at inference while showing profound quality improvement over to baselines.
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Brain-computer interface (BCI) uses brain signals to communicate with external devices without actual control. Particularly, BCI is one of the interfaces for controlling the robotic arm. In this study, we propose a knowledge distillation-based framework to manipulate robotic arm through hybrid paradigm induced EEG signals for practical use. The teacher model is designed to decode input data hierarchically and transfer knowledge to student model. To this end, soft labels and distillation loss functions are applied to the student model training. According to experimental results, student model achieved the best performance among the singular architecture-based methods. It is confirmed that using hierarchical models and knowledge distillation, the performance of a simple architecture can be improved. Since it is uncertain what knowledge is transferred, it is important to clarify this part in future studies.
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Score-based generative models are shown to achieve remarkable empirical performances in various applications such as image generation and audio synthesis. However, a theoretical understanding of score-based diffusion models is still incomplete. Recently, Song et al. showed that the training objective of score-based generative models is equivalent to minimizing the Kullback-Leibler divergence of the generated distribution from the data distribution. In this work, we show that score-based models also minimize the Wasserstein distance between them under suitable assumptions on the model. Specifically, we prove that the Wasserstein distance is upper bounded by the square root of the objective function up to multiplicative constants and a fixed constant offset. Our proof is based on a novel application of the theory of optimal transport, which can be of independent interest to the society. Our numerical experiments support our findings. By analyzing our upper bounds, we provide a few techniques to obtain tighter upper bounds.
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Emerging real-time multi-model ML (RTMM) workloads such as AR/VR and drone control often involve dynamic behaviors in various levels; task, model, and layers (or, ML operators) within a model. Such dynamic behaviors are new challenges to the system software in an ML system because the overall system load is unpredictable unlike traditional ML workloads. Also, the real-time processing requires to meet deadlines, and multi-model workloads involve highly heterogeneous models. As RTMM workloads often run on resource-constrained devices (e.g., VR headset), developing an effective scheduler is an important research problem. Therefore, we propose a new scheduler, SDRM3, that effectively handles various dynamicity in RTMM style workloads targeting multi-accelerator systems. To make scheduling decisions, SDRM3 quantifies the unique requirements for RTMM workloads and utilizes the quantified scores to drive scheduling decisions, considering the current system load and other inference jobs on different models and input frames. SDRM3 has tunable parameters that provide fast adaptivity to dynamic workload changes based on a gradient descent-like online optimization, which typically converges within five steps for new workloads. In addition, we also propose a method to exploit model level dynamicity based on Supernet for exploiting the trade-off between the scheduling effectiveness and model performance (e.g., accuracy), which dynamically selects a proper sub-network in a Supernet based on the system loads. In our evaluation on five realistic RTMM workload scenarios, SDRM3 reduces the overall UXCost, which is a energy-delay-product (EDP)-equivalent metric for real-time applications defined in the paper, by 37.7% and 53.2% on geometric mean (up to 97.6% and 97.1%) compared to state-of-the-art baselines, which shows the efficacy of our scheduling methodology.
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Vision Transformers (ViTs) have become a dominant paradigm for visual representation learning with self-attention operators. Although these operators provide flexibility to the model with their adjustable attention kernels, they suffer from inherent limitations: (1) the attention kernel is not discriminative enough, resulting in high redundancy of the ViT layers, and (2) the complexity in computation and memory is quadratic in the sequence length. In this paper, we propose a novel attention operator, called lightweight structure-aware attention (LiSA), which has a better representation power with log-linear complexity. Our operator learns structural patterns by using a set of relative position embeddings (RPEs). To achieve log-linear complexity, the RPEs are approximated with fast Fourier transforms. Our experiments and ablation studies demonstrate that ViTs based on the proposed operator outperform self-attention and other existing operators, achieving state-of-the-art results on ImageNet, and competitive results on other visual understanding benchmarks such as COCO and Something-Something-V2. The source code of our approach will be released online.
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