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Dense Contrastive Loss for Instance Segmentation 33

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Dense Contrastive Loss for Instance Segmentation

33rd British Machine Vision Conference Proceedings, BMVC 2022

作者： Chen, Hang Tang, Chufeng Hu, Xiaolin Depart. of Comp. Sci. & Tech. State Key Lab of Intell. Tech. & Sys. THU-Bosch JCML Center BNRist Institute for AI THBI IDG/McGovern Institute for Brain Research Tsinghua University Beijing China Beijing China

Instance segmentation, which requires instance-level mask prediction, is a fundamental task in computer vision. Many methods have been proposed in this field. However, the existing methods still do not perform well in complex scenarios such as occlusion. In this work, we analyzed the segmentation errors of some typical instance segmentation models. We found that false negatives (i.e. misclassification of foreground pixels as background) accounted for the majority of errors. It can be attributed to the inconsistent features of the same instance under complex scenarios. To address this problem, we proposed a dense contrastive loss to encourage the segmentation network to learn more consistent feature representations. Specifically, features on the same instance are pulled closer, while features on different instances and features between instances and the background are pushed farther apart. Without introducing any extra inference cost, the proposed method mitigated false-negative errors and achieved significant improvements on the Cityscapes and MS-COCO datasets. Code will be available at https://***/tinyalpha/DCL. © 2022. The copyright of this document resides with its authors. It may be distributed unchanged freely in print or electronic forms.

关键词： Computer vision

Master Lane Change Decision-Making by Integrating Imitation Learning with Expert Rules

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Master Lane Change Decision-Making by Integrating Imitation ...

International Conference on Computer and Automation Engineering, ICCAE

作者： Yangang Ren Jun Li Fatao Zhou Bolin Gao Fenghua Liang Keqiang Li State Key Lab of Intell. Vehicle Safety Tech. Chongqing Changan Automobile Co. Ltd. Chongqing China School of Vehicle and Mobility Tsinghua University Beijing China

ISBN: (数字)9798331533816

ISBN: (纸本)9798331533823

Imitation learning has been extensively explored in the decision-making of intell.gent vehicles, showing a promising human-like driving performance. However, it suffers significant safety challenges when further implemented in industry due to the lack of transparency. Therefore, expert systems currently dominate in the decision module of driving software, whereas the predefined rules inevitably lead to rigid driving behaviors. To benefit from both methods, in this paper we propose to learn the practical lane change decision making by integrating the imitation learning with expert rules. A lightweight Transformer network is firstly designed to extract the comprehensive interaction between dynamic agents and road elements, generating the normalized probability for lane keeping, left lane-change and right lane-change. This network is trained to predict the next-step behavior by imitation learning wherein a massive dataset of drivers is collected and annotated to provide the ground truth. Then the expert system checks the multiple outputs of the network within a sliding window to determine whether to initiate or cancel a lane change. With the rule-based state machine attached to the network, the unsafe decisions can be efficiently eliminated by road regulations and collision avoidance. Experiments show that our method can effectively capture experience from human data, automatically identifying suitable lane change opportunities. It can improve the driving speed by 16.15% compared with the expert system with respect to overtake and off-ramp scenarios. Our work provides a more trustworthy and intell.gent driving system for practical vehicles.

关键词： Imitation learning Roads Decision making Transformers Software Regulation Safety Planning Expert systems Vehicle dynamics

DAB-DETR: DYNAMIC ANCHOR BOXES ARE BETTER QUERIES FOR DETR 10

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DAB-DETR: DYNAMIC ANCHOR BOXES ARE BETTER QUERIES FOR DETR

10th International Conference on Learning Representations, ICLR 2022

作者： Liu, Shilong Li, Feng Zhang, Hao Yang, Xiao Qi, Xianbiao Su, Hang Zhu, Jun Zhang, Lei Dept. of Comp. Sci. and Tech. BNRist Center State Key Lab for Intell. Tech. & Sys. Institute for AI Tsinghua-Bosch Joint Center for ML Tsinghua University China China Hong Kong University of Science and Technology Hong Kong Peng Cheng Laboratory Guangdong Shenzhen China

We present in this paper a novel query formulation using dynamic anchor boxes for DETR (DEtection TRansformer) and offer a deeper understanding of the role of queries in DETR. This new formulation directly uses box coordinates as queries in Transformer decoders and dynamically updates them layer by layer. Using box coordinates not only helps using explicit positional priors to improve the query-to-feature similarity and eliminate the slow training convergence issue in DETR, but also allows us to modulate the positional attention map using the box width and height information. Such a design makes it clear that queries in DETR can be implemented as performing soft ROI pooling layer by layer in a cascade manner. As a result, it leads to the best performance on MS-COCO benchmark among the DETR-like detection models under the same setting, e.g., AP 45.7% using ResNet50-DC5 as backbone trained in 50 epochs. We also conducted extensive experiments to confirm our analysis and verify the effectiveness of our methods. Code is available at https://***/IDEA-opensource/DAB-DETR. © 2022 ICLR 2022 - 10th International Conference on Learning Representationss. All rights reserved.

关键词：

ANALYTIC-DPM: AN ANALYTIC ESTIMATE OF THE OPTIMAL REVERSE VARIANCE IN DIFFUSION PROBABILISTIC MODELS 10

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ANALYTIC-DPM: AN ANALYTIC ESTIMATE OF THE OPTIMAL REVERSE VA...

10th International Conference on Learning Representations, ICLR 2022

作者： Bao, Fan Li, Chongxuan Zhu, Jun Zhang, Bo Dept. of Comp. Sci. & Tech. Institute for AI Tsinghua-Huawei Joint Center for AI BNRist Center State Key Lab for Intell. Tech. & Sys. Tsinghua University Beijing China Gaoling School of Artificial Intelligence Renmin University of China Beijing China Beijing Key Laboratory of Big Data Management and Analysis Methods Beijing China

Diffusion probabilistic models (DPMs) represent a class of powerful generative models. Despite their success, the inference of DPMs is expensive since it generally needs to iterate over thousands of timesteps. A key problem in the inference is to estimate the variance in each timestep of the reverse process. In this work, we present a surprising result that both the optimal reverse variance and the corresponding optimal KL divergence of a DPM have analytic forms w.r.t. its score function. Building upon it, we propose Analytic-DPM, a training-free inference framework that estimates the analytic forms of the variance and KL divergence using the Monte Carlo method and a pretrained score-based model. Further, to correct the potential bias caused by the score-based model, we derive both lower and upper bounds of the optimal variance and clip the estimate for a better result. Empirically, our analytic-DPM improves the log-likelihood of various DPMs, produces high-quality samples, and meanwhile enjoys a 20× to 80× speed up. © 2022 ICLR 2022 - 10th International Conference on Learning Representationss. All rights reserved.

关键词： Monte Carlo methods

PRESERVING PRE-TRAINED FEATURES HELPS CALIBRATE FINE-TUNED LANGUAGE MODELS

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arXiv 2023年

作者： He, Guande Chen, Jianfei Zhu, Jun Dept. of Comp. Sci. & Tech. Institute for AI Tsinghua-Bosch Joint Center for ML BNRist Center State Key Lab for Intell. Tech. & Sys. Tsinghua University Beijing China Pazhou Lab Guangzhou510330 China

Large pre-trained language models (PLMs) have demonstrated strong performance on natural language understanding (NLU) tasks through fine-tuning. However, fine-tuned models still suffer from overconfident predictions, especially in out-of-domain settings. In this paper, we tackle the problem of calibrating fine-tuned language models. We demonstrate that the PLMs are well-calibrated on the masked language modeling task with robust predictive confidence under domain shift, yet the fine-tuned models fail to retain such property due to catastrophic forgetting, which impacts the calibration on the downstream classification task. In light of these observations, we evaluate the calibration of several methods that preserve pre-trained features and show that preserving pre-trained features can improve the calibration of fine-tuned language models. Among these methods, our proposed method that encourages the fine-tuned model to learn generative representations with auxiliary language modeling objective achieves competitive accuracy and the lowest expected calibration error compared to several strong baselines under both in-domain and out-of-domain settings on three downstream NLU tasks. Copyright © 2023, The Authors. All rights reserved.

关键词： Calibration

INVESTIGATING UNCERTAINTY CALIBRATION OF ALIGNED LANGUAGE MODELS UNDER THE MULTIPLE-CHOICE SETTING

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arXiv 2023年

作者： He, Guande Cui, Peng Chen, Jianfei Hu, Wenbo Zhu, Jun Dept. of Comp. Sci. & Tech. Institute for AI Tsinghua-Bosch Joint Center for ML BNRist Center State Key Lab for Intell. Tech. & Sys. Tsinghua University Beijing China Hefei University of Technology China

Despite the significant progress made in practical applications of aligned language models (LMs), they tend to be overconfident in output answers compared to the corresponding pre-trained LMs. In this work, we systematically evaluate the impact of the alignment process on logit-based uncertainty calibration of LMs under the multiple-choice setting. We first conduct a thoughtful empirical study on how aligned LMs differ in calibration from their pre-trained counterparts. Experimental results reveal that there are two distinct uncertainties in LMs under the multiple-choice setting, which are responsible for the answer decision and the format preference of the LMs, respectively. Then, we investigate the role of these two uncertainties on aligned LM’s calibration through fine-tuning in simple synthetic alignment schemes and conclude that one reason for aligned LMs’ overconfidence is the conflation of these two types of uncertainty. Furthermore, we examine the utility of common post-hoc calibration methods for aligned LMs and propose an easy-to-implement and sample-efficient method to calibrate aligned LMs. We hope our findings could provide insights into the design of more reliable alignment processes for LMs. © 2023, CC BY.

关键词： Calibration

Visual Recognition by Request

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arXiv 2022年

作者： Tang, Chufeng Xie, Lingxi Zhang, Xiaopeng Hu, Xiaolin Tian, Qi Dept. of Comp. Sci. & Tech. State Key Lab for Intell. Tech. & Sys. Institute for AI BNRist Tsinghua University China Huawei Inc. China

Humans have the ability of recognizing visual semantics in an unlimited granularity, but existing visual recognition algorithms cannot achieve this goal. In this paper, we establish a new paradigm named visual recognition by request (ViRReq1) to bridge the gap. The key lies in decomposing visual recognition into atomic tasks named requests and leveraging a knowledge base, a hierarchical and text-based dictionary, to assist task definition. ViRReq allows for (i) learning complicated whole-part hierarchies from highly incomplete annotations and (ii) inserting new concepts with minimal efforts. We also establish a solid baseline by integrating language-driven recognition into recent semantic and instance segmentation methods, and demonstrate its flexible recognition ability on CPP and ADE20K, two datasets with hierarchical whole-part annotations. Copyright © 2022, The Authors. All rights reserved.

关键词： Semantics

Fast instrument learning with faster rates 22

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Fast instrument learning with faster rates

Proceedings of the 36th International Conference on Neural Information Processing systems

作者： Ziyu Wang Yuhao Zhou Jun Zhu Dept. of Comp. Sci. and Tech. BNRist Center State Key Lab for Intell. Tech. & Sys. Institute for AI Tsinghua-Bosch Joint Center for ML Tsinghua University

ISBN: (纸本)9781713871088

We investigate nonlinear instrumental variable (IV) regression given high-dimensional instruments. We propose a simple algorithm which combines kernelized IV methods and an arbitrary, adaptive regression algorithm, accessed as a black box. Our algorithm enjoys faster-rate convergence and adapts to the dimensionality of informative latent features, while avoiding an expensive minimax optimization procedure, which has been necessary to establish similar guarantees. It further brings the benefit of flexible machine learning models to quasi-Bayesian uncertainty quantification, likelihood-based model selection, and model averaging. Simulation studies demonstrate the competitive performance of our method.

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Fast Instrument Learning with Faster Rates

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arXiv 2022年

作者： Wang, Ziyu Zhou, Yuhao Zhu, Jun Dept. of Comp. Sci. and Tech. BNRist Center State Key Lab for Intell. Tech. & Sys. Institute for AI Tsinghua-Bosch Joint Center for ML Tsinghua University China

关键词： Learning systems