Is my Driver Observation Model Overconfident? Input-guided Calibration Networks for Reliable and Interpretable Confidence Estimates

Driver observation models are rarely deployed under perfect conditions. In practice, illumination, camera placement and type differ from the ones present during training and unforeseen behaviours may occur at any time. While observing the human behind the steering wheel leads to more intuitive human-vehicle-interaction and safer driving, it requires recognition algorithms which do not only predict the correct driver state, but also determine their prediction quality through realistic and interpretable confidence measures. Reliable uncertainty estimates are crucial for building trust and are a serious obstacle for deploying activity recognition networks in real driving systems. In this work, we for the first time examine how well the confidence values of modern driver observation models indeed match the probability of the correct outcome and show that raw neural network-based approaches tend to significantly overestimate their prediction quality. To correct this misalignment between the confidence values and the actual uncertainty, we consider two strategies. First, we enhance two activity recognition models often used for driver observation with temperature scaling-an off-the-shelf method for confidence calibration in image classification. Then, we introduce Calibrated Action Recognition with Input Guidance (CARING)-a novel approach leveraging an additional neural network to learn scaling the confidences depending on the video representation. Extensive experiments on the Drive&Act dataset demonstrate that both strategies drastically improve the quality of model confidences, while our CARING model out-performs both, the original architectures and their temperature scaling enhancement, leading to best uncertainty estimates.

Indoor Navigation Assistance for Visually Impaired People via Dynamic SLAM and Panoptic Segmentation with an RGB-D Sensor

Exploring an unfamiliar indoor environment and avoiding obstacles is challenging for visually impaired people. Currently, several approaches achieve the avoidance of static obstacles based on the mapping of indoor scenes. To solve the issue of distinguishing dynamic obstacles, we propose an assistive system with an RGB-D sensor to detect dynamic information of a scene. Once the system captures an image, panoptic segmentation is performed to obtain the prior dynamic object information. With sparse feature points extracted from images and the depth information, poses of the user can be estimated. After the ego-motion estimation, the dynamic object can be identified and tracked. Then, poses and speed of tracked dynamic objects can be estimated, which are passed to the users through acoustic feedback.

MatchFormer: Interleaving Attention in Transformers for Feature Matching

Local feature matching is a computationally intensive task at the subpixel level. While detector-based methods coupled with feature descriptors struggle in low-texture scenes, CNN-based methods with a sequential extract-to-match pipeline, fail to make use of the matching capacity of the encoder and tend to overburden the decoder for matching. In contrast, we propose a novel hierarchical extract-and-match transformer, termed as MatchFormer. Inside each stage of the hierarchical encoder, we interleave self-attention for feature extraction and cross-attention for feature matching, enabling a human-intuitive extract-and-match scheme. Such a match-aware encoder releases the overloaded decoder and makes the model highly efficient. Further, combining self- and cross-attention on multi-scale features in a hierarchical architecture improves matching robustness, particularly in low-texture indoor scenes or with less outdoor training data. Thanks to such a strategy, MatchFormer is a multi-win solution in efficiency, robustness, and precision. Compared to the previous best method in indoor pose estimation, our lite MatchFormer has only 45% GFLOPs, yet achieves a +1.3% precision gain and a 41% running speed boost. The large MatchFormer reaches state-of-the-art on four different benchmarks, including indoor pose estimation (ScanNet), outdoor pose estimation (MegaDepth), homography estimation and image matching (HPatch), and visual localization (InLoc). Code will be made publicly available at https://github.com/jamycheung/MatchFormer.

Towards Robust Semantic Segmentation of Accident Scenes via Multi-Source Mixed Sampling and Meta-Learning

Autonomous vehicles utilize urban scene segmentation to understand the real world like a human and react accordingly. Semantic segmentation of normal scenes has experienced a remarkable rise in accuracy on conventional benchmarks. However, a significant portion of real-life accidents features abnormal scenes, such as those with object deformations, overturns, and unexpected traffic behaviors. Since even small mis-segmentation of driving scenes can lead to serious threats to human lives, the robustness of such models in accident scenarios is an extremely important factor in ensuring safety of intelligent transportation systems. In this paper, we propose a Multi-source Meta-learning Unsupervised Domain Adaptation (MMUDA) framework, to improve the generalization of segmentation transformers to extreme accident scenes. In MMUDA, we make use of Multi-Domain Mixed Sampling to augment the images of multiple-source domains (normal scenes) with the target data appearances (abnormal scenes). To train our model, we intertwine and study a meta-learning strategy in the multi-source setting for robustifying the segmentation results. We further enhance the segmentation backbone (SegFormer) with a HybridASPP decoder design, featuring large window attention spatial pyramid pooling and strip pooling, to efficiently aggregate long-range contextual dependencies. Our approach achieves a mIoU score of 46.97% on the DADA-seg benchmark, surpassing the previous state-of-the-art model by more than 7.50%. Code will be made publicly available at https://github.com/xinyu-laura/MMUDA.

Bending Reality: Distortion-aware Transformers for Adapting to Panoramic Semantic Segmentation

Panoramic images with their 360-degree directional view encompass exhaustive information about the surrounding space, providing a rich foundation for scene understanding. To unfold this potential in the form of robust panoramic segmentation models, large quantities of expensive, pixel-wise annotations are crucial for success. Such annotations are available, but predominantly for narrow-angle, pinhole-camera images which, off the shelf, serve as sub-optimal resources for training panoramic models. Distortions and the distinct image-feature distribution in 360-degree panoramas impede the transfer from the annotation-rich pinhole domain and therefore come with a big dent in performance. To get around this domain difference and bring together semantic annotations from pinhole- and 360-degree surround-visuals, we propose to learn object deformations and panoramic image distortions in the Deformable Patch Embedding (DPE) and Deformable MLP (DMLP) components which blend into our Transformer for PAnoramic Semantic Segmentation (Trans4PASS) model. Finally, we tie together shared semantics in pinhole- and panoramic feature embeddings by generating multi-scale prototype features and aligning them in our Mutual Prototypical Adaptation (MPA) for unsupervised domain adaptation. On the indoor Stanford2D3D dataset, our Trans4PASS with MPA maintains comparable performance to fully-supervised state-of-the-arts, cutting the need for over 1,400 labeled panoramas. On the outdoor DensePASS dataset, we break state-of-the-art by 14.39% mIoU and set the new bar at 56.38%. Code will be made publicly available at https://github.com/jamycheung/Trans4PASS.

CMX: Cross-Modal Fusion for RGB-X Semantic Segmentation with Transformers

The performance of semantic segmentation of RGB images can be advanced by exploiting informative features from supplementary modalities. In this work, we propose CMX, a vision-transformer-based cross-modal fusion framework for RGB-X semantic segmentation. To generalize to different sensing modalities encompassing various uncertainties, we consider that comprehensive cross-modal interactions should be provided. CMX is built with two streams to extract features from RGB images and the complementary modality (X-modality). In each feature extraction stage, we design a Cross-Modal Feature Rectification Module (CM-FRM) to calibrate the feature of the current modality by combining the feature from the other modality, in spatial- and channel-wise dimensions. With rectified feature pairs, we deploy a Feature Fusion Module (FFM) to mix them for the final semantic prediction. FFM is constructed with a cross-attention mechanism, which enables exchange of long-range contexts, enhancing both modalities' features at a global level. Extensive experiments show that CMX generalizes to diverse multi-modal combinations, achieving state-of-the-art performances on four RGB-Depth benchmarks, as well as RGB-Thermal and RGB-Polarization datasets. Besides, to investigate the generalizability to dense-sparse data fusion, we establish a RGB-Event semantic segmentation benchmark based on the EventScape dataset, on which CMX sets the new state-of-the-art. Code is available at https://github.com/huaaaliu/RGBX_Semantic_Segmentation

TransDARC: Transformer-based Driver Activity Recognition with Latent Space Feature Calibration

Traditional video-based human activity recognition has experienced remarkable progress linked to the rise of deep learning, but this effect was slower as it comes to the downstream task of driver behavior understanding. Understanding the situation inside the vehicle cabin is essential for Advanced Driving Assistant System (ADAS) as it enables identifying distraction, predicting driver's intent and leads to more convenient human-vehicle interaction. At the same time, driver observation systems face substantial obstacles as they need to capture different granularities of driver states, while the complexity of such secondary activities grows with the rising automation and increased driver freedom. Furthermore, a model is rarely deployed under conditions identical to the ones in the training set, as sensor placements and types vary from vehicle to vehicle, constituting a substantial obstacle for real-life deployment of data-driven models. In this work, we present a novel vision-based framework for recognizing secondary driver behaviours based on visual transformers and an additional augmented feature distribution calibration module. This module operates in the latent feature-space enriching and diversifying the training set at feature-level in order to improve generalization to novel data appearances, (e.g., sensor changes) and general feature quality. Our framework consistently leads to better recognition rates, surpassing previous state-of-the-art results of the public Drive&Act benchmark on all granularity levels. Our code will be made publicly available at https://github.com/KPeng9510/TransDARC.

Transformer-based Knowledge Distillation for Efficient Semantic Segmentation of Road-driving Scenes

For scene understanding in robotics and automated driving, there is a growing interest in solving semantic segmentation tasks with transformer-based methods. However, effective transformers are always too cumbersome and computationally expensive to solve semantic segmentation in real time, which is desired for robotic systems. Moreover, due to the lack of inductive biases compared to Convolutional Neural Networks (CNNs), pre-training on a large dataset is essential but it takes a long time. Knowledge Distillation (KD) speeds up inference and maintains accuracy while transferring knowledge from a pre-trained cumbersome teacher model to a compact student model. Most traditional KD methods for CNNs focus on response-based knowledge and feature-based knowledge. In contrast, we present a novel KD framework according to the nature of transformers, i.e., training compact transformers by transferring the knowledge from feature maps and patch embeddings of large transformers. To this purpose, two modules are proposed: (1) the Selective Kernel Fusion (SKF) module, which helps to construct an efficient relation-based KD framework, Selective Kernel Review (SKR); (2) the Patch Embedding Alignment (PEA) module, which performs the dimensional transformation of patch embeddings. The combined KD framework is called SKR+PEA. Through comprehensive experiments on Cityscapes and ACDC datasets, it indicates that our proposed approach outperforms recent state-of-the-art KD frameworks and rivals the time-consuming pre-training method. Code will be made publicly available at https://github.com/RuipingL/SKR_PEA.git

PanoFlow: Learning Optical Flow for Panoramic Images

Optical flow estimation is a basic task in self-driving and robotics systems, which enables to temporally interpret the traffic scene. Autonomous vehicles clearly benefit from the ultra-wide Field of View (FoV) offered by 360-degree panoramic sensors. However, due to the unique imaging process of panoramic images, models designed for pinhole images do not directly generalize satisfactorily to 360-degree panoramic images. In this paper, we put forward a novel network framework--PanoFlow, to learn optical flow for panoramic images. To overcome the distortions introduced by equirectangular projection in panoramic transformation, we design a Flow Distortion Augmentation (FDA) method. We further propose a Cyclic Flow Estimation (CFE) method by leveraging the cyclicity of spherical images to infer 360-degree optical flow and converting large displacement to relatively small displacement. PanoFlow is applicable to any existing flow estimation method and benefit from the progress of narrow-FoV flow estimation. In addition, we create and release a synthetic panoramic dataset Flow360 based on CARLA to facilitate training and quantitative analysis. PanoFlow achieves state-of-the-art performance. Our proposed approach reduces the End-Point-Error (EPE) on the established Flow360 dataset by 26%. On the public OmniFlowNet dataset, PanoFlow achieves an EPE of 3.34 pixels, a 53.1% error reduction from the best published result (7.12 pixels). We also validate our method via an outdoor collection vehicle, indicating strong potential and robustness for real-world navigation applications. Code and dataset are publicly available at https://github.com/MasterHow/PanoFlow.