Nowadays, detecting sentiment or emotion from user generated texts has been intensively studied in natural language understanding, especially via neural-based models based on text representation. However, the interpretability on how could the final text sentiment be determined by neural-based text representation has not been thoroughly unfolded yet. Consequently, in this paper, we proposeCogLignwhich injects theneural-cognitionderived from Electroencephalogram (EEG)-signal into theneural-basedtext sentiment analysis model, aimed at learning the activation of brain regions stimulated by different sentiments, so as to guide our proposedCogLignto make proper determination on text sentiment in brain-like way. Specifically, on the one hand, the given videos in different sentiments have been watched bysubjects, during which the EEG-signals are monitored to construct brain connectivity pattern asbrain graph(BG), attaining more obvious sentiment response on brain region activation forneural-cognition. On the other hand, we interpret the video-plots (or video-semantics) along timeline into text, where the entire video-interpreted-text will bestrictly boundwith the wholeEEG-signal-sequencebysegmentvia the fixed size oftime-window. Then, entities and relations are extracted from the video-interpreted-text to constructknowledge graph(KG), depicting text semantics. Next, mapping fromentities(or nodes) inKGtoEEG-Electrodes(or nodes) inBG, further dated back to different brain regions, has been learned viacognition alignmentbetween the EEG-derivedBGand text-derivedKG. In this way, by aligningneural cognitionfrombrain graphwith thesemantic cognitionfromknowledge graph, our proposed frameworkCogLigncan not only achieve the overall best sentiment analysis performance on thevideo-interpreted-text, but can also detect brain connectivity patterns in different sentiments more consistent with the prior conclusion of brain region sentiment preference, revealing competitiveinterpretabilityon text sentiment determination.

Safety is the primary priority of autonomous driving. Nevertheless, no published dataset currently supports the direct and explainable safety evaluation for autonomous driving. In this work, we propose DeepAccident, a large-scale dataset generated via a realistic simulator containing diverse accident scenarios that frequently occur in real-world driving. The proposed DeepAccident dataset includes 57K annotated frames and 285K annotated samples, approximately 7 times more than the large-scale nuScenes dataset with 40k annotated samples. In addition, we propose a new task, end-to-end motion and accident prediction, which can be used to directly evaluate the accident prediction ability for different autonomous driving algorithms. Furthermore, for each scenario, we set four vehicles along with one infrastructure to record data, thus providing diverse viewpoints for accident scenarios and enabling V2X (vehicle-to-everything) research on perception and prediction tasks. Finally, we present a baseline V2X model named V2XFormer that demonstrates superior performance for motion and accident prediction and 3D object detection compared to the single-vehicle model.

Recent advances in unsupervised domain adaptation (UDA) show that transferable prototypical learning presents a powerful means for class conditional alignment, which encourages the closeness of cross-domain class centroids. However, the cross-domain inner-class compactness and the underlying fine-grained subtype structure remained largely underexplored. In this work, we propose to adaptively carry out the fine-grained subtype-aware alignment by explicitly enforcing the class-wise separation and subtype-wise compactness with intermediate pseudo labels. Our key insight is that the unlabeled subtypes of a class can be divergent to one another with different conditional and label shifts, while inheriting the local proximity within a subtype. The cases with or without the prior information on subtype numbers are investigated to discover the underlying subtype structure in an online fashion. The proposed subtype-aware dynamic UDA achieves promising results on a medical diagnosis task.

Semantic segmentation is a class of methods to classify each pixel in an image into semantic classes, which is critical for autonomous vehicles and surgery systems. Cross-entropy (CE) loss-based deep neural networks (DNN) achieved great success w.r.t. the accuracy-based metrics, e.g., mean Intersection-over Union. However, the CE loss has a limitation in that it ignores varying degrees of severity of pair-wise misclassified results. For instance, classifying a car into the road is much more terrible than recognizing it as a bus. To sidestep this, in this work, we propose to incorporate the severity-aware inter-class correlation into our Wasserstein training framework by configuring its ground distance matrix. In addition, our method can adaptively learn the ground metric in a high-fidelity simulator, following a reinforcement alternative optimization scheme. We evaluate our method using the CARLA simulator with the Deeplab backbone, demonstraing that our method significantly improves the survival time in the CARLA simulator. In addition, our method can be readily applied to existing DNN architectures and algorithms while yielding superior performance. We report results from experiments carried out with the CamVid and Cityscapes datasets.