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Found 8 papers, 1 papers with code
Intention Action Anticipation Model with Guide-Feedback Loop Mechanism
Based on GFL, the MultiComplete-Recent Feature Aggregation (MCRFA) module is proposed to model the relation of one recent feature with multiscale complete features.
A Fast and Map-Free Model for Trajectory Prediction in Traffics
To handle the two shortcomings of existing methods, (i)nearly all models rely on high-definition (HD) maps, yet the map information is not always available in real traffic scenes and HD map-building is expensive and time-consuming and (ii) existing models usually focus on improving prediction accuracy at the expense of reducing computing efficiency, yet the efficiency is crucial for various real applications, this paper proposes an efficient trajectory prediction model that is not dependent on traffic maps.
FBLNet: FeedBack Loop Network for Driver Attention Prediction
The driving experience is extremely important for safe driving, a skilled driver is able to effortlessly predict oncoming danger (before it becomes salient) based on the driving experience and quickly pay attention to the corresponding zones. However, the nonobjective driving experience is difficult to model, so a mechanism simulating the driver experience accumulation procedure is absent in existing methods, and the current methods usually follow the technique line of saliency prediction methods to predict driver attention.
X-GGM: Graph Generative Modeling for Out-of-Distribution Generalization in Visual Question Answering
In this paper, we formulate OOD generalization in VQA as a compositional generalization problem and propose a graph generative modeling-based training scheme (X-GGM) to implicitly model the problem.
A Driving Behavior Recognition Model with Bi-LSTM and Multi-Scale CNN
In autonomous driving, perceiving the driving behaviors of surrounding agents is important for the ego-vehicle to make a reasonable decision.
Learning to Infer Unseen Attribute-Object Compositions
The composition recognition of unseen attribute-object is critical to make machines learn to decompose and compose complex concepts like people.
Traffic Agent Trajectory Prediction Using Social Convolution and Attention Mechanism
In this paper, we propose a model to predict the trajectories of target agents around an autonomous vehicle.
BEYOND SUPERVISED LEARNING: RECOGNIZING UNSEEN ATTRIBUTE-OBJECT PAIRS WITH VISION-LANGUAGE FUSION AND ATTRACTOR NETWORKS
This paper handles a challenging problem, unseen attribute-object pair recognition, which asks a model to simultaneously recognize the attribute type and the object type of a given image while this attribute-object pair is not included in the training set.
