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Found 28 papers, 8 papers with code
KI-GAN: Knowledge-Informed Generative Adversarial Networks for Enhanced Multi-Vehicle Trajectory Forecasting at Signalized Intersections
Reliable prediction of vehicle trajectories at signalized intersections is crucial to urban traffic management and autonomous driving systems.
LaMPilot: An Open Benchmark Dataset for Autonomous Driving with Language Model Programs
Autonomous driving (AD) has made significant strides in recent years.
Driving through the Concept Gridlock: Unraveling Explainability Bottlenecks in Automated Driving
In the context of human-assisted or autonomous driving, explainability models can help user acceptance and understanding of decisions made by the autonomous vehicle, which can be used to rationalize and explain driver or vehicle behavior.
Real-time Learning of Driving Gap Preference for Personalized Adaptive Cruise Control
In the online component, driver feedback is used to update the driving gap preference in real time.
Interaction-Aware Personalized Vehicle Trajectory Prediction Using Temporal Graph Neural Networks
Accurate prediction of vehicle trajectories is vital for advanced driver assistance systems and autonomous vehicles.
CEMFormer: Learning to Predict Driver Intentions from In-Cabin and External Cameras via Spatial-Temporal Transformers
Driver intention prediction seeks to anticipate drivers' actions by analyzing their behaviors with respect to surrounding traffic environments.
M$^2$DAR: Multi-View Multi-Scale Driver Action Recognition with Vision Transformer
Ensuring traffic safety and preventing accidents is a critical goal in daily driving, where the advancement of computer vision technologies can be leveraged to achieve this goal.
Class Prototypes Based Contrastive Learning for Classifying Multi-Label and Fine-Grained Educational Videos
We learn a class prototype for each class and a loss function is employed to minimize the distances between a class prototype and the samples from the class.
Query Efficient Cross-Dataset Transferable Black-Box Attack on Action Recognition
By using a nearly disjoint dataset to train the substitute model, our method removes the requirement that the substitute model be trained using the same dataset as the target model, and leverages queries to the target model to retain the fooling rate benefits provided by query-based methods.
Driver Digital Twin for Online Prediction of Personalized Lane Change Behavior
The proposed system is first evaluated on a human-in-the-loop co-simulation platform, and then in a field implementation with three passenger vehicles connected through the 4G/LTE cellular network.
Contrastive Self-Supervised Learning Leads to Higher Adversarial Susceptibility
We establish that this is a result of the presence of false negative pairs in the training process, which increases model sensitivity to input perturbations.
"Knights": First Place Submission for VIPriors21 Action Recognition Challenge at ICCV 2021
This technical report presents our approach "Knights" to solve the action recognition task on a small subset of Kinetics-400 i. e. Kinetics400ViPriors without using any extra-data.
TCLR: Temporal Contrastive Learning for Video Representation
However, prior work on contrastive learning for video data has not explored the effect of explicitly encouraging the features to be distinct across the temporal dimension.
Ranked #9 on
Self-supervised Video Retrieval
on UCF101
A generalized approach to study low as well as high $p_T$ regime of transverse momentum spectra
A good understanding of the transverse momentum $(p_T)$ spectra is pivotal in the study of QCD matter created during the heavy-ion collision.
High Energy Physics - Phenomenology
Cassandra: Detecting Trojaned Networks from Adversarial Perturbations
We also propose an anomaly detection method to identify the target class in a Trojaned network.
Ranked #1 on
Adversarial Defense
on TrojAI Round 1
RescueNet: Joint Building Segmentation and Damage Assessment from Satellite Imagery
Accurate and fine-grained information about the extent of damage to buildings is essential for directing Humanitarian Aid and Disaster Response (HADR) operations in the immediate aftermath of any natural calamity.
Character-based NMT with Transformer
Character-based translation has several appealing advantages, but its performance is in general worse than a carefully tuned BPE baseline.
Improving Robustness in Real-World Neural Machine Translation Engines
As a commercial provider of machine translation, we are constantly training engines for a variety of uses, languages, and content types.
ParsRec: A Novel Meta-Learning Approach to Recommending Bibliographic Reference Parsers
We propose ParsRec, a meta-learning based recommender-system that recommends the potentially most effective parser for a given reference string.
Reordering rules for English-Hindi SMT
Reordering is a preprocessing stage for Statistical Machine Translation (SMT) system where the words of the source sentence are reordered as per the syntax of the target language.
