Search Results for author:
Found 8 papers, 4 papers with code
RMMDet: Road-Side Multitype and Multigroup Sensor Detection System for Autonomous Driving
Then we implement muti-type sensor detection and multi-group sensors fusion in this environment, including camera-radar and camera-lidar detection based on result-level fusion.
PIZZA: A new benchmark for complex end-to-end task-oriented parsing
Much recent work in task-oriented parsing has focused on finding a middle ground between flat slots and intents, which are inexpressive but easy to annotate, and powerful representations such as the lambda calculus, which are expressive but costly to annotate.
Slow Motion Matters: A Slow Motion Enhanced Network for Weakly Supervised Temporal Action Localization
Weakly supervised temporal action localization (WTAL) aims to localize actions in untrimmed videos with only weak supervision information (e. g. video-level labels).
Weakly-supervised Temporal Action Localization
Weakly Supervised Temporal Action Localization
Cross-TOP: Zero-Shot Cross-Schema Task-Oriented Parsing
However, a single model is still typically trained and deployed for each task separately, requiring labeled training data for each, which makes it challenging to support new tasks, even within a single business vertical (e. g., food-ordering or travel booking).
Compositional Task-Oriented Parsing as Abstractive Question Answering
Task-oriented parsing (TOP) aims to convert natural language into machine-readable representations of specific tasks, such as setting an alarm.
TJ4DRadSet: A 4D Radar Dataset for Autonomous Driving
The next-generation high-resolution automotive radar (4D radar) can provide additional elevation measurement and denser point clouds, which has great potential for 3D sensing in autonomous driving.
Unfreeze with Care: Space-Efficient Fine-Tuning of Semantic Parsing Models
We examine two such promising techniques, prefix tuning and bias-term tuning, specifically on semantic parsing.
CLIP2TV: Align, Match and Distill for Video-Text Retrieval
Modern video-text retrieval frameworks basically consist of three parts: video encoder, text encoder and the similarity head.
Ranked #9 on
Video Retrieval
on MSR-VTT-1kA
(using extra training data)
