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Found 9 papers, 5 papers with code
TrajectoryFormer: 3D Object Tracking Transformer with Predictive Trajectory Hypotheses
3D multi-object tracking (MOT) is vital for many applications including autonomous driving vehicles and service robots.
Brain Topography Adaptive Network for Satisfaction Modeling in Interactive Information Access System
We explore the effectiveness of BTA for satisfaction modeling in two popular information access scenarios, i. e., search and recommendation.
MPPNet: Multi-Frame Feature Intertwining with Proxy Points for 3D Temporal Object Detection
Accurate and reliable 3D detection is vital for many applications including autonomous driving vehicles and service robots.
Web Search via an Efficient and Effective Brain-Machine Interface
While search technologies have evolved to be robust and ubiquitous, the fundamental interaction paradigm has remained relatively stable for decades.
Why Don't You Click: Neural Correlates of Non-Click Behaviors in Web Search
Inspired by these findings, we conduct supervised learning tasks to estimate the usefulness of non-click results with brain signals and conventional information (i. e., content and context factors).
Towards a Better Understanding Human Reading Comprehension with Brain Signals
In this paper, we carefully design a lab-based user study to investigate brain activities during reading comprehension.
Semantic Scene Completion via Integrating Instances and Scene in-the-Loop
The key insight is that we decouple the instances from a coarsely completed semantic scene instead of a raw input image to guide the reconstruction of instances and the overall scene.
Ranked #1 on
3D Semantic Scene Completion
on NYUv2
Salience-Guided Cascaded Suppression Network for Person Re-Identification
To tackle this limitation, we introduce a cascaded suppression strategy, which enables the network to mine diverse potential useful features that be masked by the other salient features stage-by-stage and each stage integrates different feature embedding for the last discriminative pedestrian representation.
One-Shot Adversarial Attacks on Visual Tracking With Dual Attention
Almost all adversarial attacks in computer vision are aimed at pre-known object categories, which could be offline trained for generating perturbations.
