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Found 9 papers, 2 papers with code
WOMD-LiDAR: Raw Sensor Dataset Benchmark for Motion Forecasting
To study the effect of these modular approaches, design new paradigms that mitigate these limitations, and accelerate the development of end-to-end motion forecasting models, we augment the Waymo Open Motion Dataset (WOMD) with large-scale, high-quality, diverse LiDAR data for the motion forecasting task.
AFDetV2: Rethinking the Necessity of the Second Stage for Object Detection from Point Clouds
From this observation, we have devised a single-stage anchor-free network that can fulfill these requirements.
Real-Time Anchor-Free Single-Stage 3D Detection with IoU-Awareness
Extended from our last year's award-winning model AFDet, we have made a handful of modifications to the base model, to improve the accuracy and at the same time to greatly reduce the latency.
2nd Place Solution for Waymo Open Dataset Challenge -- 2D Object Detection
A practical autonomous driving system urges the need to reliably and accurately detect vehicles and persons.
1st Place Solution for Waymo Open Dataset Challenge -- 3D Detection and Domain Adaptation
We proposed a one-stage, anchor-free and NMS-free 3D point cloud object detector AFDet, using object key-points to encode the 3D attributes, and to learn an end-to-end point cloud object detection without the need of hand-engineering or learning the anchors.
1st Place Solutions for Waymo Open Dataset Challenges -- 2D and 3D Tracking
This technical report presents the online and real-time 2D and 3D multi-object tracking (MOT) algorithms that reached the 1st places on both Waymo Open Dataset 2D tracking and 3D tracking challenges.
AFDet: Anchor Free One Stage 3D Object Detection
High-efficiency point cloud 3D object detection operated on embedded systems is important for many robotics applications including autonomous driving.
MAC: Mining Activity Concepts for Language-based Temporal Localization
Previous methods address the problem by considering features from video sliding windows and language queries and learning a subspace to encode their correlation, which ignore rich semantic cues about activities in videos and queries.
Motion-Appearance Co-Memory Networks for Video Question Answering
Specifically, there are three salient aspects: (1) a co-memory attention mechanism that utilizes cues from both motion and appearance to generate attention; (2) a temporal conv-deconv network to generate multi-level contextual facts; (3) a dynamic fact ensemble method to construct temporal representation dynamically for different questions.
Ranked #30 on
Visual Question Answering (VQA)
on MSRVTT-QA
