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Found 21 papers, 6 papers with code
FEELVOS: Fast End-to-End Embedding Learning for Video Object Segmentation
Many of the recent successful methods for video object segmentation (VOS) are overly complicated, heavily rely on fine-tuning on the first frame, and/or are slow, and are hence of limited practical use.
Ranked #1 on
Semi-Supervised Video Object Segmentation
on YouTube
Scalability in Perception for Autonomous Driving: Waymo Open Dataset
In an effort to help align the research community's contributions with real-world self-driving problems, we introduce a new large scale, high quality, diverse dataset.
Just Pick a Sign: Optimizing Deep Multitask Models with Gradient Sign Dropout
The vast majority of deep models use multiple gradient signals, typically corresponding to a sum of multiple loss terms, to update a shared set of trainable weights.
TNT: Target-driveN Trajectory Prediction
Our key insight is that for prediction within a moderate time horizon, the future modes can be effectively captured by a set of target states.
MultiPath: Multiple Probabilistic Anchor Trajectory Hypotheses for Behavior Prediction
Predicting human behavior is a difficult and crucial task required for motion planning.
Ranked #2 on
Trajectory Prediction
on PAID
NOVA: NOvel View Augmentation for Neural Composition of Dynamic Objects
We propose a novel-view augmentation (NOVA) strategy to train NeRFs for photo-realistic 3D composition of dynamic objects in a static scene.
Patchwork: A Patch-wise Attention Network for Efficient Object Detection and Segmentation in Video Streams
The latency reduction by this hard attention mechanism comes at the cost of degraded accuracy.
StarNet: Targeted Computation for Object Detection in Point Clouds
We show how our redesign---namely using only local information and using sampling instead of learned proposals---leads to a significantly more flexible and adaptable system: we demonstrate how we can vary the computational cost of a single trained StarNet without retraining, and how we can target proposals towards areas of interest with priors and heuristics.
SurfelGAN: Synthesizing Realistic Sensor Data for Autonomous Driving
In such scenarios, the ability to accurately simulate the vehicle sensors such as cameras, lidar or radar is essential.
SoDA: Multi-Object Tracking with Soft Data Association
Robust multi-object tracking (MOT) is a prerequisite fora safe deployment of self-driving cars.
Pseudo-labeling for Scalable 3D Object Detection
To safely deploy autonomous vehicles, onboard perception systems must work reliably at high accuracy across a diverse set of environments and geographies.
Large Scale Interactive Motion Forecasting for Autonomous Driving : The Waymo Open Motion Dataset
Furthermore, we introduce a new set of metrics that provides a comprehensive evaluation of both single agent and joint agent interaction motion forecasting models.
RSN: Range Sparse Net for Efficient, Accurate LiDAR 3D Object Detection
These larger detection ranges require more efficient and accurate detection models.
To the Point: Efficient 3D Object Detection in the Range Image with Graph Convolution Kernels
3D object detection is vital for many robotics applications.
HDMapGen: A Hierarchical Graph Generative Model of High Definition Maps
To address this issue, we introduce a new challenging task to generate HD maps.
Large Scale Interactive Motion Forecasting for Autonomous Driving: The Waymo Open Motion Dataset
Furthermore, we introduce a new set of metrics that provides a comprehensive evaluation of both single agent and joint agent interaction motion forecasting models.
Occupancy Flow Fields for Motion Forecasting in Autonomous Driving
We propose Occupancy Flow Fields, a new representation for motion forecasting of multiple agents, an important task in autonomous driving.
Efficient Transformer-based 3D Object Detection with Dynamic Token Halting
Although halting a token is a non-differentiable operation, our method allows for differentiable end-to-end learning by leveraging an equivalent differentiable forward-pass.
SHIFT3D: Synthesizing Hard Inputs For Tricking 3D Detectors
We present SHIFT3D, a differentiable pipeline for generating 3D shapes that are structurally plausible yet challenging to 3D object detectors.
Making Large Multimodal Models Understand Arbitrary Visual Prompts
Furthermore, we present ViP-Bench, a comprehensive benchmark to assess the capability of models in understanding visual prompts across multiple dimensions, enabling future research in this domain.
Cohere3D: Exploiting Temporal Coherence for Unsupervised Representation Learning of Vision-based Autonomous Driving
Observations from different angles enable the recovery of 3D object states from 2D image inputs if we can identify the same instance in different input frames.
