Search Results for author:
Found 33 papers, 22 papers with code
SubjectDrive: Scaling Generative Data in Autonomous Driving via Subject Control
Autonomous driving progress relies on large-scale annotated datasets.
Stream Query Denoising for Vectorized HD Map Construction
This paper introduces the Stream Query Denoising (SQD) strategy as a novel approach for temporal modeling in high-definition map (HD-map) construction.
Bootstrap Masked Visual Modeling via Hard Patches Mining
To empower the model as a teacher, we propose Hard Patches Mining (HPM), predicting patch-wise losses and subsequently determining where to mask.
Open-sourced Data Ecosystem in Autonomous Driving: the Present and Future
With the continuous maturation and application of autonomous driving technology, a systematic examination of open-source autonomous driving datasets becomes instrumental in fostering the robust evolution of the industry ecosystem.
Merlin:Empowering Multimodal LLMs with Foresight Minds
Then, FIT requires MLLMs to first predict trajectories of related objects and then reason about potential future events based on them.
Ranked #59 on
Visual Question Answering
on MM-Vet
PillarNeSt: Embracing Backbone Scaling and Pretraining for Pillar-based 3D Object Detection
Pillar-based methods mainly employ randomly initialized 2D convolution neural network (ConvNet) for feature extraction and fail to enjoy the benefits from the backbone scaling and pretraining in the image domain.
Panacea: Panoramic and Controllable Video Generation for Autonomous Driving
This work notably propels the field of autonomous driving by effectively augmenting the training dataset used for advanced BEV perception techniques.
ADriver-I: A General World Model for Autonomous Driving
Based on the vision-action pairs, we construct a general world model based on MLLM and diffusion model for autonomous driving, termed ADriver-I.
VLM-Eval: A General Evaluation on Video Large Language Models
Despite the rapid development of video Large Language Models (LLMs), a comprehensive evaluation is still absent.
TopoMLP: A Simple yet Strong Pipeline for Driving Topology Reasoning
Further, we propose TopoMLP, a simple yet high-performance pipeline for driving topology reasoning.
Ranked #3 on
3D Lane Detection
on OpenLane-V2 val
Language Prompt for Autonomous Driving
A new trend in the computer vision community is to capture objects of interest following flexible human command represented by a natural language prompt.
Far3D: Expanding the Horizon for Surround-view 3D Object Detection
Recently 3D object detection from surround-view images has made notable advancements with its low deployment cost.
Ranked #1 on
3D Object Detection
on nuScenes Camera Only
OnlineRefer: A Simple Online Baseline for Referring Video Object Segmentation
Referring video object segmentation (RVOS) aims at segmenting an object in a video following human instruction.
Referring Expression Segmentation
Referring Video Object Segmentation
+2
The 1st-place Solution for CVPR 2023 OpenLane Topology in Autonomous Driving Challenge
We present the 1st-place solution of OpenLane Topology in Autonomous Driving Challenge.
MOTRv3: Release-Fetch Supervision for End-to-End Multi-Object Tracking
Although end-to-end multi-object trackers like MOTR enjoy the merits of simplicity, they suffer from the conflict between detection and association seriously, resulting in unsatisfactory convergence dynamics.
Exploring Object-Centric Temporal Modeling for Efficient Multi-View 3D Object Detection
On the standard nuScenes benchmark, it is the first online multi-view method that achieves comparable performance (67. 6% NDS & 65. 3% AMOTA) with lidar-based methods.
Ranked #1 on
3D Multi-Object Tracking
on nuScenes Camera Only
Referring Multi-Object Tracking
In this paper, we propose a new and general referring understanding task, termed referring multi-object tracking (RMOT).
Cross Modal Transformer: Towards Fast and Robust 3D Object Detection
In this paper, we propose a robust 3D detector, named Cross Modal Transformer (CMT), for end-to-end 3D multi-modal detection.
MOTRv2: Bootstrapping End-to-End Multi-Object Tracking by Pretrained Object Detectors
In this paper, we propose MOTRv2, a simple yet effective pipeline to bootstrap end-to-end multi-object tracking with a pretrained object detector.
Ranked #2 on
Multi-Object Tracking
on DanceTrack
(using extra training data)
Multi-Object Tracking
Multiple Object Tracking with Transformer
+2
Towards 3D Object Detection with 2D Supervision
We propose a temporal 2D transformation to bridge the 3D predictions with temporal 2D labels.
The 1st-place Solution for ECCV 2022 Multiple People Tracking in Group Dance Challenge
We present our 1st place solution to the Group Dance Multiple People Tracking Challenge.
Multi-Object Tracking
Multiple Object Tracking with Transformer
+1
PETRv2: A Unified Framework for 3D Perception from Multi-Camera Images
More specifically, we extend the 3D position embedding (3D PE) in PETR for temporal modeling.
Ranked #2 on
Bird's-Eye View Semantic Segmentation
on nuScenes
(IoU lane - 224x480 - 100x100 at 0.5 metric)
Tree Energy Loss: Towards Sparsely Annotated Semantic Segmentation
The tree energy loss is effective and easy to be incorporated into existing frameworks by combining it with a traditional segmentation loss.
PETR: Position Embedding Transformation for Multi-View 3D Object Detection
Object query can perceive the 3D position-aware features and perform end-to-end object detection.
Implicit Feature Refinement for Instance Segmentation
Our IFR enjoys several advantages: 1) simulates an infinite-depth refinement network while only requiring parameters of single residual block; 2) produces high-level equilibrium instance features of global receptive field; 3) serves as a plug-and-play general module easily extended to most object recognition frameworks.
SOLQ: Segmenting Objects by Learning Queries
Moreover, the joint learning of unified query representation can greatly improve the detection performance of DETR.
Ranked #4 on
Object Detection
on COCO minival
(AP75 metric)
MOTR: End-to-End Multiple-Object Tracking with Transformer
Temporal modeling of objects is a key challenge in multiple object tracking (MOT).
Ranked #1 on
Multi-Object Tracking
on MOT17
(e2e-MOT metric)
Multi-Object Tracking
Multiple Object Tracking with Transformer
+1
Implicit Feature Pyramid Network for Object Detection
In this paper, we present an implicit feature pyramid network (i-FPN) for object detection.
Co-mining: Self-Supervised Learning for Sparsely Annotated Object Detection
Object detectors usually achieve promising results with the supervision of complete instance annotations.
Forest R-CNN: Large-Vocabulary Long-Tailed Object Detection and Instance Segmentation
In the classification tree, as the number of parent class nodes are significantly less, their logits are less noisy and can be utilized to suppress the wrong/noisy logits existed in the fine-grained class nodes.
Ranked #5 on
Few-Shot Object Detection
on LVIS v1.0 val
Learning Human-Object Interaction Detection using Interaction Points
Human-object interaction (HOI) detection strives to localize both the human and an object as well as the identification of complex interactions between them.
Deep Contextual Attention for Human-Object Interaction Detection
Our approach outperforms the state-of-the-art on all datasets.
Learning Rich Features at High-Speed for Single-Shot Object Detection
We introduce a single-stage detection framework that combines the advantages of both fine-tuning pretrained models and training from scratch.
