Search Results for author:
Found 12 papers, 6 papers with code
Is a 3D-Tokenized LLM the Key to Reliable Autonomous Driving?
Despite its simplicity, Atlas demonstrates superior performance in both 3D detection and ego planning tasks on nuScenes dataset, proving that 3D-tokenized LLM is the key to reliable autonomous driving.
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 #92 on
Visual Question Answering
on MM-Vet
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.
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
A Novel Counterfactual Data Augmentation Method for Aspect-Based Sentiment Analysis
To mitigate this problem, we propose a novel and simple counterfactual data augmentation method to generate opinion expressions with reversed sentiment polarity.
Aspect-Based Sentiment Analysis
Aspect-Based Sentiment Analysis (ABSA)
+4
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.
Referring Multi-Object Tracking
In this paper, we propose a new and general referring understanding task, termed referring multi-object tracking (RMOT).
Multi-Level Representation Learning With Semantic Alignment for Referring Video Object Segmentation
To address this, we propose a novel multi-level representation learning approach, which explores the inherent structure of the video content to provide a set of discriminative visual embedding, enabling more effective vision-language semantic alignment.
Person Re-Identification by Context-aware Part Attention and Multi-Head Collaborative Learning
In addition, we propose a novel multi-head collaborative training scheme to improve the performance, which is collaboratively supervised by multiple heads with the same structure but different parameters.
Quadruplet Network with One-Shot Learning for Fast Visual Object Tracking
In this paper, we propose a new quadruplet deep network to examine the potential connections among the training instances, aiming to achieve a more powerful representation.
