6 citations · 19 across the 14 of their papers we have counts for
13 papers · 1 filter
SpatialImaginer: Towards Adaptive Visual Imagination for Spatial Reasoning
Yian Li, Yang Jiao, Bin Zhu +4
Spatial intelligence, which refers to the ability to reason about geometric and physical structure from visual observations, remains a core challenge for multimodal large language…
UniToken: Harmonizing Multimodal Understanding and Generation through Unified Visual Encoding
Yang Jiao, Haibo Qiu, Zequn Jie +4
We introduce UniToken, an auto-regressive generation model that encodes visual inputs through a combination of discrete and continuous representations, enabling seamless integratio…
Making Large Language Models Better Planners with Reasoning-Decision Alignment
Zhijian Huang, Tao Tang, Shaoxiang Chen +5
Data-driven approaches for autonomous driving (AD) have been widely adopted in the past decade but are confronted with dataset bias and uninterpretability. Inspired by the knowledg…
MindBench: A Comprehensive Benchmark for Mind Map Structure Recognition and Analysis
Lei Chen, Feng Yan, Yujie Zhong +3
Multimodal Large Language Models (MLLM) have made significant progress in the field of document analysis. Despite this, existing benchmarks typically focus only on extracting text…
Fewer Tokens and Fewer Videos: Extending Video Understanding Abilities in Large Vision-Language Models
Shimin Chen, Yitian Yuan, Shaoxiang Chen +2
Amidst the advancements in image-based Large Vision-Language Models (image-LVLM), the transition to video-based models (video-LVLM) is hindered by the limited availability of quali…
Lumen: Unleashing Versatile Vision-Centric Capabilities of Large Multimodal Models
Yang Jiao, Shaoxiang Chen, Zequn Jie +3
Large Multimodal Model (LMM) is a hot research topic in the computer vision area and has also demonstrated remarkable potential across multiple disciplinary fields. A recent trend…
LLaVA-MoLE: Sparse Mixture of LoRA Experts for Mitigating Data Conflicts in Instruction Finetuning MLLMs
Shaoxiang Chen, Zequn Jie, Lin Ma
Instruction finetuning on a variety of image-text instruction data is the key to obtaining a versatile Multimodal Large Language Model (MLLM), and different configurations of the i…
Instance-aware Multi-Camera 3D Object Detection with Structural Priors Mining and Self-Boosting Learning
Yang Jiao, Zequn Jie, Shaoxiang Chen +4
Camera-based bird-eye-view (BEV) perception paradigm has made significant progress in the autonomous driving field. Under such a paradigm, accurate BEV representation construction…
MSMDFusion: Fusing LiDAR and Camera at Multiple Scales with Multi-Depth Seeds for 3D Object Detection
Yang Jiao, Zequn Jie, Shaoxiang Chen +3
Fusing LiDAR and camera information is essential for achieving accurate and reliable 3D object detection in autonomous driving systems. This is challenging due to the difficulty of…
MT-Net Submission to the Waymo 3D Detection Leaderboard
Shaoxiang Chen, Zequn Jie, Xiaolin Wei +1
In this technical report, we introduce our submission to the Waymo 3D Detection leaderboard. Our network is based on the Centerpoint architecture, but with significant improvements…
MORE: Multi-Order RElation Mining for Dense Captioning in 3D Scenes
Yang Jiao, Shaoxiang Chen, Zequn Jie +3
3D dense captioning is a recently-proposed novel task, where point clouds contain more geometric information than the 2D counterpart. However, it is also more challenging due to th…
Self-supervised Learning for Semi-supervised Temporal Language Grounding
Fan Luo, Shaoxiang Chen, Jingjing Chen +2
Given a text description, Temporal Language Grounding (TLG) aims to localize temporal boundaries of the segments that contain the specified semantics in an untrimmed video. TLG is…
FT-TDR: Frequency-guided Transformer and Top-Down Refinement Network for Blind Face Inpainting
Junke Wang, Shaoxiang Chen, Zuxuan Wu +1
Blind face inpainting refers to the task of reconstructing visual contents without explicitly indicating the corrupted regions in a face image. Inherently, this task faces two chal…