Search Results for author:
Found 13 papers, 9 papers with code
OneChart: Purify the Chart Structural Extraction via One Auxiliary Token
To address this, we propose OneChart: a reliable agent specifically devised for the structural extraction of chart information.
Small Language Model Meets with Reinforced Vision Vocabulary
In Vary-toy, we introduce an improved vision vocabulary, allowing the model to not only possess all features of Vary but also gather more generality.
Ranked #74 on
Visual Question Answering
on MM-Vet
Vary: Scaling up the Vision Vocabulary for Large Vision-Language Models
Accordingly, we propose Vary, an efficient and effective method to scale up the vision vocabulary of LVLMs.
Ranked #50 on
Visual Question Answering
on MM-Vet
Optical Character Recognition (OCR)
Visual Question Answering
DreamLLM: Synergistic Multimodal Comprehension and Creation
This paper presents DreamLLM, a learning framework that first achieves versatile Multimodal Large Language Models (MLLMs) empowered with frequently overlooked synergy between multimodal comprehension and creation.
Ranked #1 on
Visual Question Answering
on MMBench
(GPT-3.5 score metric)
ChatSpot: Bootstrapping Multimodal LLMs via Precise Referring Instruction Tuning
Based on precise referring instruction, we propose ChatSpot, a unified end-to-end multimodal large language model that supports diverse forms of interactivity including mouse clicks, drag-and-drop, and drawing boxes, which provides a more flexible and seamless interactive experience.
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.
BEVStereo++: Accurate Depth Estimation in Multi-view 3D Object Detection via Dynamic Temporal Stereo
Bounded by the inherent ambiguity of depth perception, contemporary multi-view 3D object detection methods fall into the performance bottleneck.
Exploring Recurrent Long-term Temporal Fusion for Multi-view 3D Perception
In this paper, we explore an embarrassingly simple long-term recurrent fusion strategy built upon the LSS-based methods and find it already able to enjoy the merits from both sides, i. e., rich long-term information and efficient fusion pipeline.
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.
Reversible Column Networks
Such architectural scheme attributes RevCol very different behavior from conventional networks: during forward propagation, features in RevCol are learned to be gradually disentangled when passing through each column, whose total information is maintained rather than compressed or discarded as other network does.
Ranked #8 on
Semantic Segmentation
on ADE20K
(using extra training data)
Autoencoders as Cross-Modal Teachers: Can Pretrained 2D Image Transformers Help 3D Representation Learning?
The success of deep learning heavily relies on large-scale data with comprehensive labels, which is more expensive and time-consuming to fetch in 3D compared to 2D images or natural languages.
Ranked #5 on
Few-Shot 3D Point Cloud Classification
on ModelNet40 10-way (10-shot)
(using extra training data)
Few-Shot 3D Point Cloud Classification
Knowledge Distillation
+1
BEVStereo: Enhancing Depth Estimation in Multi-view 3D Object Detection with Dynamic Temporal Stereo
To this end, we introduce an effective temporal stereo method to dynamically select the scale of matching candidates, enable to significantly reduce computation overhead.
Ranked #11 on
3D Object Detection
on nuScenes Camera Only
BEVDepth: Acquisition of Reliable Depth for Multi-view 3D Object Detection
In this research, we propose a new 3D object detector with a trustworthy depth estimation, dubbed BEVDepth, for camera-based Bird's-Eye-View (BEV) 3D object detection.
Ranked #4 on
3D Object Detection
on Rope3D
