Search Results for author:
Found 14 papers, 13 papers with code
OmniCaptioner: One Captioner to Rule Them All
We propose OmniCaptioner, a versatile visual captioning framework for generating fine-grained textual descriptions across a wide variety of visual domains.
SurveyForge: On the Outline Heuristics, Memory-Driven Generation, and Multi-dimensional Evaluation for Automated Survey Writing
Survey paper plays a crucial role in scientific research, especially given the rapid growth of research publications.
Dolphin: Closed-loop Open-ended Auto-research through Thinking, Practice, and Feedback
The scientific research paradigm is undergoing a profound transformation owing to the development of Artificial Intelligence (AI).
GeoX: Geometric Problem Solving Through Unified Formalized Vision-Language Pre-training
Given the significant differences between geometric diagram-symbol and natural image-text, we introduce unimodal pre-training to develop a diagram encoder and symbol decoder, enhancing the understanding of geometric images and corpora.
Training-Free Adaptive Diffusion with Bounded Difference Approximation Strategy
Diffusion models have recently achieved great success in the synthesis of high-quality images and videos.
DocGenome: An Open Large-scale Scientific Document Benchmark for Training and Testing Multi-modal Large Language Models
Scientific documents record research findings and valuable human knowledge, comprising a vast corpus of high-quality data.
OmniCorpus: A Unified Multimodal Corpus of 10 Billion-Level Images Interleaved with Text
In this paper, we introduce OmniCorpus, a 10 billion-scale image-text interleaved dataset.
How Far Are We to GPT-4V? Closing the Gap to Commercial Multimodal Models with Open-Source Suites
Compared to both open-source and proprietary models, InternVL 1. 5 shows competitive performance, achieving state-of-the-art results in 8 of 18 benchmarks.
Ranked #6 on
Multiple-choice
on Neptune-Full
ChartX & ChartVLM: A Versatile Benchmark and Foundation Model for Complicated Chart Reasoning
Recently, many versatile Multi-modal Large Language Models (MLLMs) have emerged continuously.
StructChart: On the Schema, Metric, and Augmentation for Visual Chart Understanding
Specifically, StructChart first reformulates the chart data from the tubular form (linearized CSV) to STR, which can friendlily reduce the task gap between chart perception and reasoning.
Ranked #20 on
Chart Question Answering
on ChartQA
(using extra training data)
SPOT: Scalable 3D Pre-training via Occupancy Prediction for Learning Transferable 3D Representations
Annotating 3D LiDAR point clouds for perception tasks is fundamental for many applications e. g., autonomous driving, yet it still remains notoriously labor-intensive.
ReSimAD: Zero-Shot 3D Domain Transfer for Autonomous Driving with Source Reconstruction and Target Simulation
Domain shifts such as sensor type changes and geographical situation variations are prevalent in Autonomous Driving (AD), which poses a challenge since AD model relying on the previous domain knowledge can be hardly directly deployed to a new domain without additional costs.
AD-PT: Autonomous Driving Pre-Training with Large-scale Point Cloud Dataset
It is a long-term vision for Autonomous Driving (AD) community that the perception models can learn from a large-scale point cloud dataset, to obtain unified representations that can achieve promising results on different tasks or benchmarks.
Bi3D: Bi-domain Active Learning for Cross-domain 3D Object Detection
Unsupervised Domain Adaptation (UDA) technique has been explored in 3D cross-domain tasks recently.
