Search Results for author:
Found 9 papers, 5 papers with code
InfiMM-HD: A Leap Forward in High-Resolution Multimodal Understanding
Multimodal Large Language Models (MLLMs) have experienced significant advancements recently.
Ranked #38 on
Visual Question Answering
on MM-Vet
COCO is "ALL'' You Need for Visual Instruction Fine-tuning
We argue that datasets with diverse and high-quality detailed instruction following annotations are essential and adequate for MLLMs IFT.
Ranked #44 on
Visual Question Answering
on MM-Vet
Exploring the Reasoning Abilities of Multimodal Large Language Models (MLLMs): A Comprehensive Survey on Emerging Trends in Multimodal Reasoning
In this survey, we comprehensively review the existing evaluation protocols of multimodal reasoning, categorize and illustrate the frontiers of MLLMs, introduce recent trends in applications of MLLMs on reasoning-intensive tasks, and finally discuss current practices and future directions.
InfiMM-Eval: Complex Open-Ended Reasoning Evaluation For Multi-Modal Large Language Models
To mitigate this issue, we manually curate a benchmark dataset specifically designed for MLLMs, with a focus on complex reasoning tasks.
HallE-Control: Controlling Object Hallucination in Large Multimodal Models
Current Large Multimodal Models (LMMs) achieve remarkable progress, yet there remains significant uncertainty regarding their ability to accurately apprehend visual details, that is, in performing detailed captioning.
Multitask Vision-Language Prompt Tuning
Specifically, (i) we demonstrate the effectiveness of learning a single transferable prompt from multiple source tasks to initialize the prompt for each target task; (ii) we show many target tasks can benefit each other from sharing prompt vectors and thus can be jointly learned via multitask prompt tuning.
Image2Point: 3D Point-Cloud Understanding with 2D Image Pretrained Models
We discover that we can indeed use the same architecture and pretrained weights of a neural net model to understand both images and point-clouds.
Integer-only Zero-shot Quantization for Efficient Speech Recognition
End-to-end neural network models achieve improved performance on various automatic speech recognition (ASR) tasks.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
SqueezeWave: Extremely Lightweight Vocoders for On-device Speech Synthesis
Automatic speech synthesis is a challenging task that is becoming increasingly important as edge devices begin to interact with users through speech.
Sound Audio and Speech Processing
