Search Results for author:
Found 11 papers, 6 papers with code
TextMonkey: An OCR-Free Large Multimodal Model for Understanding Document
We present TextMonkey, a large multimodal model (LMM) tailored for text-centric tasks.
Sequential Visual and Semantic Consistency for Semi-supervised Text Recognition
Scene text recognition (STR) is a challenging task that requires large-scale annotated data for training.
Class-Aware Mask-Guided Feature Refinement for Scene Text Recognition
By enhancing the alignment between the canonical mask feature and the text feature, the module ensures more effective fusion, ultimately leading to improved recognition performance.
Monkey: Image Resolution and Text Label Are Important Things for Large Multi-modal Models
Additionally, experiments on 18 datasets further demonstrate that Monkey surpasses existing LMMs in many tasks like Image Captioning and various Visual Question Answering formats.
Looking and Listening: Audio Guided Text Recognition
Text recognition in the wild is a long-standing problem in computer vision.
On the Hidden Mystery of OCR in Large Multimodal Models
In this paper, we conducted a comprehensive evaluation of Large Multimodal Models, such as GPT4V and Gemini, in various text-related visual tasks including Text Recognition, Scene Text-Centric Visual Question Answering (VQA), Document-Oriented VQA, Key Information Extraction (KIE), and Handwritten Mathematical Expression Recognition (HMER).
Feature Affinity Assisted Knowledge Distillation and Quantization of Deep Neural Networks on Label-Free Data
In this paper, we propose a feature affinity (FA) assisted knowledge distillation (KD) method to improve quantization-aware training of deep neural networks (DNN).
Searching Intrinsic Dimensions of Vision Transformers
It has been shown by many researchers that transformers perform as well as convolutional neural networks in many computer vision tasks.
NTIRE 2020 Challenge on Real-World Image Super-Resolution: Methods and Results
This paper reviews the NTIRE 2020 challenge on real world super-resolution.
TPPO: A Novel Trajectory Predictor with Pseudo Oracle
A latent variable predictor is proposed to estimate latent variable distributions from observed and ground-truth trajectories.
A Novel Graph based Trajectory Predictor with Pseudo Oracle
Recent studies focus on modeling pedestrians' motion patterns with recurrent neural networks, capturing social interactions with pooling-based or graph-based methods, and handling future uncertainties by using random Gaussian noise as the latent variable.
