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Found 17 papers, 8 papers with code
Model as a Game: On Numerical and Spatial Consistency for Generative Games
Recent advances in generative models have significantly impacted game generation.
AvatarArtist: Open-Domain 4D Avatarization
This work focuses on open-domain 4D avatarization, with the purpose of creating a 4D avatar from a portrait image in an arbitrary style.
Large Motion Video Autoencoding with Cross-modal Video VAE
Directly applying image VAEs to individual frames in isolation can result in temporal inconsistencies and suboptimal compression rates due to a lack of temporal compression.
TALE: Training-free Cross-domain Image Composition via Adaptive Latent Manipulation and Energy-guided Optimization
We present TALE, a novel training-free framework harnessing the generative capabilities of text-to-image diffusion models to address the cross-domain image composition task that focuses on flawlessly incorporating user-specified objects into a designated visual contexts regardless of domain disparity.
LLMs Meet Multimodal Generation and Editing: A Survey
With the recent advancement in large language models (LLMs), there is a growing interest in combining LLMs with multimodal learning.
TextDiffuser-2: Unleashing the Power of Language Models for Text Rendering
The diffusion model has been proven a powerful generative model in recent years, yet remains a challenge in generating visual text.
Ranked #6 on
Image Generation
on TextAtlasEval
KOSMOS-2.5: A Multimodal Literate Model
In this paper we present KOSMOS-2. 5, a multimodal literate model for machine reading of text-intensive images.
TextDiffuser: Diffusion Models as Text Painters
Diffusion models have gained increasing attention for their impressive generation abilities but currently struggle with rendering accurate and coherent text.
Chinese Character Recognition with Radical-Structured Stroke Trees
In this paper, we represent each Chinese character as a stroke tree, which is organized according to its radical structures, to fully exploit the merits of both radical and stroke levels in a decent way.
XDoc: Unified Pre-training for Cross-Format Document Understanding
The surge of pre-training has witnessed the rapid development of document understanding recently.
Ranked #8 on
Semantic entity labeling
on FUNSD
Benchmarking Chinese Text Recognition: Datasets, Baselines, and an Empirical Study
The experimental results indicate that the performance of baselines on CTR datasets is not as good as that on English datasets due to the characteristics of Chinese texts that are quite different from the Latin alphabet.
Text Gestalt: Stroke-Aware Scene Text Image Super-Resolution
However, the recognition of low-resolution scene text images remains a challenge.
MT-TransUNet: Mediating Multi-Task Tokens in Transformers for Skin Lesion Segmentation and Classification
However, these approaches formulated skin cancer diagnosis as a simple classification task, dismissing the potential benefit from lesion segmentation.
TrOCR: Transformer-based Optical Character Recognition with Pre-trained Models
Text recognition is a long-standing research problem for document digitalization.
Ranked #1 on
Handwritten Text Recognition
on IAM(line-level)
(using extra training data)
Zero-Shot Chinese Character Recognition with Stroke-Level Decomposition
Inspired by the fact that humans can generalize to know how to write characters unseen before if they have learned stroke orders of some characters, we propose a stroke-based method by decomposing each character into a sequence of strokes, which are the most basic units of Chinese characters.
Scene Text Telescope: Text-Focused Scene Image Super-Resolution
Image super-resolution, which is often regarded as a preprocessing procedure of scene text recognition, aims to recover the realistic features from a low-resolution text image.
Image Super-Resolution
Optical Character Recognition (OCR)
+2
Towards Brain-inspired System: Deep Recurrent Reinforcement Learning for Simulated Self-driving Agent
Because of the tremendous research that focuses on human brains and reinforcement learning, scientists have investigated how robots can autonomously tackle complex tasks in the form of a self-driving agent control in a human-like way.
