Trending Research
MonkeyOCR: Document Parsing with a Structure-Recognition-Relation Triplet Paradigm
We introduce MonkeyOCR, a vision-language model for document parsing that advances the state of the art by leveraging a Structure-Recognition-Relation (SRR) triplet paradigm.
WebDancer: Towards Autonomous Information Seeking Agency
We instantiate this framework in a web agent based on the ReAct, WebDancer.
ThinkSound: Chain-of-Thought Reasoning in Multimodal Large Language Models for Audio Generation and Editing
While end-to-end video-to-audio generation has greatly improved, producing high-fidelity audio that authentically captures the nuances of visual content remains challenging.
Do Large Language Models Need a Content Delivery Network?
lmcache/lmcache
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As the use of large language models (LLMs) expands rapidly, so does the range of knowledge needed to supplement various LLM queries.
IndexTTS: An Industrial-Level Controllable and Efficient Zero-Shot Text-To-Speech System
Recently, large language model (LLM) based text-to-speech (TTS) systems have gradually become the mainstream in the industry due to their high naturalness and powerful zero-shot voice cloning capabilities. Here, we introduce the IndexTTS system, which is mainly based on the XTTS and Tortoise model.
Universal Segmentation at Arbitrary Granularity with Language Instruction
yongliu20/UniLSeg
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CVPR 2024
This paper aims to achieve universal segmentation of arbitrary semantic level.
Ranked #1 on
Referring Expression Segmentation
on RefCOCOg-test
(using extra training data)
TradingAgents: Multi-Agents LLM Financial Trading Framework
Significant progress has been made in automated problem-solving using societies of agents powered by large language models (LLMs).
Learning Robust Stereo Matching in the Wild with Selective Mixture-of-Experts
To address this, we propose SMoEStereo, a novel framework that adapts VFMs for stereo matching through a tailored, scene-specific fusion of Low-Rank Adaptation (LoRA) and Mixture-of-Experts (MoE) modules.
Dolphin: Document Image Parsing via Heterogeneous Anchor Prompting
Document image parsing is challenging due to its complexly intertwined elements such as text paragraphs, figures, formulas, and tables.
