Trending Research
DocRes: A Generalist Model Toward Unifying Document Image Restoration Tasks
This underscores the potential of DocRes across a broader spectrum of document image restoration tasks.
CLLMs: Consistency Large Language Models
hao-ai-lab/Consistency_LLM
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Parallel decoding methods such as Jacobi decoding show promise for more efficient LLM inference as it breaks the sequential nature of the LLM decoding process and transforms it into parallelizable computation.
PuLID: Pure and Lightning ID Customization via Contrastive Alignment
We propose Pure and Lightning ID customization (PuLID), a novel tuning-free ID customization method for text-to-image generation.
AniTalker: Animate Vivid and Diverse Talking Faces through Identity-Decoupled Facial Motion Encoding
The paper introduces AniTalker, an innovative framework designed to generate lifelike talking faces from a single portrait.
VILA: On Pre-training for Visual Language Models
efficient-large-model/vila
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Visual language models (VLMs) rapidly progressed with the recent success of large language models.
Ranked #24 on
Visual Question Answering
on MM-Vet
AgentScope: A Flexible yet Robust Multi-Agent Platform
With the rapid advancement of Large Language Models (LLMs), significant progress has been made in multi-agent applications.
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
Visual Question Answering
on MM-Vet
AutoWebGLM: Bootstrap And Reinforce A Large Language Model-based Web Navigating Agent
Large language models (LLMs) have fueled many intelligent agent tasks, such as web navigation -- but most existing agents perform far from satisfying in real-world webpages due to three factors: (1) the versatility of actions on webpages, (2) HTML text exceeding model processing capacity, and (3) the complexity of decision-making due to the open-domain nature of web.
LLM2Vec: Large Language Models Are Secretly Powerful Text Encoders
We outperform encoder-only models by a large margin on word-level tasks and reach a new unsupervised state-of-the-art performance on the Massive Text Embeddings Benchmark (MTEB).
AM-RADIO: Agglomerative Vision Foundation Model -- Reduce All Domains Into One
nvlabs/radio
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A handful of visual foundation models (VFMs) have recently emerged as the backbones for numerous downstream tasks.
