Trending Research
OneChart: Purify the Chart Structural Extraction via One Auxiliary Token
To address this, we propose OneChart: a reliable agent specifically devised for the structural extraction of chart information.
UFO: A UI-Focused Agent for Windows OS Interaction
We introduce UFO, an innovative UI-Focused agent to fulfill user requests tailored to applications on Windows OS, harnessing the capabilities of GPT-Vision.
HairFastGAN: Realistic and Robust Hair Transfer with a Fast Encoder-Based Approach
airi-institute/hairfastgan
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Our paper addresses the complex task of transferring a hairstyle from a reference image to an input photo for virtual hair try-on.
Attention Satisfies: A Constraint-Satisfaction Lens on Factual Errors of Language Models
microsoft/mechanistic-error-probe
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We investigate the internal behavior of Transformer-based Large Language Models (LLMs) when they generate factually incorrect text.
InternVL: Scaling up Vision Foundation Models and Aligning for Generic Visual-Linguistic Tasks
However, the progress in vision and vision-language foundation models, which are also critical elements of multi-modal AGI, has not kept pace with LLMs.
Ranked #1 on
Zero-Shot Video Retrieval
on MSR-VTT-full
(using extra training data)
SCEdit: Efficient and Controllable Image Diffusion Generation via Skip Connection Editing
modelscope/swift
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Image diffusion models have been utilized in various tasks, such as text-to-image generation and controllable image synthesis.
Show-1: Marrying Pixel and Latent Diffusion Models for Text-to-Video Generation
showlab/show-1
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In this paper, we are the first to propose a hybrid model, dubbed as Show-1, which marries pixel-based and latent-based VDMs for text-to-video generation.
Ranked #2 on
Text-to-Video Generation
on EvalCrafter Text-to-Video (ECTV) Dataset
(using extra training data)
LLoCO: Learning Long Contexts Offline
We introduce LLoCO, a technique that combines context compression, retrieval, and parameter-efficient finetuning using LoRA.
Prepacking: A Simple Method for Fast Prefilling and Increased Throughput in Large Language Models
In this work, we highlight the following pitfall of prefilling: for batches containing high-varying prompt lengths, significant computation is wasted by the standard practice of padding sequences to the maximum length.
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).
