Trending Research
LLaVA-UHD: an LMM Perceiving Any Aspect Ratio and High-Resolution Images
To address the challenges, we present LLaVA-UHD, a large multimodal model that can efficiently perceive images in any aspect ratio and high resolution.
LightAutoML: AutoML Solution for a Large Financial Services Ecosystem
We present an AutoML system called LightAutoML developed for a large European financial services company and its ecosystem satisfying the set of idiosyncratic requirements that this ecosystem has for AutoML solutions.
MoRA: High-Rank Updating for Parameter-Efficient Fine-Tuning
kongds/mora • 
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Low-rank adaptation is a popular parameter-efficient fine-tuning method for large language models.
Diffusion for World Modeling: Visual Details Matter in Atari
Motivated by this paradigm shift, we introduce DIAMOND (DIffusion As a Model Of eNvironment Dreams), a reinforcement learning agent trained in a diffusion world model.
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
Hunyuan-DiT: A Powerful Multi-Resolution Diffusion Transformer with Fine-Grained Chinese Understanding
For fine-grained language understanding, we train a Multimodal Large Language Model to refine the captions of the images.
Retrieval-Augmented Generation for AI-Generated Content: A Survey
We first classify RAG foundations according to how the retriever augments the generator, distilling the fundamental abstractions of the augmentation methodologies for various retrievers and generators.
Grounding DINO 1.5: Advance the "Edge" of Open-Set Object Detection
Empirical results demonstrate the effectiveness of Grounding DINO 1. 5, with the Grounding DINO 1. 5 Pro model attaining a 54. 3 AP on the COCO detection benchmark and a 55. 7 AP on the LVIS-minival zero-shot transfer benchmark, setting new records for open-set object detection.
Ranked #1 on Zero-Shot Object Detection on LVIS v1.0 val (using extra training data)
A decoder-only foundation model for time-series forecasting
Motivated by recent advances in large language models for Natural Language Processing (NLP), we design a time-series foundation model for forecasting whose out-of-the-box zero-shot performance on a variety of public datasets comes close to the accuracy of state-of-the-art supervised forecasting models for each individual dataset.
EasySpider: A No-Code Visual System for Crawling the Web
As such, web-crawling is an essential tool for both computational and non-computational scientists to conduct research.
