Trending Research
SEED-X: Multimodal Models with Unified Multi-granularity Comprehension and Generation
ailab-cvc/seed-x
•
•
We hope that our work will inspire future research into what can be achieved by versatile multimodal foundation models in real-world applications.
Learning Visuotactile Skills with Two Multifingered Hands
ToruOwO/hato
•
•
Two significant challenges exist: the lack of an affordable and accessible teleoperation system suitable for a dual-arm setup with multifingered hands, and the scarcity of multifingered hand hardware equipped with touch sensing.
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.
Rethinking Inductive Biases for Surface Normal Estimation
Despite the growing demand for accurate surface normal estimation models, existing methods use general-purpose dense prediction models, adopting the same inductive biases as other tasks.
SnapKV: LLM Knows What You are Looking for Before Generation
Specifically, SnapKV achieves a consistent decoding speed with a 3. 6x increase in generation speed and an 8. 2x enhancement in memory efficiency compared to baseline when processing inputs of 16K tokens.
Dynamic Generation of Personalities with Large Language Models
We propose a new metric to assess personality generation capability based on this evaluation method.
ESRL: Efficient Sampling-based Reinforcement Learning for Sequence Generation
hiyouga/llama-efficient-tuning
•
•
Applying Reinforcement Learning (RL) to sequence generation models enables the direct optimization of long-term rewards (\textit{e. g.,} BLEU and human feedback), but typically requires large-scale sampling over a space of action sequences.
Visual Autoregressive Modeling: Scalable Image Generation via Next-Scale Prediction
We present Visual AutoRegressive modeling (VAR), a new generation paradigm that redefines the autoregressive learning on images as coarse-to-fine "next-scale prediction" or "next-resolution prediction", diverging from the standard raster-scan "next-token prediction".
Ranked #7 on
Image Generation
on ImageNet 256x256
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
List Items One by One: A New Data Source and Learning Paradigm for Multimodal LLMs
zzxslp/som-llava
•
•
Set-of-Mark (SoM) Prompting unleashes the visual grounding capability of GPT-4V, by enabling the model to associate visual objects with tags inserted on the image.
Ranked #47 on
Visual Question Answering
on MM-Vet
