Search Results for author:
Found 32 papers, 16 papers with code
Seed1.5-VL Technical Report
We present Seed1. 5-VL, a vision-language foundation model designed to advance general-purpose multimodal understanding and reasoning.
Ranked #1 on
Video Question Answering
on OVBench
Classification Done Right for Vision-Language Pre-Training
Due to the absence of the text encoding as contrastive target, SuperClass does not require a text encoder and does not need to maintain a large batch size as CLIP does.
LLaVA-Critic: Learning to Evaluate Multimodal Models
We introduce LLaVA-Critic, the first open-source large multimodal model (LMM) designed as a generalist evaluator to assess performance across a wide range of multimodal tasks.
MJ-Bench: Is Your Multimodal Reward Model Really a Good Judge for Text-to-Image Generation?
Compared with open-source VLMs, smaller-sized scoring models can provide better feedback regarding text-image alignment and image quality, while VLMs provide more accurate feedback regarding safety and generation bias due to their stronger reasoning capabilities.
Semantics-enhanced Cross-modal Masked Image Modeling for Vision-Language Pre-training
In vision-language pre-training (VLP), masked image modeling (MIM) has recently been introduced for fine-grained cross-modal alignment.
Unifying Latent and Lexicon Representations for Effective Video-Text Retrieval
In this work, we propose the UNIFY framework, which learns lexicon representations to capture fine-grained semantics and combines the strengths of latent and lexicon representations for video-text retrieval.
TiMix: Text-aware Image Mixing for Effective Vision-Language Pre-training
Self-supervised Multi-modal Contrastive Learning (SMCL) remarkably advances modern Vision-Language Pre-training (VLP) models by aligning visual and linguistic modalities.
Hallucination Augmented Contrastive Learning for Multimodal Large Language Model
We first analyzed the representation distribution of textual and visual tokens in MLLM, revealing two important findings: 1) there is a significant gap between textual and visual representations, indicating unsatisfactory cross-modal representation alignment; 2) representations of texts that contain and do not contain hallucinations are entangled, making it challenging to distinguish them.
Ranked #205 on
Visual Question Answering
on MM-Vet
mPLUG-PaperOwl: Scientific Diagram Analysis with the Multimodal Large Language Model
In this work, towards a more versatile copilot for academic paper writing, we mainly focus on strengthening the multi-modal diagram analysis ability of Multimodal LLMs.
mPLUG-Owl2: Revolutionizing Multi-modal Large Language Model with Modality Collaboration
Multi-modal Large Language Models (MLLMs) have demonstrated impressive instruction abilities across various open-ended tasks.
Ranked #10 on
Long-Context Understanding
on MMNeedle
UReader: Universal OCR-free Visually-situated Language Understanding with Multimodal Large Language Model
Text is ubiquitous in our visual world, conveying crucial information, such as in documents, websites, and everyday photographs.
Evaluation and Analysis of Hallucination in Large Vision-Language Models
In this paper, we propose Hallucination Evaluation based on Large Language Models (HaELM), an LLM-based hallucination evaluation framework.
BUS:Efficient and Effective Vision-language Pre-training with Bottom-Up Patch Summarization
Specifically, We incorporate a Text-Semantics-Aware Patch Selector (TSPS) into the ViT backbone to perform a coarse-grained visual token extraction and then attach a flexible Transformer-based Patch Abstraction Decoder (PAD) upon the backbone for top-level visual abstraction.
mPLUG-DocOwl: Modularized Multimodal Large Language Model for Document Understanding
Nevertheless, without in-domain training, these models tend to ignore fine-grained OCR features, such as sophisticated tables or large blocks of text, which are essential for OCR-free document understanding.
Youku-mPLUG: A 10 Million Large-scale Chinese Video-Language Dataset for Pre-training and Benchmarks
In addition, to facilitate a comprehensive evaluation of video-language models, we carefully build the largest human-annotated Chinese benchmarks covering three popular video-language tasks of cross-modal retrieval, video captioning, and video category classification.
mPLUG-Owl: Modularization Empowers Large Language Models with Multimodality
Our code, pre-trained model, instruction-tuned models, and evaluation set are available at https://github. com/X-PLUG/mPLUG-Owl.
Ranked #1 on
Visual Question Answering (VQA)
on HallusionBench
(Question Pair Acc metric)
Visual Question Answering (VQA)
Zero-Shot Video Question Answer
ChatPLUG: Open-Domain Generative Dialogue System with Internet-Augmented Instruction Tuning for Digital Human
In this paper, we present ChatPLUG, a Chinese open-domain dialogue system for digital human applications that instruction finetunes on a wide range of dialogue tasks in a unified internet-augmented format.
mPLUG-2: A Modularized Multi-modal Foundation Model Across Text, Image and Video
In contrast to predominant paradigms of solely relying on sequence-to-sequence generation or encoder-based instance discrimination, mPLUG-2 introduces a multi-module composition network by sharing common universal modules for modality collaboration and disentangling different modality modules to deal with modality entanglement.
Ranked #1 on
Video Captioning
on MSR-VTT
Learning Trajectory-Word Alignments for Video-Language Tasks
To amend this, we propose a novel TW-BERT to learn Trajectory-Word alignment by a newly designed trajectory-to-word (T2W) attention for solving video-language tasks.
BUS: Efficient and Effective Vision-Language Pre-Training with Bottom-Up Patch Summarization.
In this paper, we propose a Bottom-Up Patch Summarization approach named BUS which is inspired by the Document Summarization Task in NLP to learn a concise visual summary of lengthy visual token sequences, guided by textual semantics.
HiTeA: Hierarchical Temporal-Aware Video-Language Pre-training
We achieve state-of-the-art results on 15 well-established video-language understanding and generation tasks, especially on temporal-oriented datasets (e. g., SSv2-Template and SSv2-Label) with 8. 6% and 11. 1% improvement respectively.
Ranked #1 on
Visual Question Answering (VQA)
on TGIF-QA
All grains, one scheme (AGOS): Learning multigrain instance representation for aerial scene classification
Finally, our SSF module allows our framework to learn the same scene scheme from multigrain instance representations and fuses them, so that the entire framework is optimized as a whole.
All Grains, One Scheme (AGOS): Learning Multi-grain Instance Representation for Aerial Scene Classification
Finally, our SSF allows our framework to learn the same scene scheme from multi-grain instance representations and fuses them, so that the entire framework is optimized as a whole.
Ranked #1 on
Scene Recognition
on AID
AI-based Medical e-Diagnosis for Fast and Automatic Ventricular Volume Measurement in the Patients with Normal Pressure Hydrocephalus
Finally, we verify the reliability of the model and achieved automatic measurement of VV and ICV.
Exploring Global Diversity and Local Context for Video Summarization
By combining these two attention mechanisms, a video SUMmarization model with Diversified Contextual Attention scheme is developed, namely SUM-DCA.
Robust Weakly Supervised Learning for COVID-19 Recognition Using Multi-Center CT Images
The multi-domain shift problem for the multi-center and multi-scanner studies is therefore nontrivial that is also crucial for a dependable recognition and critical for reproducible and objective diagnosis and prognosis.
Explainable AI For COVID-19 CT Classifiers: An Initial Comparison Study
XAI is an AI model that is programmed to explain its goals, logic, and decision making so that the end users can understand.
Unbox the Black-box for the Medical Explainable AI via Multi-modal and Multi-centre Data Fusion: A Mini-Review, Two Showcases and Beyond
Explainable Artificial Intelligence (XAI) is an emerging research topic of machine learning aimed at unboxing how AI systems' black-box choices are made.
Temporal Cue Guided Video Highlight Detection With Low-Rank Audio-Visual Fusion
Video highlight detection plays an increasingly important role in social media content filtering, however, it remains highly challenging to develop automated video highlight detection methods because of the lack of temporal annotations (i. e., where the highlight moments are in long videos) for supervised learning.
Exploring global diverse attention via pairwise temporal relation for video summarization
In this paper, we propose an efficient convolutional neural network architecture for video SUMmarization via Global Diverse Attention called SUM-GDA, which adapts attention mechanism in a global perspective to consider pairwise temporal relations of video frames.
Application of Time Series Analysis to Traffic Accidents in Los Angeles
The primary objective of this paper is to apply a set of methods for the time series analysis of traffic accidents in Los Angeles in the past few years.
