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Found 46 papers, 22 papers with code
mPLUG-DocOwl 1.5: Unified Structure Learning for OCR-free Document Understanding
In this work, we emphasize the importance of structure information in Visual Document Understanding and propose the Unified Structure Learning to boost the performance of MLLMs.
Bayesian Diffusion Models for 3D Shape Reconstruction
We present Bayesian Diffusion Models (BDM), a prediction algorithm that performs effective Bayesian inference by tightly coupling the top-down (prior) information with the bottom-up (data-driven) procedure via joint diffusion processes.
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.
Hal-Eval: A Universal and Fine-grained Hallucination Evaluation Framework for Large Vision Language Models
Large Vision Language Models exhibit remarkable capabilities but struggle with hallucinations inconsistencies between images and their descriptions.
Mobile-Agent: Autonomous Multi-Modal Mobile Device Agent with Visual Perception
To assess the performance of Mobile-Agent, we introduced Mobile-Eval, a benchmark for evaluating mobile device operations.
Efficient Vision-and-Language Pre-training with Text-Relevant Image Patch Selection
Vision Transformers (ViTs) have become increasingly popular in large-scale Vision and Language Pre-training (VLP) models.
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 #74 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.
AMBER: An LLM-free Multi-dimensional Benchmark for MLLMs Hallucination Evaluation
Despite making significant progress in multi-modal tasks, current Multi-modal Large Language Models (MLLMs) encounter the significant challenge of hallucinations, which may lead to harmful consequences.
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 #11 on
Visual Question Answering (VQA)
on InfiMM-Eval
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.
ModelScope-Agent: Building Your Customizable Agent System with Open-source Large Language Models
Large language models (LLMs) have recently demonstrated remarkable capabilities to comprehend human intentions, engage in reasoning, and design planning-like behavior.
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.
Vision Transformer with Attention Map Hallucination and FFN Compaction
Vision Transformer(ViT) is now dominating many vision tasks.
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.
Towards Adaptive Prefix Tuning for Parameter-Efficient Language Model Fine-tuning
In addition, taking the gate as a probing, we validate the efficiency and effectiveness of the variable prefix.
Vision Language Pre-training by Contrastive Learning with Cross-Modal Similarity Regulation
Cross-modal contrastive learning in vision language pretraining (VLP) faces the challenge of (partial) false negatives.
Transforming Visual Scene Graphs to Image Captions
In TSG, we apply multi-head attention (MHA) to design the Graph Neural Network (GNN) for embedding scene graphs.
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 #3 on
Visual Question Answering (VQA)
on HallusionBench
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.
Adaptively Clustering Neighbor Elements for Image Captioning
We design a novel global-local Transformer named \textbf{Ada-ClustFormer} (\textbf{ACF}) to generate captions.
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
mPLUG: Effective and Efficient Vision-Language Learning by Cross-modal Skip-connections
Large-scale pretrained foundation models have been an emerging paradigm for building artificial intelligence (AI) systems, which can be quickly adapted to a wide range of downstream tasks.
Ranked #1 on
Image Captioning
on COCO Captions
Image Captioning In the Transformer Age
This drawback inspires the researchers to develop a homogeneous architecture that facilitates end-to-end training, for which Transformer is the perfect one that has proven its huge potential in both vision and language domains and thus can be used as the basic component of the visual encoder and language decoder in an IC pipeline.
Achieving Human Parity on Visual Question Answering
The Visual Question Answering (VQA) task utilizes both visual image and language analysis to answer a textual question with respect to an image.
Ranked #7 on
Visual Question Answering (VQA)
on VQA v2 test-dev
EMScore: Evaluating Video Captioning via Coarse-Grained and Fine-Grained Embedding Matching
The datasets will be released to facilitate the development of video captioning metrics.
Grid-VLP: Revisiting Grid Features for Vision-Language Pre-training
Existing approaches to vision-language pre-training (VLP) heavily rely on an object detector based on bounding boxes (regions), where salient objects are first detected from images and then a Transformer-based model is used for cross-modal fusion.
E2E-VLP: End-to-End Vision-Language Pre-training Enhanced by Visual Learning
Vision-language pre-training (VLP) on large-scale image-text pairs has achieved huge success for the cross-modal downstream tasks.
We Know What You Want: An Advertising Strategy Recommender System for Online Advertising
To achieve this goal, the advertising platform needs to identify the advertiser's optimization objectives, and then recommend the corresponding strategies to fulfill the objectives.
SemVLP: Vision-Language Pre-training by Aligning Semantics at Multiple Levels
Vision-language pre-training (VLP) on large-scale image-text pairs has recently witnessed rapid progress for learning cross-modal representations.
Learning to Infer User Hidden States for Online Sequential Advertising
To drive purchase in online advertising, it is of the advertiser's great interest to optimize the sequential advertising strategy whose performance and interpretability are both important.
A Deep Prediction Network for Understanding Advertiser Intent and Satisfaction
For e-commerce platforms such as Taobao and Amazon, advertisers play an important role in the entire digital ecosystem: their behaviors explicitly influence users' browsing and shopping experience; more importantly, advertiser's expenditure on advertising constitutes a primary source of platform revenue.
Neural Topic Modeling with Bidirectional Adversarial Training
Recent years have witnessed a surge of interests of using neural topic models for automatic topic extraction from text, since they avoid the complicated mathematical derivations for model inference as in traditional topic models such as Latent Dirichlet Allocation (LDA).
Ranked #1 on
Text Clustering
on 20 Newsgroups
Adversarial Multi-Binary Neural Network for Multi-class Classification
Multi-class text classification is one of the key problems in machine learning and natural language processing.
Learning Syntactic and Dynamic Selective Encoding for Document Summarization
Our approach has the following contributions: first, we incorporate syntactic information such as constituency parsing trees into the encoding sequence to learn both the semantic and syntactic information from the document, resulting in more accurate summary; second, we propose a dynamic gate network to select the salient information based on the context of the decoder state, which is essential to document summarization.
Selective Attention Encoders by Syntactic Graph Convolutional Networks for Document Summarization
Abstractive text summarization is a challenging task, and one need to design a mechanism to effectively extract salient information from the source text and then generate a summary.
Learning Alignment for Multimodal Emotion Recognition from Speech
Further, emotion recognition will be beneficial from using audio-textual multimodal information, it is not trivial to build a system to learn from multimodality.
Multimodal Emotion Recognition
Speech Emotion Recognition
+2
DELTA: A DEep learning based Language Technology plAtform
In this paper we present DELTA, a deep learning based language technology platform.
Ranked #3 on
Text Classification
on Yahoo! Answers
