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Found 68 papers, 43 papers with code
ShowUI: One Vision-Language-Action Model for GUI Visual Agent
In this work, we develop a vision-language-action model in digital world, namely ShowUI, which features the following innovations: (i) UI-Guided Visual Token Selection to reduce computational costs by formulating screenshots as an UI connected graph, adaptively identifying their redundant relationship and serve as the criteria for token selection during self-attention blocks; (ii) Interleaved Vision-Language-Action Streaming that flexibly unifies diverse needs within GUI tasks, enabling effective management of visual-action history in navigation or pairing multi-turn query-action sequences per screenshot to enhance training efficiency; (iii) Small-scale High-quality GUI Instruction-following Datasets by careful data curation and employing a resampling strategy to address significant data type imbalances.
LiVOS: Light Video Object Segmentation with Gated Linear Matching
Semi-supervised video object segmentation (VOS) has been largely driven by space-time memory (STM) networks, which store past frame features in a spatiotemporal memory to segment the current frame via softmax attention.
Semantic Segmentation
Semi-Supervised Video Object Segmentation
+1
GenXD: Generating Any 3D and 4D Scenes
Due to the lack of real-world 4D data in the community, we first propose a data curation pipeline to obtain camera poses and object motion strength from videos.
SlowFast-VGen: Slow-Fast Learning for Action-Driven Long Video Generation
Our approach incorporates a masked conditional video diffusion model for the slow learning of world dynamics, alongside an inference-time fast learning strategy based on a temporal LoRA module.
MMIE: Massive Multimodal Interleaved Comprehension Benchmark for Large Vision-Language Models
Extensive experiments demonstrate the effectiveness of our benchmark and metrics in providing a comprehensive evaluation of interleaved LVLMs.
EditRoom: LLM-parameterized Graph Diffusion for Composable 3D Room Layout Editing
Given the steep learning curve of professional 3D software and the time-consuming process of managing large 3D assets, language-guided 3D scene editing has significant potential in fields such as virtual reality, augmented reality, and gaming.
MM-Vet v2: A Challenging Benchmark to Evaluate Large Multimodal Models for Integrated Capabilities
Using MM-Vet v2 to benchmark large multimodal models, we found that Claude 3. 5 Sonnet is the best model with a score of 71. 8, slightly outperforming GPT-4o which scored 71. 0.
IDOL: Unified Dual-Modal Latent Diffusion for Human-Centric Joint Video-Depth Generation
First, to enable dual-modal generation and maximize the information exchange between video and depth generation, we propose a unified dual-modal U-Net, a parameter-sharing framework for joint video and depth denoising, wherein a modality label guides the denoising target, and cross-modal attention enables the mutual information flow.
Certainly Uncertain: A Benchmark and Metric for Multimodal Epistemic and Aleatoric Awareness
The ability to acknowledge the inevitable uncertainty in their knowledge and reasoning is a prerequisite for AI systems to be truly truthful and reliable.
VideoGUI: A Benchmark for GUI Automation from Instructional Videos
Graphical User Interface (GUI) automation holds significant promise for enhancing human productivity by assisting with computer tasks.
MMWorld: Towards Multi-discipline Multi-faceted World Model Evaluation in Videos
Multimodal Language Language Models (MLLMs) demonstrate the emerging abilities of "world models" -- interpreting and reasoning about complex real-world dynamics.
Motion Consistency Model: Accelerating Video Diffusion with Disentangled Motion-Appearance Distillation
Extensive experiments show that our MCM achieves the state-of-the-art video diffusion distillation performance.
Leveraging Visual Tokens for Extended Text Contexts in Multi-Modal Learning
Training models with longer in-context lengths is a significant challenge for multimodal model due to substantial GPU memory and computational costs.
List Items One by One: A New Data Source and Learning Paradigm for Multimodal LLMs
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 #104 on
Visual Question Answering
on MM-Vet
Entity6K: A Large Open-Domain Evaluation Dataset for Real-World Entity Recognition
Open-domain real-world entity recognition is essential yet challenging, involving identifying various entities in diverse environments.
TaE: Task-aware Expandable Representation for Long Tail Class Incremental Learning
Existing methods mainly focus on preserving representative samples from previous classes to combat catastrophic forgetting.
Bring Metric Functions into Diffusion Models
The first module, similar to a standard DDPM, learns to predict the added noise and is unaffected by the metric function.
COSMO: COntrastive Streamlined MultimOdal Model with Interleaved Pre-Training
\ModelName, our unified framework, merges unimodal and multimodal elements, enhancing model performance for tasks involving textual and visual data while notably reducing learnable parameters.
Interfacing Foundation Models' Embeddings
To further unleash the power of foundation models, we present FIND, a generalized interface for aligning foundation models' embeddings with unified image and dataset-level understanding spanning modality and granularity.
MM-Narrator: Narrating Long-form Videos with Multimodal In-Context Learning
We present MM-Narrator, a novel system leveraging GPT-4 with multimodal in-context learning for the generation of audio descriptions (AD).
GPT-4V in Wonderland: Large Multimodal Models for Zero-Shot Smartphone GUI Navigation
We first benchmark MM-Navigator on our collected iOS screen dataset.
The Generative AI Paradox: "What It Can Create, It May Not Understand"
Specifically, we propose and test the Generative AI Paradox hypothesis: generative models, having been trained directly to reproduce expert-like outputs, acquire generative capabilities that are not contingent upon -- and can therefore exceed -- their ability to understand those same types of outputs.
MM-VID: Advancing Video Understanding with GPT-4V(ision)
We present MM-VID, an integrated system that harnesses the capabilities of GPT-4V, combined with specialized tools in vision, audio, and speech, to facilitate advanced video understanding.
DEsignBench: Exploring and Benchmarking DALL-E 3 for Imagining Visual Design
For DEsignBench benchmarking, we perform human evaluations on generated images in DEsignBench gallery, against the criteria of image-text alignment, visual aesthetic, and design creativity.
Idea2Img: Iterative Self-Refinement with GPT-4V(ision) for Automatic Image Design and Generation
We introduce ``Idea to Image,'' a system that enables multimodal iterative self-refinement with GPT-4V(ision) for automatic image design and generation.
OpenLEAF: Open-Domain Interleaved Image-Text Generation and Evaluation
We hope our proposed framework, benchmark, and LMM evaluation could help establish the intriguing interleaved image-text generation task.
The Dawn of LMMs: Preliminary Explorations with GPT-4V(ision)
We hope that this preliminary exploration will inspire future research on the next-generation multimodal task formulation, new ways to exploit and enhance LMMs to solve real-world problems, and gaining better understanding of multimodal foundation models.
Ranked #3 on
MMR total
on MRR-Benchmark
(using extra training data)
Multimodal Foundation Models: From Specialists to General-Purpose Assistants
This paper presents a comprehensive survey of the taxonomy and evolution of multimodal foundation models that demonstrate vision and vision-language capabilities, focusing on the transition from specialist models to general-purpose assistants.
MM-Vet: Evaluating Large Multimodal Models for Integrated Capabilities
Problems include: (1) How to systematically structure and evaluate the complicated multimodal tasks; (2) How to design evaluation metrics that work well across question and answer types; and (3) How to give model insights beyond a simple performance ranking.
Spatial-Frequency U-Net for Denoising Diffusion Probabilistic Models
In this paper, we study the denoising diffusion probabilistic model (DDPM) in wavelet space, instead of pixel space, for visual synthesis.
DisCo: Disentangled Control for Realistic Human Dance Generation
In this paper, we depart from the traditional paradigm of human motion transfer and emphasize two additional critical attributes for the synthesis of human dance content in social media contexts: (i) Generalizability: the model should be able to generalize beyond generic human viewpoints as well as unseen human subjects, backgrounds, and poses; (ii) Compositionality: it should allow for the seamless composition of seen/unseen subjects, backgrounds, and poses from different sources.
Mitigating Hallucination in Large Multi-Modal Models via Robust Instruction Tuning
To efficiently measure the hallucination generated by LMMs, we propose GPT4-Assisted Visual Instruction Evaluation (GAVIE), a stable approach to evaluate visual instruction tuning like human experts.
Ranked #3 on
Visual Question Answering (VQA)
on HallusionBench
MMSum: A Dataset for Multimodal Summarization and Thumbnail Generation of Videos
To address these challenges and provide a comprehensive dataset for this new direction, we have meticulously curated the \textbf{MMSum} dataset.
An Empirical Study of Multimodal Model Merging
In this paper, we expand on this concept to a multimodal setup by merging transformers trained on different modalities.
Diagnostic Benchmark and Iterative Inpainting for Layout-Guided Image Generation
In this paper, we propose LayoutBench, a diagnostic benchmark for layout-guided image generation that examines four categories of spatial control skills: number, position, size, and shape.
Segment Everything Everywhere All at Once
In SEEM, we propose a novel decoding mechanism that enables diverse prompting for all types of segmentation tasks, aiming at a universal segmentation interface that behaves like large language models (LLMs).
Adaptive Human Matting for Dynamic Videos
The most recent efforts in video matting have focused on eliminating trimap dependency since trimap annotations are expensive and trimap-based methods are less adaptable for real-time applications.
Equivariant Similarity for Vision-Language Foundation Models
Unlike the existing image-text similarity objective which only categorizes matched pairs as similar and unmatched pairs as dissimilar, equivariance also requires similarity to vary faithfully according to the semantic changes.
Ranked #7 on
Visual Reasoning
on Winoground
NUWA-XL: Diffusion over Diffusion for eXtremely Long Video Generation
In this paper, we propose NUWA-XL, a novel Diffusion over Diffusion architecture for eXtremely Long video generation.
MM-REACT: Prompting ChatGPT for Multimodal Reasoning and Action
We propose MM-REACT, a system paradigm that integrates ChatGPT with a pool of vision experts to achieve multimodal reasoning and action.
Ranked #62 on
Visual Question Answering
on MM-Vet
Learning 3D Photography Videos via Self-supervised Diffusion on Single Images
3D photography renders a static image into a video with appealing 3D visual effects.
Ranked #1 on
Image Outpainting
on MSCOCO
Generalized Decoding for Pixel, Image, and Language
We present X-Decoder, a generalized decoding model that can predict pixel-level segmentation and language tokens seamlessly.
Ranked #4 on
Instance Segmentation
on ADE20K val
(using extra training data)
ReCo: Region-Controlled Text-to-Image Generation
Human evaluation on PaintSkill shows that ReCo is +19. 28% and +17. 21% more accurate in generating images with correct object count and spatial relationship than the T2I model.
Conditional Text-to-Image Synthesis
Layout-to-Image Generation
+1
Vision-Language Pre-training: Basics, Recent Advances, and Future Trends
This paper surveys vision-language pre-training (VLP) methods for multimodal intelligence that have been developed in the last few years.
An Empirical Study of End-to-End Video-Language Transformers with Masked Visual Modeling
Masked visual modeling (MVM) has been recently proven effective for visual pre-training.
Ranked #1 on
Video Question Answering
on LSMDC-MC
Coarse-to-Fine Vision-Language Pre-training with Fusion in the Backbone
Vision-language (VL) pre-training has recently received considerable attention.
Ranked #1 on
Phrase Grounding
on Flickr30k Entities Dev
LAVENDER: Unifying Video-Language Understanding as Masked Language Modeling
In this work, we explore a unified VidL framework LAVENDER, where Masked Language Modeling (MLM) is used as the common interface for all pre-training and downstream tasks.
GIT: A Generative Image-to-text Transformer for Vision and Language
In this paper, we design and train a Generative Image-to-text Transformer, GIT, to unify vision-language tasks such as image/video captioning and question answering.
Ranked #1 on
Image Captioning
on nocaps-XD near-domain
Cross-modal Representation Learning for Zero-shot Action Recognition
The model design provides a natural mechanism for visual and semantic representations to be learned in a shared knowledge space, whereby it encourages the learned visual embedding to be discriminative and more semantically consistent.
Ranked #4 on
Zero-Shot Action Recognition
on ActivityNet
MLP Architectures for Vision-and-Language Modeling: An Empirical Study
Based on this, we ask an even bolder question: can we have an all-MLP architecture for VL modeling, where both VL fusion and the vision encoder are replaced with MLPs?
SwinBERT: End-to-End Transformers with Sparse Attention for Video Captioning
Based on this model architecture, we show that video captioning can benefit significantly from more densely sampled video frames as opposed to previous successes with sparsely sampled video frames for video-and-language understanding tasks (e. g., video question answering).
VIOLET : End-to-End Video-Language Transformers with Masked Visual-token Modeling
Further, unlike previous studies that found pre-training tasks on video inputs (e. g., masked frame modeling) not very effective, we design a new pre-training task, Masked Visual-token Modeling (MVM), for better video modeling.
Ranked #20 on
Zero-Shot Video Retrieval
on DiDeMo
VALUE: A Multi-Task Benchmark for Video-and-Language Understanding Evaluation
Most existing video-and-language (VidL) research focuses on a single dataset, or multiple datasets of a single task.
Adversarial VQA: A New Benchmark for Evaluating the Robustness of VQA Models
We hope our Adversarial VQA dataset can shed new light on robustness study in the community and serve as a valuable benchmark for future work.
Playing Lottery Tickets with Vision and Language
However, we can find "relaxed" winning tickets at 50%-70% sparsity that maintain 99% of the full accuracy.
UC2: Universal Cross-lingual Cross-modal Vision-and-Language Pre-training
Vision-and-language pre-training has achieved impressive success in learning multimodal representations between vision and language.
LightningDOT: Pre-training Visual-Semantic Embeddings for Real-Time Image-Text Retrieval
Multimodal pre-training has propelled great advancement in vision-and-language research.
Less is More: ClipBERT for Video-and-Language Learning via Sparse Sampling
Experiments on text-to-video retrieval and video question answering on six datasets demonstrate that ClipBERT outperforms (or is on par with) existing methods that exploit full-length videos, suggesting that end-to-end learning with just a few sparsely sampled clips is often more accurate than using densely extracted offline features from full-length videos, proving the proverbial less-is-more principle.
Ranked #27 on
Visual Question Answering (VQA)
on MSRVTT-QA
(using extra training data)
A Closer Look at the Robustness of Vision-and-Language Pre-trained Models
Large-scale pre-trained multimodal transformers, such as ViLBERT and UNITER, have propelled the state of the art in vision-and-language (V+L) research to a new level.
Graph Optimal Transport for Cross-Domain Alignment
In GOT, cross-domain alignment is formulated as a graph matching problem, by representing entities into a dynamically-constructed graph.
Large-Scale Adversarial Training for Vision-and-Language Representation Learning
We present VILLA, the first known effort on large-scale adversarial training for vision-and-language (V+L) representation learning.
Ranked #7 on
Visual Entailment
on SNLI-VE val
(using extra training data)
HERO: Hierarchical Encoder for Video+Language Omni-representation Pre-training
We present HERO, a novel framework for large-scale video+language omni-representation learning.
Ranked #1 on
Video Retrieval
on TVR
Meta Module Network for Compositional Visual Reasoning
To design a more powerful NMN architecture for practical use, we propose Meta Module Network (MMN) centered on a novel meta module, which can take in function recipes and morph into diverse instance modules dynamically.
UNITER: Learning UNiversal Image-TExt Representations
Joint image-text embedding is the bedrock for most Vision-and-Language (V+L) tasks, where multimodality inputs are jointly processed for visual and textual understanding.
UNITER: UNiversal Image-TExt Representation Learning
Different from previous work that applies joint random masking to both modalities, we use conditional masking on pre-training tasks (i. e., masked language/region modeling is conditioned on full observation of image/text).
Ranked #3 on
Visual Question Answering (VQA)
on VCR (Q-A) test
Relation-Aware Graph Attention Network for Visual Question Answering
In order to answer semantically-complicated questions about an image, a Visual Question Answering (VQA) model needs to fully understand the visual scene in the image, especially the interactive dynamics between different objects.
Multi-step Reasoning via Recurrent Dual Attention for Visual Dialog
This paper presents a new model for visual dialog, Recurrent Dual Attention Network (ReDAN), using multi-step reasoning to answer a series of questions about an image.
Learning to see people like people
Humans make complex inferences on faces, ranging from objective properties (gender, ethnicity, expression, age, identity, etc) to subjective judgments (facial attractiveness, trustworthiness, sociability, friendliness, etc).
