1 citations · 1 across the 10 of their papers we have counts for
11 papers
WeAgent-MMGenEdit: A Full-Stack Recipe for Multimodal Agentic Image Generation and Editing
Hui Zhang, Zongkai Liu, Liqiang Niu +6
Image generation and editing models have advanced rapidly, yet remain unreliable when prompts require external world knowledge. Bounded and long-tail parametric knowledge prevents…
WeAgent-MMSearch: Native Text-Vision Interaction for Multimodal Search Agents
Zongkai Liu, Hui Zhang, Liqiang Niu +7
Multimodal search agents extend parametric knowledge with newly emerging and long-tail evidence from the open web. Yet many existing agentic search environments often expose retrie…
WeEdit: A Dataset, Benchmark and Glyph-Guided Framework for Text-centric Image Editing
Hui Zhang, Juntao Liu, Zongkai Liu +4
Instruction-based image editing aims to modify specific content within existing images according to user-provided instructions while preserving non-target regions. Beyond tradition…
ArrowGEV: Grounding Events in Video via Learning the Arrow of Time
Fangxu Yu, Ziyao Lu, Liqiang Niu +2
Grounding events in videos serves as a fundamental capability in video analysis. While Vision Language Models (VLMs) are increasingly employed for this task, existing approaches pr…
UME-R1: Exploring Reasoning-Driven Generative Multimodal Embeddings
Zhibin Lan, Liqiang Niu, Fandong Meng +2
The remarkable success of multimodal large language models (MLLMs) has driven advances in multimodal embeddings, yet existing models remain inherently discriminative, limiting thei…
LaCo: Efficient Layer-wise Compression of Visual Tokens for Multimodal Large Language Models
Juntao Liu, Liqiang Niu, Wenchao Chen +2
Existing visual token compression methods for Multimodal Large Language Models (MLLMs) predominantly operate as post-encoder modules, limiting their potential for efficiency gains.…
D2C: Unlocking the Potential of Continuous Autoregressive Image Generation with Discrete Tokens
Panpan Wang, Liqiang Niu, Fandong Meng +3
In the domain of image generation, latent-based generative models occupy a dominant status; however, these models rely heavily on image tokenizer. To meet modeling requirements, au…
LLaVE: Large Language and Vision Embedding Models with Hardness-Weighted Contrastive Learning
Zhibin Lan, Liqiang Niu, Fandong Meng +2
Universal multimodal embedding models play a critical role in tasks such as interleaved image-text retrieval, multimodal RAG, and multimodal clustering. However, our empirical resu…
AVG-LLaVA: An Efficient Large Multimodal Model with Adaptive Visual Granularity
Zhibin Lan, Liqiang Niu, Fandong Meng +3
Recently, large multimodal models (LMMs) have achieved significant advancements. When dealing with high-resolution images, dominant LMMs typically divide them into multiple local i…
MaskMamba: A Hybrid Mamba-Transformer Model for Masked Image Generation
Wenchao Chen, Liqiang Niu, Ziyao Lu +2
Image generation models have encountered challenges related to scalability and quadratic complexity, primarily due to the reliance on Transformer-based backbones. In this study, we…
Translatotron-V(ison): An End-to-End Model for In-Image Machine Translation
Zhibin Lan, Liqiang Niu, Fandong Meng +3
In-image machine translation (IIMT) aims to translate an image containing texts in source language into an image containing translations in target language. In this regard, convent…