26 citations · 89 across the 49 of their papers we have counts for
12 papers · 1 filter
LIBERO-Occ: Evaluating and Improving Vision-Language-Action Models under Scene-Induced Occlusion via Viewpoint Imagination
Taishan Li, Jiwen Zhang, Siyuan Wang +2
Vision-Language-Action (VLA) models achieve strong performance on standard manipulation benchmarks, but most evaluations assume that task-relevant objects are fully visible. This a…
UniReason 1.0: A Unified Reasoning Framework for World Knowledge Aligned Image Generation and Editing
Dianyi Wang, Chaofan Ma, Feng Han +8
Unified multimodal models often struggle with complex synthesis tasks that demand deep reasoning, and typically treat text-to-image generation and image editing as isolated capabil…
DeepGen 1.0: A Lightweight Unified Multimodal Model for Advancing Image Generation and Editing
Dianyi Wang, Ruihang Li, Feng Han +17
Current unified multimodal models for image generation and editing typically rely on massive parameter scales (e.g., >10B), entailing prohibitive training costs and deployment foot…
SpatialNav: Leveraging Spatial Scene Graphs for Zero-Shot Vision-and-Language Navigation
Jiwen Zhang, Zejun Li, Siyuan Wang +3
Although learning-based vision-and-language navigation (VLN) agents can learn spatial knowledge implicitly from large-scale training data, zero-shot VLN agents lack this process, r…
Simple o3: Towards Interleaved Vision-Language Reasoning
Ye Wang, Qianglong Chen, Zejun Li +4
Multimodal Large Language Models (MLLMs) have shown impressive performance on vision-language tasks, but their long Chain-of-Thought (CoT) capabilities in multimodal scenarios rema…
MoIIE: Mixture of Intra- and Inter-Modality Experts for Large Vision Language Models
Dianyi Wang, Siyuan Wang, Zejun Li +6
Large Vision-Language Models (LVLMs) have demonstrated remarkable performance across multi-modal tasks by scaling model size and training data. However, these dense LVLMs incur sig…
Autoregressive Semantic Visual Reconstruction Helps VLMs Understand Better
Dianyi Wang, Wei Song, Yikun Wang +4
Typical large vision-language models (LVLMs) apply autoregressive supervision solely to textual sequences, without fully incorporating the visual modality into the learning process…
OViP: Online Vision-Language Preference Learning for VLM Hallucination
Shujun Liu, Siyuan Wang, Zejun Li +3
Large vision-language models (LVLMs) remain vulnerable to hallucination, often generating content misaligned with visual inputs. Although recent training-based approaches aim to mi…
Activating Distributed Visual Region within LLMs for Efficient and Effective Vision-Language Training and Inference
Siyuan Wang, Dianyi Wang, Chengxing Zhou +4
Large Vision-Language Models (LVLMs) typically learn visual capacity through visual instruction tuning, involving updates to both a projector and their LLM backbones. Inspired by t…
Negative Sample is Negative in Its Own Way: Tailoring Negative Sentences for Image-Text Retrieval
Zhihao Fan, Zhongyu Wei, Zejun Li +2
Matching model is essential for Image-Text Retrieval framework. Existing research usually train the model with a triplet loss and explore various strategy to retrieve hard negative…
Constructing Phrase-level Semantic Labels to Form Multi-Grained Supervision for Image-Text Retrieval
Zhihao Fan, Zhongyu Wei, Zejun Li +4
Existing research for image text retrieval mainly relies on sentence-level supervision to distinguish matched and mismatched sentences for a query image. However, semantic mismatch…
TCIC: Theme Concepts Learning Cross Language and Vision for Image Captioning
Zhihao Fan, Zhongyu Wei, Siyuan Wang +4
Existing research for image captioning usually represents an image using a scene graph with low-level facts (objects and relations) and fails to capture the high-level semantics. I…