11 papers · 1 filter
Can Text-to-Image Models Draw from the Right Frame of Reference?
Zheyuan Gu, Ruihang Li, Yong Huang +5
Spatial instruction following has become a crucial requirement for text-to-image (T2I) generation. A common challenge arises when directional expressions are interpreted under diff…
Test-Time Curriculum for Open-Set AIGC Detection
Yiqian Zhang, Zheyuan Gu, Xiangzhao Hao +8
AI-generated image detectors deployed in open-world environments inevitably face distribution shifts as new and stronger generative models continue to emerge. Although existing met…
ReLoop-UME: Recurrent Depth with Learnable Retrieval Registers for Universal Multimodal Embedding
Shijie Wang, Xiangzhao Hao, Yueti Li +3
Universal multimodal embedding (UME) maps heterogeneous multimodal inputs into a shared embedding space. Existing UME models either form embeddings through single forward encoding…
PLUME: Latent Reasoning Based Universal Multimodal Embedding
Chenwei He, Xiangzhao Hao, Tianyu Yang +6
Universal multimodal embedding (UME) maps heterogeneous inputs into a shared retrieval space with a single model. Recent approaches improve UME by generating explicit chain-of-thou…
CLEAR: Unlocking Generative Potential for Degraded Image Understanding in Unified Multimodal Models
Xiangzhao Hao, Zefeng Zhang, Zhenyu Zhang +6
Image degradation from blur, noise, compression, and poor illumination severely undermines multimodal understanding in real-world settings. Unified multimodal models that combine u…
ReCALL: Recalibrating Capability Degradation for MLLM-based Composed Image Retrieval
Tianyu Yang, Chenwei He, Xiangzhao Hao +6
Composed Image Retrieval (CIR) aims to retrieve target images based on a hybrid query comprising a reference image and a modification text. Early dual-tower Vision-Language Models…
WISER: Wider Search, Deeper Thinking, and Adaptive Fusion for Training-Free Zero-Shot Composed Image Retrieval
Tianyue Wang, Leigang Qu, Tianyu Yang +4
Zero-Shot Composed Image Retrieval (ZS-CIR) aims to retrieve target images given a multimodal query (comprising a reference image and a modification text), without training on anno…
TRACE: Task-Adaptive Reasoning and Representation Learning for Universal Multimodal Retrieval
Xiangzhao Hao, Shijie Wang, Tianyu Yang +3
Universal Multimodal Retrieval requires unified embedding models capable of interpreting diverse user intents, ranging from simple keywords to complex compositional instructions. W…
COOPER: A Unified Model for Cooperative Perception and Reasoning in Spatial Intelligence
Zefeng Zhang, Xiangzhao Hao, Hengzhu Tang +8
Visual Spatial Reasoning is crucial for enabling Multimodal Large Language Models (MLLMs) to understand object properties and spatial relationships, yet current models still strugg…
UniFGVC: Universal Training-Free Few-Shot Fine-Grained Vision Classification via Attribute-Aware Multimodal Retrieval
Hongyu Guo, Xiangzhao Hao, Jiarui Guo +3
Few-shot fine-grained visual classification (FGVC) aims to discriminate between subtly different categories with very limited data. While Multimodal Large Language Models (MLLM) ex…
Referring Expression Instance Retrieval and A Strong End-to-End Baseline
Xiangzhao Hao, Kuan Zhu, Hongyu Guo +5
Using natural language to query visual information is a fundamental need in real-world applications. Text-Image Retrieval (TIR) retrieves a target image from a gallery based on an…