Search Results for author:
Found 7 papers, 4 papers with code
Discriminative Probing and Tuning for Text-to-Image Generation
We present a discriminative adapter built on T2I models to probe their discriminative abilities on two representative tasks and leverage discriminative fine-tuning to improve their text-image alignment.
Generative Cross-Modal Retrieval: Memorizing Images in Multimodal Language Models for Retrieval and Beyond
Building upon this capability, we propose to enable multimodal large language models (MLLMs) to memorize and recall images within their parameters.
NExT-GPT: Any-to-Any Multimodal LLM
While recently Multimodal Large Language Models (MM-LLMs) have made exciting strides, they mostly fall prey to the limitation of only input-side multimodal understanding, without the ability to produce content in multiple modalities.
LayoutLLM-T2I: Eliciting Layout Guidance from LLM for Text-to-Image Generation
Afterward, we propose a fine-grained object-interaction diffusion method to synthesize high-faithfulness images conditioned on the prompt and the automatically generated layout.
Learnable Pillar-based Re-ranking for Image-Text Retrieval
Image-text retrieval aims to bridge the modality gap and retrieve cross-modal content based on semantic similarities.
Composed Image Retrieval with Text Feedback via Multi-grained Uncertainty Regularization
The key idea underpinning the proposed method is to integrate fine- and coarse-grained retrieval as matching data points with small and large fluctuations, respectively.
Composed Image Retrieval (CoIR)
Image Retrieval with Multi-Modal Query
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Dynamic Modality Interaction Modeling for Image-Text Retrieval
To address these issues, we develop a novel modality interaction modeling network based upon the routing mechanism, which is the first unified and dynamic multimodal interaction framework towards image-text retrieval.
