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- Chen Change LoySuggested from co-authorship
- Dacheng TaoSuggested from co-authorship
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Works12 from public data
- Towards Open Vocabulary Learning: A Survey285
This paper thoroughly reviews open vocabulary learning, summarizing and analyzing recent developments in the field, and juxtaposing open vocabulary learning with analogous concepts such as zero-shot learning, open-set recognition, and out-of-distribution detection.
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- Towards Robust Referring Image Segmentation66
This work proposes a new formulation of RIS, named Robust Referring Image Segmentation (R-RIS), which considers the negative sentence inputs besides the regular positive text inputs, and proposes a new transformer-based model, called RefSegformer, with a token-based vision and language fusion module.
- Towards Language-Driven Video Inpainting via Multimodal Large Language Models48
This work introduces a new task - language-driven video inpainting, which uses natural language instructions to guide the inpainting process, integrating Multimodal Large Language Models to understand and execute complex language-based inpaintingrequests effectively.
- Betrayed by Captions: Joint Caption Grounding and Generation for Open Vocabulary Instance Segmentation40
This work devise a joint Caption Grounding and Generation (CGG) framework, which incorporates a novel grounding loss that only focuses on matching object nouns to improve learning efficiency and introduces a caption generation head that enables additional supervision and contextual modeling as a complementation to the grounding loss.
- An Empirical Study of GPT-4o Image Generation Capabilities31
An empirical study of GPT-4o's image generation capabilities is conducted, benchmarking it against leading open-source and commercial models and identifying promising directions for future unified generative models, emphasizing the role of architectural design and data scaling.
- DreamRelation: Bridging Customization and Relation Generation11
This work introduces DreamRelation, a framework that disentangles identity and relation learning using a carefully curated dataset and proposes two key modules to tackle the two main challenges of generating accurate and natural relationships, especially when significant pose adjustments are required, and avoiding object confusion in cases of overlap.
- LoomVideo: Unifying Multimodal Inputs into Video Generation and Editing6
This work introduces a zero-overhead Scale-and-Add conditioning approach for video editing and eliminates the need for token concatenation, drastically reducing computational cost while maintaining robust capabilities for complex, non-rigid edits.
- Auto Cherry-Picker : Learning from High-quality Generative Data Driven by Language4
This work presents Auto Cherry-Picker (ACP), a novel framework that generates high-quality cross-modality training samples at scale to augment perception and multi-modal training and finds a positive correlation between CLIS and performance gains in downstream tasks.
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- Towards Customized Multimodal Role-Play–
It is argued that CMRP, coupled with unified modeling, provides a basis for next-generation characterful and immersive interactive agents.
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Publication data from OpenAlex; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-13. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
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