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Works27 from public data
- Text4Seg: Reimagining Image Segmentation as Text Generation70
Text4Seg is introduced, a novel text-as-mask paradigm that casts image segmentation as a text generation problem, eliminating the need for additional decoders and significantly simplifying the segmentation process.
- GeoGround: A Unified Large Vision-Language Model for Remote Sensing Visual Grounding59
GeoGround is a novel framework that unifies support for HBB, OBB, and mask RS visual grounding tasks, allowing flexible output selection through the Text-Mask technique, and defines prompt-assisted and geometry-guided learning to enhance consistency across different signals.
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- MIMO Is All You Need：A Strong Multi-in-Multi-Out Baseline for Video Prediction44
Surprisingly, empirical studies reveal that a simple MIMO model can outperform the state-of-the-art work with a large margin much more than expected, especially in dealing with long-term error accumulation.
- Contrasformer: A Brain Network Contrastive Transformer for Neurodegenerative Condition Identification25
Evaluated on 4 functional brain network datasets over 4 different diseases, Contrasformer outperforms the state-of-the-art methods for brain networks by achieving up to 10.8% improvement in accuracy, which demonstrates its efficacy in neurological disorder identification.
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- Multi-Atlas Brain Network Classification Through Consistency Distillation and Complementary Information Fusion15
The Atlas-Integrated Distillation and Fusion network (AIDFusion), a novel framework designed to enhance brain network classification using fMRI data, is proposed, which demonstrates its superior classification performance and computational efficiency compared to state-of-the-art methods.
- Text4Seg++: Advancing Image Segmentation via Generative Language Modeling11
A novel text-as-mask paradigm that casts image segmentation as a text generation problem, eliminating the need for additional decoders and significantly simplifying the segmentation process is proposed, highlighting the effectiveness, scalability, and generalizability of text-driven image segmentation within the MLLM framework.
- Learning to Discover Knowledge: A Weakly-Supervised Partial Domain Adaptation Approach9
Self-paced transfer classifier learning (SP-TCL) learns to discover faithful knowledge via a carefully designed prudent loss function and simultaneously adapts the learned knowledge to the target domain by iteratively excluding source examples from training under the self-paced fashion.
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- Multimodal mathematical reasoning embedded in aerial vehicle imagery: Benchmarking, analysis, and exploration3
This paper benchmarks 14 prominent VLMs through a comprehensive evaluation and demonstrates that, despite their success on previous multimodal benchmarks, these models struggle with the reasoning tasks in AVI-Math, the first benchmark to rigorously evaluate multimodal mathematical reasoning in aerial vehicle imagery.
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- NOCL: Node-Oriented Conceptualization LLM for Graph Tasks without Message Passing–
The Node-Oriented Conceptualization LLM (NOCL), a novel framework that leverages two core techniques that converts heterogeneous node attributes into structured natural language, extending LLM from TAGs to non-TAGs and significantly reducing token lengths, is proposed.
Publication data from OpenAlex; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-11. 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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