Qi Yang

Orcid: 0000-0001-8373-6096

Affiliations:
  • Chinese Academy of Sciences, Institute of Automation, Beijing, China


According to our database1, Qi Yang authored at least 14 papers between 2023 and 2026.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

Legend:

Book  In proceedings  Article  PhD thesis  Dataset  Other 

Links

Online presence:

On csauthors.net:

Bibliography

2026
CC-VQA: Conflict- and Correlation-Aware Method for Mitigating Knowledge Conflict in Knowledge-Based Visual Question Answering.
CoRR, February, 2026

MedSAM-Agent: Empowering Interactive Medical Image Segmentation with Multi-turn Agentic Reinforcement Learning.
CoRR, February, 2026

Efficient redundancy reduction for open-vocabulary semantic segmentation.
Neurocomputing, 2026

2025
SAM-MI: A Mask-Injected Framework for Enhancing Open-Vocabulary Semantic Segmentation with SAM.
CoRR, November, 2025

Taming Modality Entanglement in Continual Audio-Visual Segmentation.
CoRR, October, 2025

Knowledge-based Visual Question Answer with Multimodal Processing, Retrieval and Filtering.
CoRR, October, 2025

R-4B: Incentivizing General-Purpose Auto-Thinking Capability in MLLMs via Bi-Mode Annealing and Reinforce Learning.
CoRR, August, 2025

Re-ranking Reasoning Context with Tree Search Makes Large Vision-Language Models Stronger.
Proceedings of the Forty-second International Conference on Machine Learning, 2025

2024
Continuous Speculative Decoding for Autoregressive Image Generation.
CoRR, 2024

Draw an Audio: Leveraging Multi-Instruction for Video-to-Audio Synthesis.
CoRR, 2024

AVESFormer: Efficient Transformer Design for Real-Time Audio-Visual Segmentation.
CoRR, 2024

Cooperation Does Matter: Exploring Multi-Order Bilateral Relations for Audio-Visual Segmentation.
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2024

2023
Dynamic High-Resolution Network for Semantic Segmentation in Remote-Sensing Images.
Remote. Sens., May, 2023

Continual Semantic Segmentation via Scalable Contrastive Clustering and Background Diversity.
Proceedings of the IEEE International Conference on Data Mining, 2023


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