Chongjun Tu

Orcid: 0009-0003-7405-2022

According to our database1, Chongjun Tu authored at least 13 papers between 2023 and 2026.

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

Timeline

Legend:

Book  In proceedings  Article  PhD thesis  Dataset  Other 

Links

On csauthors.net:

Bibliography

2026
Δ-DiT: Accelerating Diffusion Transformers without Training via Denoising Property Alignment.
Int. J. Comput. Vis., June, 2026

Attention Reallocation: Towards Zero-cost and Controllable Hallucination Mitigation of MLLMs.
Int. J. Comput. Vis., January, 2026

LSTM-MAS: A Long Short-Term Memory Inspired Multi-Agent System for Long-Context Understanding.
CoRR, January, 2026

Mitigating Low-Quality Reasoning in MLLMs: Self-Driven Refined Multimodal CoT with Selective Thinking and Step-wise Visual Enhancement.
Proceedings of the Fortieth AAAI Conference on Artificial Intelligence, 2026

2025
Sparse-to-Dense Training: A Novel Training Scheme to Enhance Vision Transformers.
IEEE Trans. Circuits Syst. Video Technol., December, 2025

Revisiting Multimodal KV Cache Compression: A Frequency-Domain-Guided Outlier-KV-Aware Approach.
CoRR, November, 2025

PaceLLM: Brain-Inspired Large Language Models for Long-Context Understanding.
CoRR, June, 2025

FAVOR-Bench: A Comprehensive Benchmark for Fine-Grained Video Motion Understanding.
CoRR, March, 2025

TokenCarve: Information-Preserving Visual Token Compression in Multimodal Large Language Models.
CoRR, March, 2025

Efficient Architecture Search via Bi-Level Data Pruning.
IEEE Trans. Circuits Syst. Video Technol., February, 2025

ClipSAM: CLIP and SAM collaboration for zero-shot anomaly segmentation.
Neurocomputing, 2025

2024
Δ-DiT: A Training-Free Acceleration Method Tailored for Diffusion Transformers.
CoRR, 2024

2023
Partial Fine-Tuning: A Successor to Full Fine-Tuning for Vision Transformers.
CoRR, 2023


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