collaborators

7 papers

cs.CV2026

EgoOmniWorld: Unifying Egocentric Understanding via Ego-Action-World Interactions

Songruo Chen, Yuqian Fu, Yu Li +7

Egocentric video understanding spans diverse capabilities, modalities, and evaluation formats, yet these are often studied in isolation despite their tight coupling in real-world i…

cs.CV2026

ObjectStream: Latent Objects as Memory Anchors for Streaming Video Understanding

Mingkang Dong, Muxin Pu, Jie Li +8

Streaming video understanding requires models to continuously retain useful visual evidence before future questions are known. Existing approaches primarily manage the growing visu…

cs.CV2026

OnceSelect: Reusable Data Selection for Efficient Multimodal Instruction Tuning

Mingkang Dong, Hongyi Cai, Xiwen Lei +8

Multimodal instruction tuning is widely used to adapt multimodal large language models (MLLMs), yet the large-scale image-text datasets it relies on are often highly redundant. Exi…

cs.CV2026

Evo-Depth: A Lightweight Depth-Enhanced Vision-Language-Action Model

Tao Lin, Yuxin Du, Jiting Liu +14

Vision-Language-Action models have emerged as a promising paradigm for robotic manipulation by unifying perception, language grounding, and action generation. However, they often s…

cs.CV2026

VisNec: Measuring and Leveraging Visual Necessity for Multimodal Instruction Tuning

Mingkang Dong, Hongyi Cai, Jie Li +4

The effectiveness of multimodal instruction tuning depends not only on dataset scale, but critically on whether training samples genuinely require visual reasoning. However, existi…

cs.CV2025

Pistachio: Towards Synthetic, Balanced, and Long-Form Video Anomaly Benchmarks

Jie Li, Hongyi Cai, Mingkang Dong +4

Automatically detecting abnormal events in videos is crucial for modern autonomous systems, yet existing Video Anomaly Detection (VAD) benchmarks lack the scene diversity, balanced…

cs.CV2025

AutoDebias: Automated Framework for Debiasing Text-to-Image Models

Hongyi Cai, Mohammad Mahdinur Rahman, Mingkang Dong +7

Text-to-Image (T2I) models generate high-quality images but are vulnerable to malicious backdoor attacks that inject harmful biases (e.g., trigger-activated gender or racial stereo…