arXiv Computer Vision
4d ago

Task-State Adaptation with Prototype Memory for Multi-Task Dense Prediction

The paper introduces MemMTL, a multi‑task dense prediction framework that uses a compact task state derived from global visual context and refines it via a learnable prototype memory. This refined state informs task‑conditioned expert logits, which are combined with token‑level logits and routed through a sparse top‑k selection over a shared local expert bank. A task‑agnostic residual bank offers a common adaptation path, and both paths are added to the backbone feature before task‑specific prediction. The authors outline an evaluation protocol on NYUD‑v2 and PASCAL‑Context using SAM 3 and ViT‑L backbones to assess predictive quality, computational cost, and the contributions of task‑state conditioning, prototype retrieval, and sparse routing.

By Yangyang Xu, Haobo Yuan, Yuzhu Wang, Duo Su, Xi Ye, Yibo Yang, Jun Zhu
arXiv AI
Jun 15

Learning What to Predict: Downstream-Guided Task Design for Continued Pretraining

arXiv:2601. 22108v2 Announce Type: replace-cross Abstract: Continued pretraining is optimized with fixed self-supervised tasks but selected by downstream performance, creating a coarse feedback loop in which practitioners evaluate checkpoints, change data mixtures or objectives, and restart runs, while individual updates remain blind to target capabilities.

By Shuqi Ke, Giulia Fanti
arXiv AI
Aug 5

Video-DeepResearch: Towards the Next-Generation Multimodal Deepresearch Agent

arXiv:2608. 03979v1 Announce Type: cross Abstract: We introduce Video-DeepResearch (Video-DR), extending multimodal agents from static images to continuous video streams, a setting that demands dense spatiotemporal grounding coupled with open-web exploration.

By Zhen Fang, Yu Zeng, Wenxuan Huang, Yiming Zhao, Shiting Huang, Tianfei Ren, Qi Lu, Qingnan Ren, Qisheng Su, Lionel Z. Wang, Qingyu Yin, Shuang Chen, Zehui Chen, Lin Chen, Zhenfei Yin, Yao Hu, Shaohui Lin, Wanli Ouyang, Shaosheng Cao, Feng Zhao