most citedCPCL: Cross-Modal Prototypical Contrastive Learning for Weakly Supervised Text-based Person Retrieval

3 citations · 4 across the 6 of their papers we have counts for

collaborators

6 papers

cs.DC2025

LOIP:Collaborative Lossless LLM Inference Serving with Offloading-based Pipeline Parallelism on Edge Devices

Mingyu Sun, Xiao Zhang, Shen Qu +5

Providing lossless inference services of LLMs on edge devices remains challenging, especially given the extremely tight memory budgets. The existing offloading techniques inevitabl…

cs.AI2024

Federating to Grow Transformers with Constrained Resources without Model Sharing

Shikun Shen, Yifei Zou, Yuan Yuan +4

The high resource consumption of large-scale models discourages resource-constrained users from developing their customized transformers. To this end, this paper considers a federa…

cs.AI2024★ 1 cited

Efficient Strategy Learning by Decoupling Searching and Pathfinding for Object Navigation

Yanwei Zheng, Shaopu Feng, Bowen Huang +3

Inspired by human-like behaviors for navigation: first searching to explore unknown areas before discovering the target, and then the pathfinding of moving towards the discovered t…

cs.CV2024

Leveraging Unknown Objects to Construct Labeled-Unlabeled Meta-Relationships for Zero-Shot Object Navigation

Yanwei Zheng, Changrui Li, Chuanlin Lan +5

Zero-shot object navigation (ZSON) addresses situation where an agent navigates to an unseen object that does not present in the training set. Previous works mainly train agent usi…

cs.CV2024

Temporal-Spatial Object Relations Modeling for Vision-and-Language Navigation

Bowen Huang, Yanwei Zheng, Chuanlin Lan +3

Vision-and-Language Navigation (VLN) is a challenging task where an agent is required to navigate to a natural language described location via vision observations. The navigation a…

cs.CV2024★ 3 cited

CPCL: Cross-Modal Prototypical Contrastive Learning for Weakly Supervised Text-based Person Retrieval

Xinpeng Zhao, Yanwei Zheng, Chuanlin Lan +4

Weakly supervised text-based person retrieval seeks to retrieve images of a target person using textual descriptions, without relying on identity annotations and is more challengin…