4 citations · 6 across the 8 of their papers we have counts for
9 papers
Robot Manipulation with GPT-6-Astra: Body Knowledge, Experience Reuse, Emergent Skills, and Sim2Real Transfer
Sida He, Lingxi Xie, Yunning Cao +6
General-purpose multimodal agents can write robot-control programs, but repeated exploration and model-mediated action selection can make execution slow. We study how external body…
Reasoning LLM Improves Speaker Recognition in Long-form TV Dramas
Yuxuan Li, Lingxi Xie, Xinyue Huo +6
Long-form TV dramas present a formidable challenge for comprehensive video understanding, where deciphering complex storyline often relies on \textbf{speaker recognition}, the task…
LongVideo-R1: Smart Navigation for Low-cost Long Video Understanding
Jihao Qiu, Lingxi Xie, Xinyue Huo +2
This paper addresses the critical and underexplored challenge of long video understanding with low computational budgets. We propose LongVideo-R1, an active, reasoning-equipped mul…
SAM-CP: Marrying SAM with Composable Prompts for Versatile Segmentation
Pengfei Chen, Lingxi Xie, Xinyue Huo +5
The Segment Anything model (SAM) has shown a generalized ability to group image pixels into patches, but applying it to semantic-aware segmentation still faces major challenges. Th…
One-bit Supervision for Image Classification: Problem, Solution, and Beyond
Hengtong Hu, Lingxi Xie, Xinyue Hue +2
This paper presents one-bit supervision, a novel setting of learning with fewer labels, for image classification. Instead of training model using the accurate label of each sample,…
Focus on Your Target: A Dual Teacher-Student Framework for Domain-adaptive Semantic Segmentation
Xinyue Huo, Lingxi Xie, Wengang Zhou +2
We study unsupervised domain adaptation (UDA) for semantic segmentation. Currently, a popular UDA framework lies in self-training which endows the model with two-fold abilities: (i…
Domain-Agnostic Prior for Transfer Semantic Segmentation
Xinyue Huo, Lingxi Xie, Hengtong Hu +3
Unsupervised domain adaptation (UDA) is an important topic in the computer vision community. The key difficulty lies in defining a common property between the source and target dom…
Heterogeneous Contrastive Learning: Encoding Spatial Information for Compact Visual Representations
Xinyue Huo, Lingxi Xie, Longhui Wei +6
Contrastive learning has achieved great success in self-supervised visual representation learning, but existing approaches mostly ignored spatial information which is often crucial…
ATSO: Asynchronous Teacher-Student Optimization for Semi-Supervised Medical Image Segmentation
Xinyue Huo, Lingxi Xie, Jianzhong He +2
In medical image analysis, semi-supervised learning is an effective method to extract knowledge from a small amount of labeled data and a large amount of unlabeled data. This paper…