9 papers
What Memory Do GUI Agents Really Need? From Passive Records to Active Task-Driving States
Chen Liu, Ling Chen, Hanzhang Zhou +7
Mobile GUI agents increasingly face long-horizon tasks that require reading, updating, and reusing task-relevant data across pages and applications. Existing methods treat memory l…
When Should Service Agents Reconsider? Difficulty-Routed Control in Customer-Service Operations
Qian Chen, Chengyuan Liu, Xin Yu
Autonomous customer-service agents are shifting from conversational interfaces toward operational execution roles: they retrieve firm records, apply service policies, and execute b…
One Forward Beats Two: InnerZoom for Accurate and Efficient GUI Grounding
Chen Liu, Ling Chen, Hanzhang Zhou +5
MLLM-based GUI grounding methods commonly formulate target localization as autoregressive coordinate generation, enabling models to leverage the strong instruction-following and se…
When One Moment Isn't Enough: Multi-Moment Retrieval with Cross-Moment Interactions
Zhuo Cao, Heming Du, Bingqing Zhang +3
Existing Moment retrieval (MR) methods focus on Single-Moment Retrieval (SMR). However, one query can correspond to multiple relevant moments in real-world applications. This makes…
Dynamic Orchestration of Multi-Agent System for Real-World Multi-Image Agricultural VQA
Yan Ke, Xin Yu, Heming Du +2
Agricultural visual question answering is essential for providing farmers and researchers with accurate and timely knowledge. However, many existing approaches are predominantly de…
FlashVTG: Feature Layering and Adaptive Score Handling Network for Video Temporal Grounding
Zhuo Cao, Bingqing Zhang, Heming Du +3
Text-guided Video Temporal Grounding (VTG) aims to localize relevant segments in untrimmed videos based on textual descriptions, encompassing two subtasks: Moment Retrieval (MR) an…
TokenBinder: Text-Video Retrieval with One-to-Many Alignment Paradigm
Bingqing Zhang, Zhuo Cao, Heming Du +4
Text-Video Retrieval (TVR) methods typically match query-candidate pairs by aligning text and video features in coarse-grained, fine-grained, or combined (coarse-to-fine) manners.…
Divide and Ensemble: Progressively Learning for the Unknown
Hu Zhang, Xin Shen, Heming Du +10
In the wheat nutrient deficiencies classification challenge, we present the DividE and EnseMble (DEEM) method for progressive test data predictions. We find that (1) test images ar…
When 3D Bounding-Box Meets SAM: Point Cloud Instance Segmentation with Weak-and-Noisy Supervision
Qingtao Yu, Heming Du, Chen Liu +1
Learning from bounding-boxes annotations has shown great potential in weakly-supervised 3D point cloud instance segmentation. However, we observed that existing methods would suffe…