6 citations · 9 across the 12 of their papers we have counts for
13 papers
OmniCam: Omni-Camera Trajectory Generation via Geometry-Grounded Pose Token Learning
Zhenyang Liu, Chenjie Cao, Yisu Zhang +5
Camera trajectories control viewpoint changes in video generation, scene reconstruction, and robotic perception. Generating them from language requires both scene geometry and targ…
SnapGuard: Lightweight Prompt Injection Detection for Screenshot-Based Web Agents
Mengyao Du, Han Fang, Haokai Ma +4
Web agents have emerged as an effective paradigm for automating interactions with complex web environments, yet remain vulnerable to prompt injection attacks that embed malicious i…
CityCube: Benchmarking Cross-view Spatial Reasoning on Vision-Language Models in Urban Environments
Haotian Xu, Yue Hu, Zhengqiu Zhu +7
Cross-view spatial reasoning is essential for embodied AI, underpinning spatial understanding, mental simulation and planning in complex environments. Existing benchmarks primarily…
Towards Autonomous UAV Visual Object Search in City Space: Benchmark and Agentic Methodology
Yatai Ji, Zhengqiu Zhu, Yong Zhao +7
Aerial Visual Object Search (AVOS) tasks in urban environments require Unmanned Aerial Vehicles (UAVs) to autonomously search for and identify target objects using visual and textu…
GeoNav: Empowering MLLMs with dual-scale geospatial reasoning for language-goal aerial navigation
Haotian Xu, Yue Hu, Chen Gao +4
Language-goal aerial navigation requires UAVs to localize targets in the complex outdoors, such as urban blocks based on textual instructions. The indoor methods are often hard to…
SwimVG: Step-wise Multimodal Fusion and Adaption for Visual Grounding
Liangtao Shi, Ting Liu, Xiantao Hu +3
Visual grounding aims to ground an image region through natural language, which heavily relies on cross-modal alignment. Most existing methods transfer visual/linguistic knowledge…
Multi-Stage Vision Token Dropping: Towards Efficient Multimodal Large Language Model
Ting Liu, Liangtao Shi, Richang Hong +3
The vision tokens in multimodal large language models usually exhibit significant spatial and temporal redundancy and take up most of the input tokens, which harms their inference…
MaPPER: Multimodal Prior-guided Parameter Efficient Tuning for Referring Expression Comprehension
Ting Liu, Zunnan Xu, Yue Hu +3
Referring Expression Comprehension (REC), which aims to ground a local visual region via natural language, is a task that heavily relies on multimodal alignment. Most existing meth…
M2IST: Multi-Modal Interactive Side-Tuning for Efficient Referring Expression Comprehension
Xuyang Liu, Ting Liu, Siteng Huang +6
Referring expression comprehension (REC) is a vision-language task to locate a target object in an image based on a language expression. Fully fine-tuning general-purpose pre-train…
DARA: Domain- and Relation-aware Adapters Make Parameter-efficient Tuning for Visual Grounding
Ting Liu, Xuyang Liu, Siteng Huang +5
Visual grounding (VG) is a challenging task to localize an object in an image based on a textual description. Recent surge in the scale of VG models has substantially improved perf…
Sparse-Tuning: Adapting Vision Transformers with Efficient Fine-tuning and Inference
Ting Liu, Xuyang Liu, Liangtao Shi +6
Parameter-efficient fine-tuning (PEFT) has emerged as a popular solution for adapting pre-trained Vision Transformer (ViT) models to downstream applications by updating only a smal…
Conversational Crowdsensing: A Parallel Intelligence Powered Novel Sensing Approach
Zhengqiu Zhu, Yong Zhao, Bin Chen +6
The transition from CPS-based Industry 4.0 to CPSS-based Industry 5.0 brings new requirements and opportunities to current sensing approaches, especially in light of recent progres…
Vision-Language Navigation: A Survey and Taxonomy
Wansen Wu, Tao Chang, Xinmeng Li
Vision-Language Navigation (VLN) tasks require an agent to follow human language instructions to navigate in previously unseen environments. This challenging field involving proble…