1 citations · 3 across the 15 of their papers we have counts for
16 papers
VEGAS: Human-Aligned Video Caption Evaluation via Gaze
Shenghui Chen, Po-han Li, Ximeng Sun +5
Vision-language models excel at video captioning, yet typically generate descriptions that fail to capture individual viewers' attention. We propose VEGAS (Video caption Evaluation…
What We are Missing in Multimodal LLM Evaluation?
Po-han Li, Shenghui Chen, Sandeep Chinchali +1
Multimodal large language models (MLLMs) can process diverse inputs, e.g., text, images, audio, and video, and generate textual responses. While their capabilities have advanced ra…
ViSIL: Unified Evaluation of Information Loss in Multimodal Video Captioning
Po-han Li, Shenghui Chen, Ufuk Topcu +1
Multimodal video captioning condenses dense footage into a structured format of keyframes and natural language. By creating a cohesive multimodal summary, this approach anchors gen…
VIBE: Annotation-Free Video-to-Text Information Bottleneck Evaluation for TL;DR
Shenghui Chen, Po-han Li, Sandeep Chinchali +1
Many decision-making tasks, where both accuracy and efficiency matter, still require human supervision. For example, tasks like traffic officers reviewing hour-long dashcam footage…
Evaluating Human Trust in LLM-Based Planners: A Preliminary Study
Shenghui Chen, Yunhao Yang, Kayla Boggess +3
Large Language Models (LLMs) are increasingly used for planning tasks, offering unique capabilities not found in classical planners such as generating explanations and iterative re…
IG-MCTS: Human-in-the-Loop Cooperative Navigation under Incomplete Information
Shenghui Chen, Ruihan Zhao, Sandeep Chinchali +1
Human-robot cooperative navigation is challenging under incomplete information. We introduce CoNav-Maze, a simulated environment where a robot navigates with local perception while…
Human-Agent Coordination in Games under Incomplete Information via Multi-Step Intent
Shenghui Chen, Ruihan Zhao, Sandeep Chinchali +1
Strategic coordination between autonomous agents and human partners under incomplete information can be modeled as turn-based cooperative games. We extend a turn-based game under i…
Learning to Coordinate without Communication under Incomplete Information
Shenghui Chen, Shufang Zhu, Giuseppe De Giacomo +1
Achieving seamless coordination in cooperative games is a crucial challenge in artificial intelligence, particularly when players operate under incomplete information. While commun…
Human-Agent Cooperation in Games under Incomplete Information through Natural Language Communication
Shenghui Chen, Daniel Fried, Ufuk Topcu
Developing autonomous agents that can strategize and cooperate with humans under information asymmetry is challenging without effective communication in natural language. We introd…
Soft-Bellman Equilibrium in Affine Markov Games: Forward Solutions and Inverse Learning
Shenghui Chen, Yue Yu, David Fridovich-Keil +1
Markov games model interactions among multiple players in a stochastic, dynamic environment. Each player in a Markov game maximizes its expected total discounted reward, which depe…
Cost Design in Atomic Routing Games
Yue Yu, Shenghui Chen, David Fridovich-Keil +1
An atomic routing game is a multiplayer game on a directed graph. Each player in the game chooses a path -- a sequence of links that connect its origin node to its destination node…
Relationship Design for Socially-Aware Behavior in Static Games
Shenghui Chen, Yigit E. Bayiz, David Fridovich-Keil +1
Autonomous agents can adopt socially-aware behaviors to reduce social costs, mimicking the way animals interact in nature and humans in society. We present a new approach to model…
Deceptive Planning for Resource Allocation
Shenghui Chen, Yagiz Savas, Mustafa O. Karabag +2
We consider a team of autonomous agents that navigate in an adversarial environment and aim to achieve a task by allocating their resources over a set of target locations. An adver…
Multi-Objective Controller Synthesis with Uncertain Human Preferences
Shenghui Chen, Kayla Boggess, David Parker +1
Complex real-world applications of cyber-physical systems give rise to the need for multi-objective controller synthesis, which concerns the problem of computing an optimal control…
Towards Personalized Explanation of Robot Path Planning via User Feedback
Kayla Boggess, Shenghui Chen, Lu Feng
Prior studies have found that explaining robot decisions and actions helps to increase system transparency, improve user understanding, and enable effective human-robot collaborati…
Towards Transparent Robotic Planning via Contrastive Explanations
Shenghui Chen, Kayla Boggess, Lu Feng
Providing explanations of chosen robotic actions can help to increase the transparency of robotic planning and improve users' trust. Social sciences suggest that the best explanati…