7 citations · 8 across the 7 of their papers we have counts for
8 papers
Multimodal Thinking with Renderable Programs
Sunli Chen, Ding Zhong, Ziqiao Ma +6
Current vision-language models (VLMs) excel at visual content understanding and text-based reasoning, yet their structure limits the advancement of incorporating images into the re…
RoboMME: Benchmarking and Understanding Memory for Robotic Generalist Policies
Yinpei Dai, Hongze Fu, Jayjun Lee +6
Memory is critical for long-horizon and history-dependent robotic manipulation. Such tasks often involve counting repeated actions or manipulating objects that become temporarily o…
Transparent and Coherent Procedural Mistake Detection
Shane Storks, Itamar Bar-Yossef, Yayuan Li +3
Procedural mistake detection (PMD) is a challenging problem of classifying whether a human user (observed through egocentric video) has successfully executed a task (specified by a…
Do Vision-Language Models Represent Space and How? Evaluating Spatial Frame of Reference Under Ambiguities
Zheyuan Zhang, Fengyuan Hu, Jayjun Lee +4
Spatial expressions in situated communication can be ambiguous, as their meanings vary depending on the frames of reference (FoR) adopted by speakers and listeners. While spatial l…
RACER: Rich Language-Guided Failure Recovery Policies for Imitation Learning
Yinpei Dai, Jayjun Lee, Nima Fazeli +1
Developing robust and correctable visuomotor policies for robotic manipulation is challenging due to the lack of self-recovery mechanisms from failures and the limitations of simpl…
Eliciting In-Context Learning in Vision-Language Models for Videos Through Curated Data Distributional Properties
Keunwoo Peter Yu, Zheyuan Zhang, Fengyuan Hu +2
A major reason behind the recent success of large language models (LLMs) is their \textit{in-context learning} capability, which makes it possible to rapidly adapt them to downstre…
From Heuristic to Analytic: Cognitively Motivated Strategies for Coherent Physical Commonsense Reasoning
Zheyuan Zhang, Shane Storks, Fengyuan Hu +4
Pre-trained language models (PLMs) have shown impressive performance in various language tasks. However, they are prone to spurious correlations, and often generate illusory inform…
CycleNet: Rethinking Cycle Consistency in Text-Guided Diffusion for Image Manipulation
Sihan Xu, Ziqiao Ma, Yidong Huang +2
Diffusion models (DMs) have enabled breakthroughs in image synthesis tasks but lack an intuitive interface for consistent image-to-image (I2I) translation. Various methods have bee…