1 citations · 1 across the 6 of their papers we have counts for
6 papers
Decoupling Planning and Control for Instructable Agents
Zineng Tang, Kelsey R. Allen, Sjoerd van Steenkiste +2
Recent work shows that pre-trained, instruction-tuned vision-language models (VLMs) perform well at mapping from instructions and observations to high-level plans, but struggle to…
Images are Worth Variable Length of Representations
Lingjun Mao, Rodolfo Corona, Xin Liang +2
Most existing vision encoders map images into a fixed-length sequence of tokens, overlooking the fact that different images contain varying amounts of information. For example, a v…
TULIP: Towards Unified Language-Image Pretraining
Zineng Tang, Long Lian, Seun Eisape +6
Despite the recent success of image-text contrastive models like CLIP and SigLIP, these models often struggle with vision-centric tasks that demand high-fidelity image understandin…
Evaluating Model Perception of Color Illusions in Photorealistic Scenes
Lingjun Mao, Zineng Tang, Alane Suhr
We study the perception of color illusions by vision-language models. Color illusion, where a person's visual system perceives color differently from actual color, is well-studied…
Grounding Language in Multi-Perspective Referential Communication
Zineng Tang, Lingjun Mao, Alane Suhr
We introduce a task and dataset for referring expression generation and comprehension in multi-agent embodied environments. In this task, two agents in a shared scene must take int…
AMONGAGENTS: Evaluating Large Language Models in the Interactive Text-Based Social Deduction Game
Yizhou Chi, Lingjun Mao, Zineng Tang
Strategic social deduction games serve as valuable testbeds for evaluating the understanding and inference skills of language models, offering crucial insights into social science,…