1 citations · 1 across the 13 of their papers we have counts for
14 papers
MG-VQA: Manipulation Grounded Visual Question Answering with VLMs
Vineet Bhat, Siyi Chen, Mikaela Angelina Uy +6
Vision-language models (VLMs) have shown promising spatial reasoning capabilities from static visual inputs, where the evidence needed to answer a question is available in the prov…
Data-Forcing Distillation: Restoring Diversity and Fidelity in Few-Step Video Generation
Siyi Chen, Shaowei Liu, Yixuan Jia +4
Recent progress has shown promise in distilling multi-step video diffusion models into efficient few-step students. Among them, Distribution Matching Distillation (DMD) and its suc…
Vesta: A Generalist Embodied Reasoning Model
Johan Bjorck, Zhiqi Li, Yunze Man +29
Robots operating in open-world environments must seamlessly integrate localization, spatial reasoning, navigation, and long-horizon planning. While specialist models excel at indiv…
See What I See, Know What I Think: Dense Latent Communication Across Heterogeneous Agents
Siyi Chen, Xiaoyan Zhang, Meng Wu +7
Language-model agents build internal representations of the information they observe and the reasoning they perform. Sharing these representations offers a way to communicate both…
VoLo: A Physical Orchestrator for Open-Vocabulary Long-Horizon Manipulation
Siyi Chen, Hugo Hadfield, Alex Zook +9
Open-vocabulary long-horizon manipulation requires robots to reason over flexible instructions and complex multi-object scenes while adaptively planning, executing, monitoring, and…
On the Limits of Token Reduction for Efficient Unified Vision Language Training
Siyi Chen, Weiming Zhuang, Jingtao Li +1
Unified vision-language models (VLMs) integrate visual understanding and visual generation within a single autoregressive backbone, but their joint training is computationally expe…
SpaceTools: Tool-Augmented Spatial Reasoning via Double Interactive RL
Siyi Chen, Mikaela Angelina Uy, Chan Hee Song +6
Vision Language Models (VLMs) demonstrate strong qualitative visual understanding, but struggle with metrically precise spatial reasoning required for embodied applications. The ag…
Understanding Generalization in Diffusion Distillation via Probability Flow Distance
Huijie Zhang, Zijian Huang, Siyi Chen +4
Diffusion distillation provides an effective approach for learning lightweight and few-steps diffusion models with efficient generation. However, evaluating their generalization re…
Factorization of multimeters: a unified view on nonclassical quantum phenomena
Tim Achenbach, Andreas Bluhm, Leevi Leppäjärvi +2
Quantum theory exhibits various nonclassical features, such as measurement incompatibility, contextuality, steering, and Bell nonlocality, which distinguish it from classical physi…
Understanding Representation Dynamics of Diffusion Models via Low-Dimensional Modeling
Xiao Li, Zekai Zhang, Xiang Li +4
Diffusion models, though originally designed for generative tasks, have demonstrated impressive self-supervised representation learning capabilities. A particularly intriguing phen…
Explaining and Mitigating the Modality Gap in Contrastive Multimodal Learning
Can Yaras, Siyi Chen, Peng Wang +1
Multimodal learning has recently gained significant popularity, demonstrating impressive performance across various zero-shot classification tasks and a range of perceptive and gen…
Unfolding Videos Dynamics via Taylor Expansion
Siyi Chen, Minkyu Choi, Zesen Zhao +3
Taking inspiration from physical motion, we present a new self-supervised dynamics learning strategy for videos: Video Time-Differentiation for Instance Discrimination (ViDiDi). Vi…
Breaking the Curse of Dimensionality: Diffusion Models Efficiently Learn Low-Dimensional Distributions
Peng Wang, Huijie Zhang, Zekai Zhang +3
Despite their empirical success across a wide range of generative tasks, the fundamental principles underlying the ability of diffusion models to learn data distributions are poorl…
Exploring Low-Dimensional Subspaces in Diffusion Models for Controllable Image Editing
Siyi Chen, Huijie Zhang, Minzhe Guo +3
Recently, diffusion models have emerged as a powerful class of generative models. Despite their success, there is still limited understanding of their semantic spaces. This makes i…