61 citations · 86 across the 11 of their papers we have counts for
13 papers
GazeAct: A Human-Gaze-Annotated Manipulation Dataset for Attention-Regularized Policies
Anupam Pani, Wang Yuan, Qifan Wang +1
Vision-language-action (VLA) policies must ground language instructions in the right objects, contact points, and target locations for reliable manipulation. Human gaze provides a…
SituHOI: Scalable Capture of Scene-Situated Full-Body Human-Object Interaction
Qifan Wang, Qihang Fang, Boao Shi +4
Modeling human-object interaction (HOI) in real environments requires jointly representing whole-body motion, manipulated objects, and scene geometry, yet existing datasets trade o…
InfoAtlas: A Foundation Model for Zero-Shot Statistical Dependence Estimate
Zhengyang Hu, Yanzhi Chen, Hanxiang Ren +5
Measuring statistical dependency between high-dimensional random variables is a fundamental task in data science and machine learning. Neural mutual information (MI) estimators off…
Divided Attention: Unsupervised Multi-Object Discovery with Contextually Separated Slots
Dong Lao, Zhengyang Hu, Francesco Locatello +2
We investigate the emergence of objects in visual perception in the absence of any semantic annotation. The resulting model has received no supervision, does not use any pre-traine…
Learning Semantic-Aware Dynamics for Video Prediction
Xinzhu Bei, Yanchao Yang, Stefano Soatto
We propose an architecture and training scheme to predict video frames by explicitly modeling dis-occlusions and capturing the evolution of semantically consistent regions in the v…
DyStaB: Unsupervised Object Segmentation via Dynamic-Static Bootstrapping
Yanchao Yang, Brian Lai, Stefano Soatto
We describe an unsupervised method to detect and segment portions of images of live scenes that, at some point in time, are seen moving as a coherent whole, which we refer to as ob…
FDA: Fourier Domain Adaptation for Semantic Segmentation
Yanchao Yang, Stefano Soatto
We describe a simple method for unsupervised domain adaptation, whereby the discrepancy between the source and target distributions is reduced by swapping the low-frequency spectru…
Learning to Manipulate Individual Objects in an Image
Yanchao Yang, Yutong Chen, Stefano Soatto
We describe a method to train a generative model with latent factors that are (approximately) independent and localized. This means that perturbing the latent variables affects onl…
Phase Consistent Ecological Domain Adaptation
Yanchao Yang, Dong Lao, Ganesh Sundaramoorthi +1
We introduce two criteria to regularize the optimization involved in learning a classifier in a domain where no annotated data are available, leveraging annotated data in a differe…
Dense Depth Posterior (DDP) from Single Image and Sparse Range
Yanchao Yang, Alex Wong, Stefano Soatto
We present a deep learning system to infer the posterior distribution of a dense depth map associated with an image, by exploiting sparse range measurements, for instance from a li…
Unsupervised Moving Object Detection via Contextual Information Separation
Yanchao Yang, Antonio Loquercio, Davide Scaramuzza +1
We propose an adversarial contextual model for detecting moving objects in images. A deep neural network is trained to predict the optical flow in a region using information from e…
Conditional Prior Networks for Optical Flow
Yanchao Yang, Stefano Soatto
Classical computation of optical flow involves generic priors (regularizers) that capture rudimentary statistics of images, but not long-range correlations or semantics. On the oth…
Shape Tracking With Occlusions via Coarse-To-Fine Region-Based Sobolev Descent
Yanchao Yang, Ganesh Sundaramoorthi
We present a method to track the precise shape of an object in video based on new modeling and optimization on a new Riemannian manifold of parameterized regions. Joint dynamic sha…