68 citations · 92 across the 8 of their papers we have counts for
9 papers
SAM 3: Segment Anything with Concepts
Nicolas Carion, Laura Gustafson, Yuan-Ting Hu +35
We present Segment Anything Model (SAM) 3, a unified model that detects, segments, and tracks objects in images and videos based on concept prompts, which we define as either short…
PointInfinity: Resolution-Invariant Point Diffusion Models
Zixuan Huang, Justin Johnson, Shoubhik Debnath +2
We present PointInfinity, an efficient family of point cloud diffusion models. Our core idea is to use a transformer-based architecture with a fixed-size, resolution-invariant late…
ConvNeXt V2: Co-designing and Scaling ConvNets with Masked Autoencoders
Sanghyun Woo, Shoubhik Debnath, Ronghang Hu +4
Driven by improved architectures and better representation learning frameworks, the field of visual recognition has enjoyed rapid modernization and performance boost in the early 2…
Exploring Long-Sequence Masked Autoencoders
Ronghang Hu, Shoubhik Debnath, Saining Xie +1
Masked Autoencoding (MAE) has emerged as an effective approach for pre-training representations across multiple domains. In contrast to discrete tokens in natural languages, the in…
RGB-D Local Implicit Function for Depth Completion of Transparent Objects
Luyang Zhu, Arsalan Mousavian, Yu Xiang +4
Majority of the perception methods in robotics require depth information provided by RGB-D cameras. However, standard 3D sensors fail to capture depth of transparent objects due to…
Self-Supervised Real-to-Sim Scene Generation
Aayush Prakash, Shoubhik Debnath, Jean-Francois Lafleche +4
Synthetic data is emerging as a promising solution to the scalability issue of supervised deep learning, especially when real data are difficult to acquire or hard to annotate. Syn…
Accelerating Goal-Directed Reinforcement Learning by Model Characterization
Shoubhik Debnath, Gaurav Sukhatme, Lantao Liu
We propose a hybrid approach aimed at improving the sample efficiency in goal-directed reinforcement learning. We do this via a two-step mechanism where firstly, we approximate a m…
Solving Markov Decision Processes with Reachability Characterization from Mean First Passage Times
Shoubhik Debnath, Lantao Liu, Gaurav Sukhatme
A new mechanism for efficiently solving the Markov decision processes (MDPs) is proposed in this paper. We introduce the notion of reachability landscape where we use the Mean Firs…
Reachability and Differential based Heuristics for Solving Markov Decision Processes
Shoubhik Debnath, Lantao Liu, Gaurav Sukhatme
The solution convergence of Markov Decision Processes (MDPs) can be accelerated by prioritized sweeping of states ranked by their potential impacts to other states. In this paper,…