44 citations · 177 across the 20 of their papers we have counts for
17 papers · 1 filter
PhyProbe: Rethinking Physical Consistency Evaluation in Generated Videos
Max Ku, Jiaojiao Fan, Zekun Hao +5
Evaluating the physical consistency of generated videos remains a fundamental challenge. Existing approaches rely on off-the-shelf vision-language models, which can often be myopic…
Cosmos 3: Omnimodal World Models for Physical AI
NVIDIA, :, Aditi +293
We introduce Cosmos 3, a family of omnimodal world models designed to jointly process and generate language, image, video, audio, and action sequences within a unified mixture-of-t…
World Simulation with Video Foundation Models for Physical AI
NVIDIA, :, Arslan Ali +87
We introduce [Cosmos-Predict2.5], the latest generation of the Cosmos World Foundation Models for Physical AI. Built on a flow-based architecture, [Cosmos-Predict2.5] unifies Text2…
Cosmos World Foundation Model Platform for Physical AI
NVIDIA, :, Niket Agarwal +76
Physical AI needs to be trained digitally first. It needs a digital twin of itself, the policy model, and a digital twin of the world, the world model. In this paper, we present th…
We're Not Using Videos Effectively: An Updated Domain Adaptive Video Segmentation Baseline
Simar Kareer, Vivek Vijaykumar, Harsh Maheshwari +3
There has been abundant work in unsupervised domain adaptation for semantic segmentation (DAS) seeking to adapt a model trained on images from a labeled source domain to an unlabel…
SkyScenes: A Synthetic Dataset for Aerial Scene Understanding
Sahil Khose, Anisha Pal, Aayushi Agarwal +3
Real-world aerial scene understanding is limited by a lack of datasets that contain densely annotated images curated under a diverse set of conditions. Due to inherent challenges i…
AUGCAL: Improving Sim2Real Adaptation by Uncertainty Calibration on Augmented Synthetic Images
Prithvijit Chattopadhyay, Bharat Goyal, Boglarka Ecsedi +2
Synthetic data (SIM) drawn from simulators have emerged as a popular alternative for training models where acquiring annotated real-world images is difficult. However, transferring…
Battle of the Backbones: A Large-Scale Comparison of Pretrained Models across Computer Vision Tasks
Micah Goldblum, Hossein Souri, Renkun Ni +10
Neural network based computer vision systems are typically built on a backbone, a pretrained or randomly initialized feature extractor. Several years ago, the default option was an…
LANCE: Stress-testing Visual Models by Generating Language-guided Counterfactual Images
Viraj Prabhu, Sriram Yenamandra, Prithvijit Chattopadhyay +1
We propose an automated algorithm to stress-test a trained visual model by generating language-guided counterfactual test images (LANCE). Our method leverages recent progress in la…
Benchmarking Low-Shot Robustness to Natural Distribution Shifts
Aaditya Singh, Kartik Sarangmath, Prithvijit Chattopadhyay +1
Robustness to natural distribution shifts has seen remarkable progress thanks to recent pre-training strategies combined with better fine-tuning methods. However, such fine-tuning…
PASTA: Proportional Amplitude Spectrum Training Augmentation for Syn-to-Real Domain Generalization
Prithvijit Chattopadhyay, Kartik Sarangmath, Vivek Vijaykumar +1
Synthetic data offers the promise of cheap and bountiful training data for settings where labeled real-world data is scarce. However, models trained on synthetic data significantly…
RobustNav: Towards Benchmarking Robustness in Embodied Navigation
Prithvijit Chattopadhyay, Judy Hoffman, Roozbeh Mottaghi +1
As an attempt towards assessing the robustness of embodied navigation agents, we propose RobustNav, a framework to quantify the performance of embodied navigation agents when expos…
Learning to Balance Specificity and Invariance for In and Out of Domain Generalization
Prithvijit Chattopadhyay, Yogesh Balaji, Judy Hoffman
We introduce Domain-specific Masks for Generalization, a model for improving both in-domain and out-of-domain generalization performance. For domain generalization, the goal is to…
Likelihood Landscapes: A Unifying Principle Behind Many Adversarial Defenses
Fu Lin, Rohit Mittapalli, Prithvijit Chattopadhyay +2
Convolutional Neural Networks have been shown to be vulnerable to adversarial examples, which are known to locate in subspaces close to where normal data lies but are not naturally…
Choose Your Neuron: Incorporating Domain Knowledge through Neuron-Importance
Ramprasaath R. Selvaraju, Prithvijit Chattopadhyay, Mohamed Elhoseiny +4
Individual neurons in convolutional neural networks supervised for image-level classification tasks have been shown to implicitly learn semantically meaningful concepts ranging fro…
It Takes Two to Tango: Towards Theory of AI's Mind
Arjun Chandrasekaran, Deshraj Yadav, Prithvijit Chattopadhyay +2
Theory of Mind is the ability to attribute mental states (beliefs, intents, knowledge, perspectives, etc.) to others and recognize that these mental states may differ from one's ow…
Counting Everyday Objects in Everyday Scenes
Prithvijit Chattopadhyay, Ramakrishna Vedantam, Ramprasaath R. Selvaraju +2
We are interested in counting the number of instances of object classes in natural, everyday images. Previous counting approaches tackle the problem in restricted domains such as c…