49 citations · 94 across the 7 of their papers we have counts for
8 papers
Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilities
Gheorghe Comanici, Eric Bieber, Mike Schaekermann +3431
In this report, we introduce the Gemini 2.X model family: Gemini 2.5 Pro and Gemini 2.5 Flash, as well as our earlier Gemini 2.0 Flash and Flash-Lite models. Gemini 2.5 Pro is our…
Rethinking Learning Approaches for Long-Term Action Anticipation
Megha Nawhal, Akash Abdu Jyothi, Greg Mori
Action anticipation involves predicting future actions having observed the initial portion of a video. Typically, the observed video is processed as a whole to obtain a video-level…
Piggyback GAN: Efficient Lifelong Learning for Image Conditioned Generation
Mengyao Zhai, Lei Chen, Jiawei He +3
Humans accumulate knowledge in a lifelong fashion. Modern deep neural networks, on the other hand, are susceptible to catastrophic forgetting: when adapted to perform new tasks, th…
Activity Graph Transformer for Temporal Action Localization
Megha Nawhal, Greg Mori
We introduce Activity Graph Transformer, an end-to-end learnable model for temporal action localization, that receives a video as input and directly predicts a set of action instan…
MCMI: Multi-Cycle Image Translation with Mutual Information Constraints
Xiang Xu, Megha Nawhal, Greg Mori +1
We present a mutual information-based framework for unsupervised image-to-image translation. Our MCMI approach treats single-cycle image translation models as modules that can be u…
Generating Videos of Zero-Shot Compositions of Actions and Objects
Megha Nawhal, Mengyao Zhai, Andreas Lehrmann +2
Human activity videos involve rich, varied interactions between people and objects. In this paper we develop methods for generating such videos -- making progress toward addressing…
Continuous Graph Flow
Zhiwei Deng, Megha Nawhal, Lili Meng +1
In this paper, we propose Continuous Graph Flow, a generative continuous flow based method that aims to model complex distributions of graph-structured data. Once learned, the mode…
Lifelong GAN: Continual Learning for Conditional Image Generation
Mengyao Zhai, Lei Chen, Fred Tung +3
Lifelong learning is challenging for deep neural networks due to their susceptibility to catastrophic forgetting. Catastrophic forgetting occurs when a trained network is not able…