16 citations · 28 across the 12 of their papers we have counts for
15 papers
Trokens: Semantic-Aware Relational Trajectory Tokens for Few-Shot Action Recognition
Pulkit Kumar, Shuaiyi Huang, Matthew Walmer +2
Video understanding requires effective modeling of both motion and appearance information, particularly for few-shot action recognition. While recent advances in point tracking hav…
Diffusion Autoencoders are Scalable Image Tokenizers
Yinbo Chen, Rohit Girdhar, Xiaolong Wang +2
Tokenizing images into compact visual representations is a key step in learning efficient and high-quality image generative models. We present a simple diffusion tokenizer (DiTo) t…
Movie Gen: A Cast of Media Foundation Models
Adam Polyak, Amit Zohar, Andrew Brown +85
We present Movie Gen, a cast of foundation models that generates high-quality, 1080p HD videos with different aspect ratios and synchronized audio. We also show additional capabili…
Trajectory-aligned Space-time Tokens for Few-shot Action Recognition
Pulkit Kumar, Namitha Padmanabhan, Luke Luo +2
We propose a simple yet effective approach for few-shot action recognition, emphasizing the disentanglement of motion and appearance representations. By harnessing recent progress…
UVIS: Unsupervised Video Instance Segmentation
Shuaiyi Huang, Saksham Suri, Kamal Gupta +3
Video instance segmentation requires classifying, segmenting, and tracking every object across video frames. Unlike existing approaches that rely on masks, boxes, or category label…
InstanceDiffusion: Instance-level Control for Image Generation
Xudong Wang, Trevor Darrell, Sai Saketh Rambhatla +2
Text-to-image diffusion models produce high quality images but do not offer control over individual instances in the image. We introduce InstanceDiffusion that adds precise instanc…
Emu Video: Factorizing Text-to-Video Generation by Explicit Image Conditioning
Rohit Girdhar, Mannat Singh, Andrew Brown +7
We present Emu Video, a text-to-video generation model that factorizes the generation into two steps: first generating an image conditioned on the text, and then generating a video…
SelfEval: Leveraging the discriminative nature of generative models for evaluation
Sai Saketh Rambhatla, Ishan Misra
We present an automated way to evaluate the text alignment of text-to-image generative diffusion models using standard image-text recognition datasets. Our method, called SelfEval,…
MOST: Multiple Object localization with Self-supervised Transformers for object discovery
Sai Saketh Rambhatla, Ishan Misra, Rama Chellappa +1
We tackle the challenging task of unsupervised object localization in this work. Recently, transformers trained with self-supervised learning have been shown to exhibit object loca…
Self-Denoising Neural Networks for Few Shot Learning
Steven Schwarcz, Sai Saketh Rambhatla, Rama Chellappa
In this paper, we introduce a new architecture for few shot learning, the task of teaching a neural network from as few as one or five labeled examples. Inspired by the theoretical…
To Boost or not to Boost: On the Limits of Boosted Neural Networks
Sai Saketh Rambhatla, Michael Jones, Rama Chellappa
Boosting is a method for finding a highly accurate hypothesis by linearly combining many ``weak" hypotheses, each of which may be only moderately accurate. Thus, boosting is a meth…
The Pursuit of Knowledge: Discovering and Localizing Novel Categories using Dual Memory
Sai Saketh Rambhatla, Rama Chellappa, Abhinav Shrivastava
We tackle object category discovery, which is the problem of discovering and localizing novel objects in a large unlabeled dataset. While existing methods show results on datasets…
Spatial Priming for Detecting Human-Object Interactions
Ankan Bansal, Sai Saketh Rambhatla, Abhinav Shrivastava +1
The relative spatial layout of a human and an object is an important cue for determining how they interact. However, until now, spatial layout has been used just as side-informatio…
A Dual-Path Model With Adaptive Attention For Vehicle Re-Identification
Pirazh Khorramshahi, Amit Kumar, Neehar Peri +3
In recent years, attention models have been extensively used for person and vehicle re-identification. Most re-identification methods are designed to focus attention on key-point l…
Detecting Human-Object Interactions via Functional Generalization
Ankan Bansal, Sai Saketh Rambhatla, Abhinav Shrivastava +1
We present an approach for detecting human-object interactions (HOIs) in images, based on the idea that humans interact with functionally similar objects in a similar manner. The p…