334 citations · 883 across the 97 of their papers we have counts for
19 papers · 1 filter
Video Dynamics Prior: An Internal Learning Approach for Robust Video Enhancements
Gaurav Shrivastava, Ser-Nam Lim, Abhinav Shrivastava
In this paper, we present a novel robust framework for low-level vision tasks, including denoising, object removal, frame interpolation, and super-resolution, that does not require…
Gen2Det: Generate to Detect
Saksham Suri, Fanyi Xiao, Animesh Sinha +4
Recently diffusion models have shown improvement in synthetic image quality as well as better control in generation. We motivate and present Gen2Det, a simple modular pipeline to c…
Multimodality-guided Image Style Transfer using Cross-modal GAN Inversion
Hanyu Wang, Pengxiang Wu, Kevin Dela Rosa +2
Image Style Transfer (IST) is an interdisciplinary topic of computer vision and art that continuously attracts researchers' interests. Different from traditional Image-guided Image…
EAGLES: Efficient Accelerated 3D Gaussians with Lightweight EncodingS
Sharath Girish, Kamal Gupta, Abhinav Shrivastava
Recently, 3D Gaussian splatting (3D-GS) has gained popularity in novel-view scene synthesis. It addresses the challenges of lengthy training times and slow rendering speeds associa…
A Video is Worth 10,000 Words: Training and Benchmarking with Diverse Captions for Better Long Video Retrieval
Matthew Gwilliam, Michael Cogswell, Meng Ye +3
Existing long video retrieval systems are trained and tested in the paragraph-to-video retrieval regime, where every long video is described by a single long paragraph. This neglec…
Do text-free diffusion models learn discriminative visual representations?
Soumik Mukhopadhyay, Matthew Gwilliam, Yosuke Yamaguchi +6
While many unsupervised learning models focus on one family of tasks, either generative or discriminative, we explore the possibility of a unified representation learner: a model w…
Multi-entity Video Transformers for Fine-Grained Video Representation Learning
Matthew Walmer, Rose Kanjirathinkal, Kai Sheng Tai +3
The area of temporally fine-grained video representation learning focuses on generating frame-by-frame representations for temporally dense tasks, such as fine-grained action phase…
SHACIRA: Scalable HAsh-grid Compression for Implicit Neural Representations
Sharath Girish, Abhinav Shrivastava, Kamal Gupta
Implicit Neural Representations (INR) or neural fields have emerged as a popular framework to encode multimedia signals such as images and radiance fields while retaining high-qual…
Chop & Learn: Recognizing and Generating Object-State Compositions
Nirat Saini, Hanyu Wang, Archana Swaminathan +4
Recognizing and generating object-state compositions has been a challenging task, especially when generalizing to unseen compositions. In this paper, we study the task of cutting o…
Diff2Lip: Audio Conditioned Diffusion Models for Lip-Synchronization
Soumik Mukhopadhyay, Saksham Suri, Ravi Teja Gadde +1
The task of lip synchronization (lip-sync) seeks to match the lips of human faces with different audio. It has various applications in the film industry as well as for creating vir…
Diffusion Models Beat GANs on Image Classification
Soumik Mukhopadhyay, Matthew Gwilliam, Vatsal Agarwal +5
While many unsupervised learning models focus on one family of tasks, either generative or discriminative, we explore the possibility of a unified representation learner: a model w…
SimpSON: Simplifying Photo Cleanup with Single-Click Distracting Object Segmentation Network
Chuong Huynh, Yuqian Zhou, Zhe Lin +4
In photo editing, it is common practice to remove visual distractions to improve the overall image quality and highlight the primary subject. However, manually selecting and removi…
HNeRV: A Hybrid Neural Representation for Videos
Hao Chen, Matt Gwilliam, Ser-Nam Lim +1
Implicit neural representations store videos as neural networks and have performed well for various vision tasks such as video compression and denoising. With frame index or positi…
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…
ASIC: Aligning Sparse in-the-wild Image Collections
Kamal Gupta, Varun Jampani, Carlos Esteves +4
We present a method for joint alignment of sparse in-the-wild image collections of an object category. Most prior works assume either ground-truth keypoint annotations or a large d…
FlexNeRF: Photorealistic Free-viewpoint Rendering of Moving Humans from Sparse Views
Vinoj Jayasundara, Amit Agrawal, Nicolas Heron +2
We present FlexNeRF, a method for photorealistic freeviewpoint rendering of humans in motion from monocular videos. Our approach works well with sparse views, which is a challengin…
Towards Scalable Neural Representation for Diverse Videos
Bo He, Xitong Yang, Hanyu Wang +6
Implicit neural representations (INR) have gained increasing attention in representing 3D scenes and images, and have been recently applied to encode videos (e.g., NeRV, E-NeRV). W…
Align and Attend: Multimodal Summarization with Dual Contrastive Losses
Bo He, Jun Wang, Jielin Qiu +3
The goal of multimodal summarization is to extract the most important information from different modalities to form output summaries. Unlike the unimodal summarization, the multimo…
COVID-VTS: Fact Extraction and Verification on Short Video Platforms
Fuxiao Liu, Yaser Yacoob, Abhinav Shrivastava
We introduce a new benchmark, COVID-VTS, for fact-checking multi-modal information involving short-duration videos with COVID19- focused information from both the real world and ma…