Search Results for author: Abhinav Shrivastava

Found 53 papers, 21 papers with code

Diverse Video Generation using a Gaussian Process Trigger

1 code implementation ICLR 2021 Gaurav Shrivastava, Abhinav Shrivastava

Our approach, Diverse Video Generator, uses a Gaussian Process (GP) to learn priors on future states given the past and maintains a probability distribution over possible futures given a particular sample.

Video Generation

Learning Graphs for Knowledge Transfer With Limited Labels

no code implementations CVPR 2021 Pallabi Ghosh, Nirat Saini, Larry S. Davis, Abhinav Shrivastava

The standard paradigm is to utilize relationships in the input graph to transfer information using GCNs from training to testing nodes in the graph; for example, the semi-supervised, zero-shot, and few-shot learning setups.

Action Recognition Few-Shot Learning +1

Hierarchical Video Prediction Using Relational Layouts for Human-Object Interactions

no code implementations CVPR 2021 Navaneeth Bodla, Gaurav Shrivastava, Rama Chellappa, Abhinav Shrivastava

Our work builds on hierarchical video prediction models, which disentangle the video generation process into two stages: predicting a high-level representation, such as pose sequence, and then learning a pose-to-pixels translation model for pixel generation.

Human-Object Interaction Detection Relational Reasoning +3

Learning to Predict Visual Attributes in the Wild

no code implementations CVPR 2021 Khoi Pham, Kushal Kafle, Zhe Lin, Zhihong Ding, Scott Cohen, Quan Tran, Abhinav Shrivastava

In this paper, we introduce a large-scale in-the-wild visual attribute prediction dataset consisting of over 927K attribute annotations for over 260K object instances.

Contrastive Learning Multi-Label Classification

Rethinking Pseudo Labels for Semi-Supervised Object Detection

no code implementations1 Jun 2021 Hengduo Li, Zuxuan Wu, Abhinav Shrivastava, Larry S. Davis

In this paper, we introduce certainty-aware pseudo labels tailored for object detection, which can effectively estimate the classification and localization quality of derived pseudo labels.

Classification Image Classification +2

Towards Discovery and Attribution of Open-world GAN Generated Images

no code implementations ICCV 2021 Sharath Girish, Saksham Suri, Saketh Rambhatla, Abhinav Shrivastava

Through extensive experiments, we show that our algorithm discovers unseen GANs with high accuracy and also generalizes to GANs trained on unseen real datasets.

Out-of-Distribution Detection

The Pursuit of Knowledge: Discovering and Localizing Novel Categories using Dual Memory

no code implementations ICCV 2021 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.

Learned Spatial Representations for Few-shot Talking-Head Synthesis

no code implementations ICCV 2021 Moustafa Meshry, Saksham Suri, Larry S. Davis, Abhinav Shrivastava

In contrast, we propose to factorize the representation of a subject into its spatial and style components.

StEP: Style-based Encoder Pre-training for Multi-modal Image Synthesis

no code implementations CVPR 2021 Moustafa Meshry, Yixuan Ren, Larry S Davis, Abhinav Shrivastava

Specifically, we pre-train a generic style encoder using a novel proxy task to learn an embedding of images, from arbitrary domains, into a low-dimensional style latent space.

Image Generation Translation

Knowledge Evolution in Neural Networks

1 code implementation CVPR 2021 Ahmed Taha, Abhinav Shrivastava, Larry Davis

We evaluate KE using relatively small datasets (e. g., CUB-200) and randomly initialized deep networks.

Metric Learning

SVMax: A Feature Embedding Regularizer

1 code implementation4 Mar 2021 Ahmed Taha, Alex Hanson, Abhinav Shrivastava, Larry Davis

The SVMax regularizer supports both supervised and unsupervised learning.

Deep Video Inpainting Detection

no code implementations26 Jan 2021 Peng Zhou, Ning Yu, Zuxuan Wu, Larry S. Davis, Abhinav Shrivastava, Ser-Nam Lim

This paper studies video inpainting detection, which localizes an inpainted region in a video both spatially and temporally.

Video Inpainting

Multimodal Attention for Layout Synthesis in Diverse Domains

no code implementations1 Jan 2021 Kamal Gupta, Vijay Mahadevan, Alessandro Achille, Justin Lazarow, Larry S. Davis, Abhinav Shrivastava

We address the problem of scene layout generation for diverse domains such as images, mobile applications, documents and 3D objects.

Learning What Not to Model: Gaussian Process Regression with Negative Constraints

no code implementations1 Jan 2021 Gaurav Shrivastava, Harsh Shrivastava, Abhinav Shrivastava

But, what if for an input point '$\bar{\mathbf{x}}$', we want to constrain the GP to avoid a target regression value '$\bar{y}(\bar{\mathbf{x}})$' (a negative datapair)?

GTA: Global Temporal Attention for Video Action Understanding

no code implementations15 Dec 2020 Bo He, Xitong Yang, Zuxuan Wu, Hao Chen, Ser-Nam Lim, Abhinav Shrivastava

To this end, we introduce Global Temporal Attention (GTA), which performs global temporal attention on top of spatial attention in a decoupled manner.

Action Recognition Action Understanding

The Lottery Ticket Hypothesis for Object Recognition

1 code implementation CVPR 2021 Sharath Girish, Shishira R. Maiya, Kamal Gupta, Hao Chen, Larry Davis, Abhinav Shrivastava

The recently proposed Lottery Ticket Hypothesis (LTH) states that deep neural networks trained on large datasets contain smaller subnetworks that achieve on par performance as the dense networks.

Instance Segmentation Object Detection +2

Analyzing and Mitigating JPEG Compression Defects in Deep Learning

no code implementations17 Nov 2020 Max Ehrlich, Larry Davis, Ser-Nam Lim, Abhinav Shrivastava

We show that there is a significant penalty on common performance metrics for high compression.

Learning Visual Representations for Transfer Learning by Suppressing Texture

1 code implementation3 Nov 2020 Shlok Mishra, Anshul Shah, Ankan Bansal, Jonghyun Choi, Abhinav Shrivastava, Abhishek Sharma, David Jacobs

Recent literature has shown that features obtained from supervised training of CNNs may over-emphasize texture rather than encoding high-level information.

Image Classification Object Detection +2

Pose And Joint-Aware Action Recognition

no code implementations16 Oct 2020 Anshul Shah, Shlok Mishra, Ankan Bansal, Jun-Cheng Chen, Rama Chellappa, Abhinav Shrivastava

Our model also outperforms the baseline on Mimetics, a dataset with out-of-context videos by 1. 14% while using only pose heatmaps.

Action Recognition Data Augmentation

Improved Modeling of 3D Shapes with Multi-view Depth Maps

1 code implementation7 Sep 2020 Kamal Gupta, Susmija Jabbireddy, Ketul Shah, Abhinav Shrivastava, Matthias Zwicker

Our simple encoder-decoder framework, comprised of a novel identity encoder and class-conditional viewpoint generator, generates 3D consistent depth maps.

Image Generation

Deep Co-Training with Task Decomposition for Semi-Supervised Domain Adaptation

1 code implementation ICCV 2021 Luyu Yang, Yan Wang, Mingfei Gao, Abhinav Shrivastava, Kilian Q. Weinberger, Wei-Lun Chao, Ser-Nam Lim

To integrate the strengths of the two classifiers, we apply the well-established co-training framework, in which the two classifiers exchange their high confident predictions to iteratively "teach each other" so that both classifiers can excel in the target domain.

Unsupervised Domain Adaptation

End-to-end Learning of Compressible Features

no code implementations23 Jul 2020 Saurabh Singh, Sami Abu-El-Haija, Nick Johnston, Johannes Ballé, Abhinav Shrivastava, George Toderici

We propose a learned method that jointly optimizes for compressibility along with the task objective for learning the features.


Curriculum Manager for Source Selection in Multi-Source Domain Adaptation

no code implementations ECCV 2020 Luyu Yang, Yogesh Balaji, Ser-Nam Lim, Abhinav Shrivastava

In this paper, we proposed an adversarial agent that learns a dynamic curriculum for source samples, called Curriculum Manager for Source Selection (CMSS).

Multi-Source Unsupervised Domain Adaptation Unsupervised Domain Adaptation

Group Ensemble: Learning an Ensemble of ConvNets in a single ConvNet

1 code implementation1 Jul 2020 Hao Chen, Abhinav Shrivastava

Owing to group convolution and the shared-base, GENet can fully leverage the advantage of explicit ensemble learning while retaining the same computation as a single ConvNet.

Action Recognition Ensemble Learning +1

LayoutTransformer: Layout Generation and Completion with Self-attention

2 code implementations ICCV 2021 Kamal Gupta, Justin Lazarow, Alessandro Achille, Larry Davis, Vijay Mahadevan, Abhinav Shrivastava

Generating a new layout or extending an existing layout requires understanding the relationships between these primitives.

Quantization Guided JPEG Artifact Correction

1 code implementation ECCV 2020 Max Ehrlich, Larry Davis, Ser-Nam Lim, Abhinav Shrivastava

The JPEG image compression algorithm is the most popular method of image compression because of its ability for large compression ratios.

JPEG Artifact Correction Quantization

Spatial Priming for Detecting Human-Object Interactions

no code implementations9 Apr 2020 Ankan Bansal, Sai Saketh Rambhatla, Abhinav Shrivastava, Rama Chellappa

The proposed method consists of a layout module which primes a visual module to predict the type of interaction between a human and an object.

Human-Object Interaction Detection

PatchVAE: Learning Local Latent Codes for Recognition

1 code implementation CVPR 2020 Kamal Gupta, Saurabh Singh, Abhinav Shrivastava

Unsupervised representation learning holds the promise of exploiting large amounts of unlabeled data to learn general representations.

Unsupervised Representation Learning

Hand-Priming in Object Localization for Assistive Egocentric Vision

no code implementations28 Feb 2020 Kyungjun Lee, Abhinav Shrivastava, Hernisa Kacorri

Egocentric vision holds great promises for increasing access to visual information and improving the quality of life for people with visual impairments, with object recognition being one of the daily challenges for this population.

Hand Segmentation Multi-Task Learning +2

Depth Completion Using a View-constrained Deep Prior

no code implementations21 Jan 2020 Pallabi Ghosh, Vibhav Vineet, Larry S. Davis, Abhinav Shrivastava, Sudipta Sinha, Neel Joshi

Given color images and noisy and incomplete target depth maps, we optimize a randomly-initialized CNN model to reconstruct a depth map restored by virtue of using the CNN network structure as a prior combined with a view-constrained photo-consistency loss.

Depth Completion Image Denoising

Scalable Model Compression by Entropy Penalized Reparameterization

no code implementations ICLR 2020 Deniz Oktay, Johannes Ballé, Saurabh Singh, Abhinav Shrivastava

We describe a simple and general neural network weight compression approach, in which the network parameters (weights and biases) are represented in a "latent" space, amounting to a reparameterization.

Classification General Classification +1

Render4Completion: Synthesizing Multi-View Depth Maps for 3D Shape Completion

no code implementations17 Apr 2019 Tao Hu, Zhizhong Han, Abhinav Shrivastava, Matthias Zwicker

Different from image-to-image translation network that completes each view separately, our novel network, multi-view completion net (MVCN), leverages information from all views of a 3D shape to help the completion of each single view.

Image-to-Image Translation Translation

Detecting Human-Object Interactions via Functional Generalization

no code implementations5 Apr 2019 Ankan Bansal, Sai Saketh Rambhatla, Abhinav Shrivastava, Rama Chellappa

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.

Human-Object Interaction Detection

Unsupervised Data Uncertainty Learning in Visual Retrieval Systems

no code implementations7 Feb 2019 Ahmed Taha, Yi-Ting Chen, Teruhisa Misu, Abhinav Shrivastava, Larry Davis

We introduce an unsupervised formulation to estimate heteroscedastic uncertainty in retrieval systems.

Video Retrieval

Generate, Segment and Refine: Towards Generic Manipulation Segmentation

1 code implementation24 Nov 2018 Peng Zhou, Bor-Chun Chen, Xintong Han, Mahyar Najibi, Abhinav Shrivastava, Ser Nam Lim, Larry S. Davis

The advent of image sharing platforms and the easy availability of advanced photo editing software have resulted in a large quantities of manipulated images being shared on the internet.

Detecting Image Manipulation Image Generation +3

Actor-Centric Relation Network

1 code implementation ECCV 2018 Chen Sun, Abhinav Shrivastava, Carl Vondrick, Kevin Murphy, Rahul Sukthankar, Cordelia Schmid

A visualization of the learned relation features confirms that our approach is able to attend to the relevant relations for each action.

Action Classification Action Detection +2

Training Region-based Object Detectors with Online Hard Example Mining

5 code implementations CVPR 2016 Abhinav Shrivastava, Abhinav Gupta, Ross Girshick

Our motivation is the same as it has always been -- detection datasets contain an overwhelming number of easy examples and a small number of hard examples.

Object Detection

Cross-stitch Networks for Multi-task Learning

2 code implementations CVPR 2016 Ishan Misra, Abhinav Shrivastava, Abhinav Gupta, Martial Hebert

In this paper, we propose a principled approach to learn shared representations in ConvNets using multi-task learning.

Multi-Task Learning

Watch and Learn: Semi-Supervised Learning of Object Detectors from Videos

no code implementations21 May 2015 Ishan Misra, Abhinav Shrivastava, Martial Hebert

We present a semi-supervised approach that localizes multiple unknown object instances in long videos.

Object Detection

Mid-level Elements for Object Detection

no code implementations27 Apr 2015 Aayush Bansal, Abhinav Shrivastava, Carl Doersch, Abhinav Gupta

Building on the success of recent discriminative mid-level elements, we propose a surprisingly simple approach for object detection which performs comparable to the current state-of-the-art approaches on PASCAL VOC comp-3 detection challenge (no external data).

Object Detection

Enriching Visual Knowledge Bases via Object Discovery and Segmentation

no code implementations CVPR 2014 Xinlei Chen, Abhinav Shrivastava, Abhinav Gupta

In this paper, we propose to enrich these knowledge bases by automatically discovering objects and their segmentations from noisy Internet images.

Object Discovery

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