39 citations · 102 across the 19 of their papers we have counts for
22 papers
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…
Fighting Malicious Media Data: A Survey on Tampering Detection and Deepfake Detection
Junke Wang, Zhenxin Li, Chao Zhang +4
Online media data, in the forms of images and videos, are becoming mainstream communication channels. However, recent advances in deep learning, particularly deep generative models…
TAG: Boosting Text-VQA via Text-aware Visual Question-answer Generation
Jun Wang, Mingfei Gao, Yuqian Hu +5
Text-VQA aims at answering questions that require understanding the textual cues in an image. Despite the great progress of existing Text-VQA methods, their performance suffers fro…
Neural Space-filling Curves
Hanyu Wang, Kamal Gupta, Larry Davis +1
We present Neural Space-filling Curves (SFCs), a data-driven approach to infer a context-based scan order for a set of images. Linear ordering of pixels forms the basis for many ap…
Leveraging Bitstream Metadata for Fast, Accurate, Generalized Compressed Video Quality Enhancement
Max Ehrlich, Jon Barker, Namitha Padmanabhan +4
Video compression is a central feature of the modern internet powering technologies from social media to video conferencing. While video compression continues to mature, for many c…
InvGAN: Invertible GANs
Partha Ghosh, Dominik Zietlow, Michael J. Black +2
Generation of photo-realistic images, semantic editing and representation learning are a few of many potential applications of high resolution generative models. Recent progress in…
Learning Realistic Human Reposing using Cyclic Self-Supervision with 3D Shape, Pose, and Appearance Consistency
Soubhik Sanyal, Alex Vorobiov, Timo Bolkart +5
Synthesizing images of a person in novel poses from a single image is a highly ambiguous task. Most existing approaches require paired training images; i.e. images of the same pers…
DiscoBox: Weakly Supervised Instance Segmentation and Semantic Correspondence from Box Supervision
Shiyi Lan, Zhiding Yu, Christopher Choy +5
We introduce DiscoBox, a novel framework that jointly learns instance segmentation and semantic correspondence using bounding box supervision. Specifically, we propose a self-ensem…
Rethinking Pseudo Labels for Semi-Supervised Object Detection
Hengduo Li, Zuxuan Wu, Abhinav Shrivastava +1
Recent advances in semi-supervised object detection (SSOD) are largely driven by consistency-based pseudo-labeling methods for image classification tasks, producing pseudo labels a…
More Than Just Attention: Improving Cross-Modal Attentions with Contrastive Constraints for Image-Text Matching
Yuxiao Chen, Jianbo Yuan, Long Zhao +4
Cross-modal attention mechanisms have been widely applied to the image-text matching task and have achieved remarkable improvements thanks to its capability of learning fine-graine…
VideoLT: Large-scale Long-tailed Video Recognition
Xing Zhang, Zuxuan Wu, Zejia Weng +4
Label distributions in real-world are oftentimes long-tailed and imbalanced, resulting in biased models towards dominant labels. While long-tailed recognition has been extensively…
Learned Spatial Representations for Few-shot Talking-Head Synthesis
Moustafa Meshry, Saksham Suri, Larry S. Davis +1
We propose a novel approach for few-shot talking-head synthesis. While recent works in neural talking heads have produced promising results, they can still produce images that do n…
M3DeTR: Multi-representation, Multi-scale, Mutual-relation 3D Object Detection with Transformers
Tianrui Guan, Jun Wang, Shiyi Lan +4
We present a novel architecture for 3D object detection, M3DeTR, which combines different point cloud representations (raw, voxels, bird-eye view) with different feature scales bas…
Dual Contrastive Loss and Attention for GANs
Ning Yu, Guilin Liu, Aysegul Dundar +4
Generative Adversarial Networks (GANs) produce impressive results on unconditional image generation when powered with large-scale image datasets. Yet generated images are still eas…
Responsible Disclosure of Generative Models Using Scalable Fingerprinting
Ning Yu, Vladislav Skripniuk, Dingfan Chen +2
Over the past years, deep generative models have achieved a new level of performance. Generated data has become difficult, if not impossible, to be distinguished from real data. Wh…
SLADE: A Self-Training Framework For Distance Metric Learning
Jiali Duan, Yen-Liang Lin, Son Tran +2
Most existing distance metric learning approaches use fully labeled data to learn the sample similarities in an embedding space. We present a self-training framework, SLADE, to imp…
Analyzing and Mitigating JPEG Compression Defects in Deep Learning
Max Ehrlich, Larry Davis, Ser-Nam Lim +1
With the proliferation of deep learning methods, many computer vision problems which were considered academic are now viable in the consumer setting. One drawback of consumer appli…
LayoutTransformer: Layout Generation and Completion with Self-attention
Kamal Gupta, Justin Lazarow, Alessandro Achille +3
We address the problem of scene layout generation for diverse domains such as images, mobile applications, documents, and 3D objects. Most complex scenes, natural or human-designed…
Inclusive GAN: Improving Data and Minority Coverage in Generative Models
Ning Yu, Ke Li, Peng Zhou +3
Generative Adversarial Networks (GANs) have brought about rapid progress towards generating photorealistic images. Yet the equitable allocation of their modeling capacity among sub…
Fashion Outfit Complementary Item Retrieval
Yen-Liang Lin, Son Tran, Larry S. Davis
Complementary fashion item recommendation is critical for fashion outfit completion. Existing methods mainly focus on outfit compatibility prediction but not in a retrieval setting…
A weakly supervised adaptive triplet loss for deep metric learning
Xiaonan Zhao, Huan Qi, Rui Luo +1
We address the problem of distance metric learning in visual similarity search, defined as learning an image embedding model which projects images into Euclidean space where semant…
Attributing Fake Images to GANs: Learning and Analyzing GAN Fingerprints
Ning Yu, Larry Davis, Mario Fritz
Recent advances in Generative Adversarial Networks (GANs) have shown increasing success in generating photorealistic images. But they also raise challenges to visual forensics and…