Search Results for author:
Found 42 papers, 21 papers with code
Neural Space-filling Curves
We present Neural Space-filling Curves (SFCs), a data-driven approach to infer a context-based scan order for a set of images.
Leveraging Bitstream Metadata for Fast, Accurate, Generalized Compressed Video Quality Enhancement
Video compression is a central feature of the modern internet powering technologies from social media to video conferencing.
Learning Realistic Human Reposing using Cyclic Self-Supervision with 3D Shape, Pose, and Appearance Consistency
Synthesizing images of a person in novel poses from a single image is a highly ambiguous task.
More Than Just Attention: Improving Cross-Modal Attentions with Contrastive Constraints for Image-Text Matching
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-grained relevance across different modalities.
Unsupervised Super-Resolution of Satellite Imagery for High Fidelity Material Label Transfer
Urban material recognition in remote sensing imagery is a highly relevant, yet extremely challenging problem due to the difficulty of obtaining human annotations, especially on low resolution satellite images.
VideoLT: Large-scale Long-tailed Video Recognition
In this paper, we introduce VideoLT, a large-scale long-tailed video recognition dataset, as a step toward real-world video recognition.
M3DeTR: Multi-representation, Multi-scale, Mutual-relation 3D Object Detection with Transformers
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 based on multi-scale feature pyramids.
Ranked #1 on
3D Object Detection
on KITTI Cars Hard val
Beyond Short Clips: End-to-End Video-Level Learning with Collaborative Memories
The standard way of training video models entails sampling at each iteration a single clip from a video and optimizing the clip prediction with respect to the video-level label.
Dual Contrastive Loss and Attention for GANs
Lastly, we study different attention architectures in the discriminator, and propose a reference attention mechanism.
Knowledge Evolution in Neural Networks
We evaluate KE using relatively small datasets (e. g., CUB-200) and randomly initialized deep networks.
SVMax: A Feature Embedding Regularizer
The SVMax regularizer supports both supervised and unsupervised learning.
Responsible Disclosure of Generative Models Using Scalable Fingerprinting
Over the past years, deep generative models have achieved a new level of performance.
The Lottery Ticket Hypothesis for Object Recognition
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.
Analyzing and Mitigating JPEG Compression Defects in Deep Learning
We show that there is a significant penalty on common performance metrics for high compression.
Hierarchical Contrastive Motion Learning for Video Action Recognition
Thus, the motion features at higher levels are trained to gradually capture semantic dynamics and evolve more discriminative for action recognition.
A Generic Visualization Approach for Convolutional Neural Networks
Compared to classification networks, attention visualization for retrieval networks is hardly studied.
ASAP-NMS: Accelerating Non-Maximum Suppression Using Spatially Aware Priors
The widely adopted sequential variant of Non Maximum Suppression (or Greedy-NMS) is a crucial module for object-detection pipelines.
LayoutTransformer: Layout Generation and Completion with Self-attention
Generating a new layout or extending an existing layout requires understanding the relationships between these primitives.
Quantization Guided JPEG Artifact Correction
The JPEG image compression algorithm is the most popular method of image compression because of its ability for large compression ratios.
Inclusive GAN: Improving Data and Minority Coverage in Generative Models
Generative Adversarial Networks (GANs) have brought about rapid progress towards generating photorealistic images.
WSLLN:Weakly Supervised Natural Language Localization Networks
We propose weakly supervised language localization networks (WSLLN) to detect events in long, untrimmed videos given language queries.
Making an Invisibility Cloak: Real World Adversarial Attacks on Object Detectors
We present a systematic study of adversarial attacks on state-of-the-art object detection frameworks.
A weakly supervised adaptive triplet loss for deep metric learning
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 semantically and visually similar images are closer and dissimilar images are further from one another.
Style-based Encoder Pre-training for Multi-modal Image Synthesis
Then we train a generator to transform an input image along with a style-code to the output domain.
STEP: Spatio-Temporal Progressive Learning for Video Action Detection
In this paper, we propose Spatio-TEmporal Progressive (STEP) action detector---a progressive learning framework for spatio-temporal action detection in videos.
Ranked #7 on
Action Detection
on UCF101-24
Unsupervised Data Uncertainty Learning in Visual Retrieval Systems
We introduce an unsupervised formulation to estimate heteroscedastic uncertainty in retrieval systems.
Boosting Standard Classification Architectures Through a Ranking Regularizer
We employ triplet loss as a feature embedding regularizer to boost classification performance.
Exploring Uncertainty in Conditional Multi-Modal Retrieval Systems
We cast visual retrieval as a regression problem by posing triplet loss as a regression loss.
Deep Residual Learning in the JPEG Transform Domain
We introduce a general method of performing Residual Network inference and learning in the JPEG transform domain that allows the network to consume compressed images as input.
Attributing Fake Images to GANs: Learning and Analyzing GAN Fingerprints
Our experiments show that (1) GANs carry distinct model fingerprints and leave stable fingerprints in their generated images, which support image attribution; (2) even minor differences in GAN training can result in different fingerprints, which enables fine-grained model authentication; (3) fingerprints persist across different image frequencies and patches and are not biased by GAN artifacts; (4) fingerprint finetuning is effective in immunizing against five types of adversarial image perturbations; and (5) comparisons also show our learned fingerprints consistently outperform several baselines in a variety of setups.
Two Stream Self-Supervised Learning for Action Recognition
The self-supervised pre-trained weights effectiveness is validated on the action recognition task.
Fused Deep Neural Networks for Efficient Pedestrian Detection
The classification system further classifies the generated candidates based on opinions of multiple deep verification networks and a fusion network which utilizes a novel soft-rejection fusion method to adjust the confidence in the detection results.
Learning to Color from Language
Automatic colorization is the process of adding color to greyscale images.
Deep Motion Boundary Detection
In this paper, we propose the first dedicated end-to-end deep learning approach for motion boundary detection, which we term as MoBoNet.
Class Subset Selection for Transfer Learning using Submodularity
To accomplish this, we use a submodular set function to model the accuracy achievable on a new task when the features have been learned on a given subset of classes of the source dataset.
Face-MagNet: Magnifying Feature Maps to Detect Small Faces
In this paper, we introduce the Face Magnifier Network (Face-MageNet), a face detector based on the Faster-RCNN framework which enables the flow of discriminative information of small scale faces to the classifier without any skip or residual connections.
Boundary-sensitive Network for Portrait Segmentation
Compared to the general semantic segmentation problem, portrait segmentation has higher precision requirement on boundary area.
SSH: Single Stage Headless Face Detector
Surprisingly, with a headless VGG-16, SSH beats the ResNet-101-based state-of-the-art on the WIDER dataset.
The Amazing Mysteries of the Gutter: Drawing Inferences Between Panels in Comic Book Narratives
While computers can now describe what is explicitly depicted in natural images, in this paper we examine whether they can understand the closure-driven narratives conveyed by stylized artwork and dialogue in comic book panels.
Weakly Supervised Learning of Heterogeneous Concepts in Videos
Typical textual descriptions that accompany online videos are 'weak': i. e., they mention the main concepts in the video but not their corresponding spatio-temporal locations.
Learning Discriminative Features via Label Consistent Neural Network
Deep Convolutional Neural Networks (CNN) enforces supervised information only at the output layer, and hidden layers are trained by back propagating the prediction error from the output layer without explicit supervision.
Learning Structured Ordinal Measures for Video based Face Recognition
This paper presents a structured ordinal measure method for video-based face recognition that simultaneously learns ordinal filters and structured ordinal features.
