Search Results for author:
Found 32 papers, 10 papers with code
Data Pruning via Separability, Integrity, and Model Uncertainty-Aware Importance Sampling
This paper improves upon existing data pruning methods for image classification by introducing a novel pruning metric and pruning procedure based on importance sampling.
FPGAN-Control: A Controllable Fingerprint Generator for Training with Synthetic Data
We demonstrate the merits of FPGAN-Control, both quantitatively and qualitatively, in terms of identity preservation level, degree of appearance control, and low synthetic-to-real domain gap.
Shape of You: Precise 3D shape estimations for diverse body types
This paper presents Shape of You (SoY), an approach to improve the accuracy of 3D body shape estimation for vision-based clothing recommendation systems.
Ranked #4 on
3D Human Shape Estimation
on SSP-3D
GPT4Rec: A Generative Framework for Personalized Recommendation and User Interests Interpretation
The generated queries naturally serve as interpretable representations of user interests and can be searched to recommend cold-start items.
Asymmetric Image Retrieval with Cross Model Compatible Ensembles
In this setting, a large model is used for indexing the gallery while a lightweight model is used for querying.
Contrastive Learning for Interactive Recommendation in Fashion
Recommender systems and search are both indispensable in facilitating personalization and ease of browsing in online fashion platforms.
Identity Preserving Loss for Learned Image Compression
We present a novel Identity Preserving Reconstruction (IPR) loss function which achieves Bits-Per-Pixel (BPP) values that are ~38% and ~42% of CRF-23 HEVC compression for LFW (low-resolution) and CelebA-HQ (high-resolution) datasets, respectively, while maintaining parity in recognition accuracy.
OutfitTransformer: Learning Outfit Representations for Fashion Recommendation
For compatibility prediction, we design an outfit token to capture a global outfit representation and train the framework using a classification loss.
Efficient Video Instance Segmentation via Tracklet Query and Proposal
In addition, VisTR is not fully end-to-end learnable in multiple video clips as it requires a hand-crafted data association to link instance tracklets between successive clips.
Synthetic Data for Model Selection
In contrast to using synthetic data for training, in this work we explore whether synthetic data can be beneficial for model selection.
Energy-Based Learning for Scene Graph Generation
The proposed formulation allows for efficiently incorporating the structure of scene graphs in the output space.
Ranked #4 on
Scene Graph Generation
on Visual Genome
GAN-Control: Explicitly Controllable GANs
We present a framework for training GANs with explicit control over generated images.
From Real to Synthetic and Back: Synthesizing Training Data for Multi-Person Scene Understanding
We present a method for synthesizing naturally looking images of multiple people interacting in a specific scenario.
AOWS: Adaptive and optimal network width search with latency constraints
We introduce a novel efficient one-shot NAS approach to optimally search for channel numbers, given latency constraints on a specific hardware.
MILA: Multi-Task Learning from Videos via Efficient Inter-Frame Attention
We embed the attention module in a ``slow-fast'' architecture, where the slower network runs on sparsely sampled keyframes and the light-weight shallow network runs on non-keyframes at a high frame rate.
ExpNet: Landmark-Free, Deep, 3D Facial Expressions
Our ExpNet CNN is applied directly to the intensities of a face image and regresses a 29D vector of 3D expression coefficients.
Ranked #1 on
3D Facial Expression Recognition
on 2017_test set
(using extra training data)
Extreme 3D Face Reconstruction: Seeing Through Occlusions
Motivated by the concept of bump mapping, we propose a layered approach which decouples estimation of a global shape from its mid-level details (e. g., wrinkles).
FacePoseNet: Making a Case for Landmark-Free Face Alignment
Instead, we compare our FPN with existing methods by evaluating how they affect face recognition accuracy on the IJB-A and IJB-B benchmarks: using the same recognition pipeline, but varying the face alignment method.
Ranked #1 on
Facial Landmark Detection
on 300W
(Mean Error Rate metric)
On Face Segmentation, Face Swapping, and Face Perception
To this end, we use the Labeled Faces in the Wild (LFW) benchmark and measure the effect of intra- and inter-subject face swapping on recognition.
Deep 3D Face Identification
We also propose a 3D face augmentation technique which synthesizes a number of different facial expressions from a single 3D face scan.
Regressing Robust and Discriminative 3D Morphable Models with a very Deep Neural Network
The 3D shapes of faces are well known to be discriminative.
Ranked #4 on
3D Face Reconstruction
on Florence
(Average 3D Error metric)
Graph-Based Manifold Frequency Analysis for Denoising
We propose a new framework for manifold denoising based on processing in the graph Fourier frequency domain, derived from the spectral decomposition of the discrete graph Laplacian.
Pooling Faces: Template based Face Recognition with Pooled Face Images
We propose a novel approach to template based face recognition.
Pose-Aware Face Recognition in the Wild
We propose a method to push the frontiers of unconstrained face recognition in the wild, focusing on the problem of extreme pose variations.
Capturing Dynamic Textured Surfaces of Moving Targets
We present an end-to-end system for reconstructing complete watertight and textured models of moving subjects such as clothed humans and animals, using only three or four handheld sensors.
Exploring Local Context for Multi-target Tracking in Wide Area Aerial Surveillance
Tracking many vehicles in wide coverage aerial imagery is crucial for understanding events in a large field of view.
Face Recognition Using Deep Multi-Pose Representations
In our representation, a face image is processed by several pose-specific deep convolutional neural network (CNN) models to generate multiple pose-specific features.
Ranked #15 on
Face Verification
on IJB-A
Do We Really Need to Collect Millions of Faces for Effective Face Recognition?
Face recognition capabilities have recently made extraordinary leaps.
Ranked #13 on
Face Verification
on IJB-A
A Closed-Form Solution to Tensor Voting: Theory and Applications
We prove a closed-form solution to tensor voting (CFTV): given a point set in any dimensions, our closed-form solution provides an exact, continuous and efficient algorithm for computing a structure-aware tensor that simultaneously achieves salient structure detection and outlier attenuation.
Facial Landmark Detection with Tweaked Convolutional Neural Networks
We present a novel convolutional neural network (CNN) design for facial landmark coordinate regression.
3D Modeling from Wide Baseline Range Scans using Contour Coherence
We propose here a novel approach which leverages contour coherence and allows us to align two wide baseline range scans with limited overlap from a poor initialization.
Persistent Tracking for Wide Area Aerial Surveillance
Persistent surveillance of large geographic areas from unmanned aerial vehicles allows us to learn much about the daily activities in the region of interest.
