Search Results for author:
Found 57 papers, 25 papers with code
Driver Attention Tracking and Analysis
This network has a camera calibration module that can compute an embedding vector that represents the spatial configuration between the driver and the camera system.
Blur2Blur: Blur Conversion for Unsupervised Image Deblurring on Unknown Domains
This algorithm works by transforming a blurry input image, which is challenging to deblur, into another blurry image that is more amenable to deblurring.
HanDiffuser: Text-to-Image Generation With Realistic Hand Appearances
To generate images with realistic hands, we propose a novel diffusion-based architecture called HanDiffuser that achieves realism by injecting hand embeddings in the generative process.
Count What You Want: Exemplar Identification and Few-shot Counting of Human Actions in the Wild
To develop and evaluate our approach, we introduce a diverse and realistic dataset consisting of real-world data from 37 subjects and 50 action categories, encompassing both sensor and audio data.
Interactive Class-Agnostic Object Counting
The user can provide feedback by selecting a region with obvious counting errors and specifying the range for the estimated number of objects within it.
HyperCUT: Video Sequence from a Single Blurry Image using Unsupervised Ordering
We consider the challenging task of training models for image-to-video deblurring, which aims to recover a sequence of sharp images corresponding to a given blurry image input.
Gazeformer: Scalable, Effective and Fast Prediction of Goal-Directed Human Attention
In response, we pose a new task called ZeroGaze, a new variant of zero-shot learning where gaze is predicted for never-before-searched objects, and we develop a novel model, Gazeformer, to solve the ZeroGaze problem.
Unifying Top-down and Bottom-up Scanpath Prediction Using Transformers
Most models of visual attention aim at predicting either top-down or bottom-up control, as studied using different visual search and free-viewing tasks.
Object Detection With Self-Supervised Scene Adaptation
This paper proposes a novel method to improve the performance of a trained object detector on scenes with fixed camera perspectives based on self-supervised adaptation.
Patch-level Gaze Distribution Prediction for Gaze Following
Gaze following aims to predict where a person is looking in a scene, by predicting the target location, or indicating that the target is located outside the image.
Self-Supervised Learning with Multi-View Rendering for 3D Point Cloud Analysis
Inspired by the success of deep learning in the image domain, we devise a novel pre-training technique for better model initialization by utilizing the multi-view rendering of the 3D data.
Text-Derived Knowledge Helps Vision: A Simple Cross-modal Distillation for Video-based Action Anticipation
Anticipating future actions in a video is useful for many autonomous and assistive technologies.
Few-shot Object Counting and Detection
We tackle a new task of few-shot object counting and detection.
Ranked #10 on
Object Counting
on FSC147
Target-absent Human Attention
In this paper, we propose the first data-driven computational model that addresses the search-termination problem and predicts the scanpath of search fixations made by people searching for targets that do not appear in images.
Exemplar Free Class Agnostic Counting
We tackle the task of Class Agnostic Counting, which aims to count objects in a novel object category at test time without any access to labeled training data for that category.
Forward Propagation, Backward Regression, and Pose Association for Hand Tracking in the Wild
The first stage is Forward Propagation, where the features from frame t-1 are propagated to frame t based on previously detected hands and their estimated motion.
Ranked #1 on
Multiple Object Tracking
on YouTube-Hands
(using extra training data)
Whose Hands Are These? Hand Detection and Hand-Body Association in the Wild
We also introduce a new challenging dataset called BodyHands containing unconstrained images with hand and their corresponding body locations annotations.
Vicinal Counting Networks
Given an image containing multiple objects of a novel visual category and few exemplar bounding boxes depicting the visual category of interest, we want to count all of the instances of the desired visual category in the image.
Toward Realistic Single-View 3D Object Reconstruction with Unsupervised Learning from Multiple Images
In this paper, we eliminate the symmetry requirement with a novel unsupervised algorithm that can learn a 3D reconstruction network from a multi-image dataset.
Explore Image Deblurring via Encoded Blur Kernel Space
This paper introduces a method to encode the blur operators of an arbitrary dataset of sharp-blur image pairs into a blur kernel space.
Dictionary-Guided Scene Text Recognition
Language prior plays an important role in the way humans perceive and recognize text in the wild.
Learning To Count Everything
We also present a novel adaptation strategy to adapt our network to any novel visual category at test time, using only a few exemplar objects from the novel category.
Ranked #13 on
Object Counting
on FSC147
Progressive Semantic Segmentation
In this work, we present MagNet, a multi-scale framework that resolves local ambiguity by looking at the image at multiple magnification levels.
Ranked #3 on
Land Cover Classification
on DeepGlobe
Lipstick ain't enough: Beyond Color Matching for In-the-Wild Makeup Transfer
However, existing works overlooked the latter components and confined makeup transfer to color manipulation, focusing only on light makeup styles.
Ranked #1 on
Facial Makeup Transfer
on CPM-Synt-2
Explore Image Deblurring via Blur Kernel Space
This paper introduces a method to encode the blur operators of an arbitrary dataset of sharp-blur image pairs into a blur kernel space.
Blind Image Deblurring
Facial Expression Recognition (FER)
+1
FineNet: Frame Interpolation and Enhancement for Face Video Deblurring
The objective of this work is to deblur face videos.
Localization in the Crowd with Topological Constraints
Due to various challenges, a localization method is prone to spatial semantic errors, i. e., predicting multiple dots within a same person or collapsing multiple dots in a cluttered region.
Structural and Functional Decomposition for Personality Image Captioning in a Communication Game
Personality image captioning (PIC) aims to describe an image with a natural language caption given a personality trait.
Detecting Hands and Recognizing Physical Contact in the Wild
The first attention mechanism is based on the hand and a region's affinity, enclosing the hand and the object, and densely pools features from this region to the hand region.
Uncertainty Estimation and Sample Selection for Crowd Counting
We present sample selection strategies which make use of the density and uncertainty of predictions from the networks trained on one domain to select the informative images from a target domain of interest to acquire human annotation.
Distribution Matching for Crowd Counting
Existing crowd counting methods need to use a Gaussian to smooth each annotated dot or to estimate the likelihood of every pixel given the annotated point.
Ranked #3 on
Crowd Counting
on UCF CC 50
A Study of Human Gaze Behavior During Visual Crowd Counting
Analyzing the collected gaze behavior of ten human participants on thirty crowd images, we observe some common approaches for visual counting.
Interactive Visual Study of Multiple Attributes Learning Model of X-Ray Scattering Images
Existing interactive visualization tools for deep learning are mostly applied to the training, debugging, and refinement of neural network models working on natural images.
Predicting Goal-directed Human Attention Using Inverse Reinforcement Learning
These maps were learned by IRL and then used to predict behavioral scanpaths for multiple target categories.
Predicting Goal-directed Attention Control Using Inverse-Reinforcement Learning
Using machine learning and the psychologically-meaningful principle of reward, it is possible to learn the visual features used in goal-directed attention control.
Visual Understanding of Multiple Attributes Learning Model of X-Ray Scattering Images
This extended abstract presents a visualization system, which is designed for domain scientists to visually understand their deep learning model of extracting multiple attributes in x-ray scattering images.
Attentive Action and Context Factorization
This is a challenging task due to the subtlety of human actions in video and the co-occurrence of contextual elements.
Contextual Attention for Hand Detection in the Wild
We also conduct ablation studies on hand detection to show the effectiveness of the proposed contextual attention module.
Knowledge Distillation for Human Action Anticipation
In this paper, we propose a novel knowledge distillation framework that uses an action recognition network to supervise the training of an action anticipation network, guiding the latter to attend to the relevant information needed for correctly anticipating the future actions.
BusyHands: A Hand-Tool Interaction Database for Assembly Tasks Semantic Segmentation
Visual segmentation has seen tremendous advancement recently with ready solutions for a wide variety of scene types, including human hands and other body parts.
GIF2Video: Color Dequantization and Temporal Interpolation of GIF images
In this paper, we propose GIF2Video, the first learning-based method for enhancing the visual quality of GIFs in the wild.
Fake Sentence Detection as a Training Task for Sentence Encoding
We automatically generate fake sentences by corrupting original sentences from a source collection and train the encoders to produce representations that are effective at detecting fake sentences.
Iterative Crowd Counting
In this work, we tackle the problem of crowd counting in images.
Ranked #10 on
Crowd Counting
on UCF CC 50
Good View Hunting: Learning Photo Composition From Dense View Pairs
Finding views with good photo composition is a challenging task for machine learning methods.
Pulling Actions out of Context: Explicit Separation for Effective Combination
The ability to recognize human actions in video has many potential applications.
A+D Net: Training a Shadow Detector with Adversarial Shadow Attenuation
The A-Net modifies the original training images constrained by a simplified physical shadow model and is focused on fooling the D-Net's shadow predictions.
Ranked #4 on
Shadow Detection
on SBU
Shadow Detection With Conditional Generative Adversarial Networks
We introduce scGAN, a novel extension of conditional Generative Adversarial Networks (GAN) tailored for the challenging problem of shadow detection in images.
Ranked #6 on
RGB Salient Object Detection
on ISTD
Eigen Evolution Pooling for Human Action Recognition
We introduce Eigen Evolution Pooling, an efficient method to aggregate a sequence of feature vectors.
Evolution-Preserving Dense Trajectory Descriptors
Recently Trajectory-pooled Deep-learning Descriptors were shown to achieve state-of-the-art human action recognition results on a number of datasets.
X-ray Scattering Image Classification Using Deep Learning
In this paper, we explore the use of deep learning to develop methods for automatically analyzing x-ray scattering images.
Noisy Label Recovery for Shadow Detection in Unfamiliar Domains
However, shadow detection on broader image domains is still challenging due to the lack of annotated training data.
Region Ranking SVM for Image Classification
RRSVM exploits the correlation of local regions in an image, and it jointly learns a region evaluation function and a scheme for integrating multiple regions.
Latent Bi-constraint SVM for Video-based Object Recognition
LBSVM is based on Structured-Output SVM, but extends it to handle noisy video data and ensure consistency of the output decision throughout time.
Improving Human Action Recognition by Non-action Classification
In this paper we consider the task of recognizing human actions in realistic video where human actions are dominated by irrelevant factors.
Leave-One-Out Kernel Optimization for Shadow Detection
Optimizing the leave-one-out cross validation error is typically difficult, but it can be done efficiently in our framework.
Talking Heads: Detecting Humans and Recognizing Their Interactions
The objective of this work is to accurately and efficiently detect configurations of one or more people in edited TV material.
