Search Results for author:
Found 35 papers, 16 papers with code
Instance Tracking in 3D Scenes from Egocentric Videos
We explore this problem by first introducing a new benchmark dataset, consisting of RGB and depth videos, per-frame camera pose, and instance-level annotations in both 2D camera and 3D world coordinates.
Guided Recommendation for Model Fine-Tuning
With thousands of historical training jobs, a recommendation system can be learned to predict the model selection score given the features of the dataset and the model as input.
The Best of Both Worlds: Combining Model-based and Nonparametric Approaches for 3D Human Body Estimation
Our framework leverages the best of non-parametric and model-based methods and is also robust to partial occlusion.
Ranked #1 on
3D Absolute Human Pose Estimation
on Human3.6M
(PA-MPJPE metric)
Task Adaptive Parameter Sharing for Multi-Task Learning
TAPS solves a joint optimization problem which determines which layers to share with the base model and the value of the task-specific weights.
GeoFill: Reference-Based Image Inpainting with Better Geometric Understanding
Our approach achieves state-of-the-art performance on both RealEstate10K and MannequinChallenge dataset with large baselines, complex geometry and extreme camera motions.
Representation Consolidation from Multiple Expert Teachers
Indeed, we observe experimentally that standard distillation of task-specific teachers, or using these teacher representations directly, **reduces** downstream transferability compared to a task-agnostic generalist model.
Representation Consolidation for Training Expert Students
Traditionally, distillation has been used to train a student model to emulate the input/output functionality of a teacher.
SSCAP: Self-supervised Co-occurrence Action Parsing for Unsupervised Temporal Action Segmentation
However, it is quite expensive to annotate every frame in a large corpus of videos to construct a comprehensive supervised training dataset.
A linearized framework and a new benchmark for model selection for fine-tuning
Since all model selection algorithms in the literature have been tested on different use-cases and never compared directly, we introduce a new comprehensive benchmark for model selection comprising of: i) A model zoo of single and multi-domain models, and ii) Many target tasks.
Exponential Moving Average Normalization for Self-supervised and Semi-supervised Learning
We present a plug-in replacement for batch normalization (BN) called exponential moving average normalization (EMAN), which improves the performance of existing student-teacher based self- and semi-supervised learning techniques.
Self-Supervised Learning
Semi-Supervised Image Classification
Camera Pose Matters: Improving Depth Prediction by Mitigating Pose Distribution Bias
We show that jointly applying the two methods improves depth prediction on images captured under uncommon and even never-before-seen camera poses.
Weak Supervision and Referring Attention for Temporal-Textual Association Learning
The referring attention is our designed mechanism acting as a scoring function for grounding the given queries over frames temporally.
Celeganser: Automated Analysis of Nematode Morphology and Age
The nematode Caenorhabditis elegans (C. elegans) serves as an important model organism in a wide variety of biological studies.
Domain Decluttering: Simplifying Images to Mitigate Synthetic-Real Domain Shift and Improve Depth Estimation
In this setting, we find that existing domain translation approaches are difficult to train and offer little advantage over simple baselines that use a mix of real and synthetic data.
Geometric Pose Affordance: 3D Human Pose with Scene Constraints
Full 3D estimation of human pose from a single image remains a challenging task despite many recent advances.
Modularized Textual Grounding for Counterfactual Resilience
Computer Vision applications often require a textual grounding module with precision, interpretability, and resilience to counterfactual inputs/queries.
Multigrid Predictive Filter Flow for Unsupervised Learning on Videos
We introduce multigrid Predictive Filter Flow (mgPFF), a framework for unsupervised learning on videos.
Sparse Representations for Object and Ego-motion Estimation in Dynamic Scenes
Dynamic scenes that contain both object motion and egomotion are a challenge for monocular visual odometry (VO).
Task2Vec: Task Embedding for Meta-Learning
We demonstrate that this embedding is capable of predicting task similarities that match our intuition about semantic and taxonomic relations between different visual tasks (e. g., tasks based on classifying different types of plants are similar) We also demonstrate the practical value of this framework for the meta-task of selecting a pre-trained feature extractor for a new task.
Image Reconstruction with Predictive Filter Flow
We propose a simple, interpretable framework for solving a wide range of image reconstruction problems such as denoising and deconvolution.
Ranked #18 on
Image Super-Resolution
on Set14 - 4x upscaling
Active Testing: An Efficient and Robust Framework for Estimating Accuracy
Much recent work on visual recognition aims to scale up learning to massive, noisily-annotated datasets.
Resisting Large Data Variations via Introspective Transformation Network
Experimental results verify that our approach significantly improves the ability of deep networks to resist large variations between training and testing data and achieves classification accuracy improvements on several benchmark datasets, including MNIST, affNIST, SVHN, CIFAR-10 and miniImageNet.
Pixel-wise Attentional Gating for Parsimonious Pixel Labeling
To achieve parsimonious inference in per-pixel labeling tasks with a limited computational budget, we propose a \emph{Pixel-wise Attentional Gating} unit (\emph{PAG}) that learns to selectively process a subset of spatial locations at each layer of a deep convolutional network.
Ranked #7 on
Semantic Segmentation
on KITTI Semantic Segmentation
Fine-Grained Facial Expression Analysis Using Dimensional Emotion Model
Specifically, we first mapped facial expressions into dimensional measures so that we transformed facial expression analysis from a classification problem to a regression one.
Structured Triplet Learning with POS-tag Guided Attention for Visual Question Answering
Visual question answering (VQA) is of significant interest due to its potential to be a strong test of image understanding systems and to probe the connection between language and vision.
Recurrent Pixel Embedding for Instance Grouping
We introduce a differentiable, end-to-end trainable framework for solving pixel-level grouping problems such as instance segmentation consisting of two novel components.
Recurrent Scene Parsing with Perspective Understanding in the Loop
We propose a depth-aware gating module that adaptively selects the pooling field size in a convolutional network architecture according to the object scale (inversely proportional to the depth) so that small details are preserved for distant objects while larger receptive fields are used for those nearby.
Ranked #32 on
Semantic Segmentation
on SUN-RGBD
(using extra training data)
Low-rank Bilinear Pooling for Fine-Grained Classification
To address the computational demands of high feature dimensionality, we propose to represent the covariance features as a matrix and apply a low-rank bilinear classifier.
Photo Aesthetics Ranking Network with Attributes and Content Adaptation
In this work, we propose to learn a deep convolutional neural network to rank photo aesthetics in which the relative ranking of photo aesthetics are directly modeled in the loss function.
Ranked #7 on
Aesthetics Quality Assessment
on AVA
On Image segmentation using Fractional Gradients-Learning Model Parameters using Approximate Marginal Inference
Estimates of image gradients play a ubiquitous role in image segmentation and classification problems since gradients directly relate to the boundaries or the edges of a scene.
Spatially Aware Dictionary Learning and Coding for Fossil Pollen Identification
We propose a robust approach for performing automatic species-level recognition of fossil pollen grains in microscopy images that exploits both global shape and local texture characteristics in a patch-based matching methodology.
The Open World of Micro-Videos
Micro-videos are six-second videos popular on social media networks with several unique properties.
Tracking Objects with Higher Order Interactions using Delayed Column Generation
We study the problem of multi-target tracking and data association in video.
Planar Ultrametrics for Image Segmentation
We study the problem of hierarchical clustering on planar graphs.
Do We Need More Training Data?
Datasets for training object recognition systems are steadily increasing in size.
