Search Results for author:
Found 17 papers, 3 papers with code
Towards Holistic Scene Understanding: Feedback Enabled Cascaded Classification Models
In many machine learning domains (such as scene understanding), several related sub-tasks (such as scene categorization, depth estimation, object detection) operate on the same raw data and provide correlated outputs.
\theta-MRF: Capturing Spatial and Semantic Structure in the Parameters for Scene Understanding
For most scene understanding tasks (such as object detection or depth estimation), the classifiers need to consider contextual information in addition to the local features.
It's Not Polite to Point: Describing People with Uncertain Attributes
However, this is not a trivial task.
Revisiting Depth Layers from Occlusions
We cast the problem of depth-layer segmentation as a discrete labeling problem on a spatiotemporal Markov Random Field (MRF) that uses the motion occlusion cues along with monocular cues and a smooth motion prior for the moving object.
3D-Based Reasoning with Blocks, Support, and Stability
Our algorithm incorporates the intuition that a good 3D representation of the scene is the one that fits the data well, and is a stable, self-supporting (i. e., one that does not topple) arrangement of objects.
A Mixed Bag of Emotions: Model, Predict, and Transfer Emotion Distributions
First, we show through psychovisual studies that different people have different emotional reactions to the same image, which is a strong and novel departure from previous work that only records and predicts a single dominant emotion for each image.
Deep Neural Network for Real-Time Autonomous Indoor Navigation
Autonomous indoor navigation of Micro Aerial Vehicles (MAVs) possesses many challenges.
You Are Here: Mimicking the Human Thinking Process in Reading Floor-Plans
A human can easily find his or her way in an unfamiliar building, by walking around and reading the floor-plan.
Recent Advances in Convolutional Neural Networks
In the last few years, deep learning has led to very good performance on a variety of problems, such as visual recognition, speech recognition and natural language processing.
QUOTE: "Querying" Users as Oracles in Tag Engines - A Semi-Supervised Learning Approach to Personalized Image Tagging
Previous work has correctly identified that many of the tags that users provide on images are not visually relevant (i. e. representative of the salient content in the image) and they go on to treat such tags as noise, ignoring that the users chose to provide those tags over others that could have been more visually relevant.
In the Shadows, Shape Priors Shine: Using Occlusion to Improve Multi-Region Segmentation
We present a new algorithm for multi-region segmentation of 2D images with objects that may partially occlude each other.
An Empirical Study of Language CNN for Image Captioning
Language Models based on recurrent neural networks have dominated recent image caption generation tasks.
Stack-Captioning: Coarse-to-Fine Learning for Image Captioning
On the other hand, multi-stage image caption model is hard to train due to the vanishing gradient problem.
DSAC-C: Constrained Maximum Entropy for Robust Discrete Soft-Actor Critic
We present a novel extension to the family of Soft Actor-Critic (SAC) algorithms.
MaxEnt Loss: Constrained Maximum Entropy for Calibration under Out-of-Distribution Shift
We present a new loss function that addresses the out-of-distribution (OOD) calibration problem.
FER-C: Benchmarking Out-of-Distribution Soft Calibration for Facial Expression Recognition
We present a soft benchmark for calibrating facial expression recognition (FER).
FSL Model can Score Higher as It Is
It then transfers the style or shape of the test image to the train-class images for generation of more test-class samples, before performing classification based on a set of generated samples instead of just one sample.
