Search Results for author:
Found 9 papers, 5 papers with code
Detecting Suicidality with a Contextual Graph Neural Network
Discovering individuals’ suicidality on social media has become increasingly important.
Leveraging Temporal Contextualization for Video Action Recognition
We propose Temporal Contextualization (TC), a novel layer-wise temporal information infusion mechanism for video that extracts core information from each frame, interconnects relevant information across the video to summarize into context tokens, and ultimately leverages the context tokens during the feature encoding process.
Interpretable Online Network Dictionary Learning for Inferring Long-Range Chromatin Interactions
First, we demonstrate that online cvxNDL retains the accuracy of classical DL methods while simultaneously ensuring unique interpretability and scalability.
Neural Collage Transfer: Artistic Reconstruction via Material Manipulation
Collage is a creative art form that uses diverse material scraps as a base unit to compose a single image.
Addressing Distribution Shift in RTB Markets via Exponential Tilting
Distribution shift in machine learning models can be a primary cause of performance degradation.
EXOT: Exit-aware Object Tracker for Safe Robotic Manipulation of Moving Object
We evaluate our method on the first-person video benchmark dataset, TREK-150, and on the custom dataset, RMOT-223, that we collect from the UR5e robot.
Towards Sequence-Level Training for Visual Tracking
Despite the extensive adoption of machine learning on the task of visual object tracking, recent learning-based approaches have largely overlooked the fact that visual tracking is a sequence-level task in its nature; they rely heavily on frame-level training, which inevitably induces inconsistency between training and testing in terms of both data distributions and task objectives.
Ranked #16 on
Visual Object Tracking
on TrackingNet
From Scratch to Sketch: Deep Decoupled Hierarchical Reinforcement Learning for Robotic Sketching Agent
We present an automated learning framework for a robotic sketching agent that is capable of learning stroke-based rendering and motor control simultaneously.
Hierarchical Reinforcement Learning
reinforcement-learning
+1
Online Hybrid Lightweight Representations Learning: Its Application to Visual Tracking
This paper presents a novel hybrid representation learning framework for streaming data, where an image frame in a video is modeled by an ensemble of two distinct deep neural networks; one is a low-bit quantized network and the other is a lightweight full-precision network.
