Search Results for author:
Found 14 papers, 5 papers with code
Reinforcement Explanation Learning
Black-box methods to generate saliency maps are particularly interesting due to the fact that they do not utilize the internals of the model to explain the decision.
Contrast and Mix: Temporal Contrastive Video Domain Adaptation with Background Mixing
Unsupervised domain adaptation which aims to adapt models trained on a labeled source domain to a completely unlabeled target domain has attracted much attention in recent years.
Semi-Supervised Action Recognition with Temporal Contrastive Learning
We approach this problem by learning a two-pathway temporal contrastive model using unlabeled videos at two different speeds leveraging the fact that changing video speed does not change an action.
Select, Label, and Mix: Learning Discriminative Invariant Feature Representations for Partial Domain Adaptation
Partial domain adaptation which assumes that the unknown target label space is a subset of the source label space has attracted much attention in computer vision.
Ranked #1 on
Partial Domain Adaptation
on Office-31
Mitigating Dataset Imbalance via Joint Generation and Classification
In this work we address these questions from the perspective of dataset imbalance resulting out of severe under-representation of annotated training data for certain classes and its effect on both deep classification and generation methods.
Revisiting Few-shot Activity Detection with Class Similarity Control
In this paper, we present a conceptually simple and general yet novel framework for few-shot temporal activity detection based on proposal regression which detects the start and end time of the activities in untrimmed videos.
Two-Stream Region Convolutional 3D Network for Temporal Activity Detection
We address the problem of temporal activity detection in continuous, untrimmed video streams.
Ranked #4 on
Action Recognition
on THUMOS’14
RISE: Randomized Input Sampling for Explanation of Black-box Models
We compare our approach to state-of-the-art importance extraction methods using both an automatic deletion/insertion metric and a pointing metric based on human-annotated object segments.
Weakly Supervised Summarization of Web Videos
Casting the problem as a weakly supervised learning problem, we propose a flexible deep 3D CNN architecture to learn the notion of importance using only video-level annotation, and without any human-crafted training data.
R-C3D: Region Convolutional 3D Network for Temporal Activity Detection
We address the problem of activity detection in continuous, untrimmed video streams.
Ranked #1 on
Action Recognition In Videos
on THUMOS’14
Top-down Visual Saliency Guided by Captions
Neural image/video captioning models can generate accurate descriptions, but their internal process of mapping regions to words is a black box and therefore difficult to explain.
Video Summarization in a Multi-View Camera Network
While most existing video summarization approaches aim to extract an informative summary of a single video, we propose a novel framework for summarizing multi-view videos by exploiting both intra- and inter-view content correlations in a joint embedding space.
Temporal Model Adaptation for Person Re-Identification
Person re-identification is an open and challenging problem in computer vision.
Continuous Adaptation of Multi-Camera Person Identification Models through Sparse Non-redundant Representative Selection
We demonstrate the effectiveness of our approach on multi-camera person re-identification datasets, to demonstrate the feasibility of learning online classification models in multi-camera big data applications.
