Search Results for author:
Found 15 papers, 4 papers with code
Few-Shot 3D Volumetric Segmentation with Multi-Surrogate Fusion
Conventional 3D medical image segmentation methods typically require learning heavy 3D networks (e. g., 3D-UNet), as well as large amounts of in-domain data with accurate pixel/voxel-level labels to avoid overfitting.
Large Language Models for Automatic Milestone Detection in Group Discussions
Large language models like GPT have proven widely successful on natural language understanding tasks based on written text documents.
Self-supervised Learning with Local Contrastive Loss for Detection and Semantic Segmentation
We evaluate our SSL approach on two downstream tasks -- object detection and semantic segmentation, using COCO, PASCAL VOC, and CityScapes datasets.
Dynamic Distillation Network for Cross-Domain Few-Shot Recognition with Unlabeled Data
As the base dataset and unlabeled dataset are from different domains, projecting the target images in the class-domain of the base dataset with a fixed pretrained model might be sub-optimal.
Towards Visually Explaining Similarity Models
We show that the resulting similarity models perform, and can be visually explained, better than the corresponding baseline models trained without these constraints.
Weakly Supervised Temporal Action Localization Using Deep Metric Learning
We propose a classification module to generate action labels for each segment in the video, and a deep metric learning module to learn the similarity between different action instances.
Ranked #1 on
Temporal Action Localization
on ActivityNet-1.2
Multi-person Spatial Interaction in a Large Immersive Display Using Smartphones as Touchpads
In this paper, we present a multi-user interaction interface for a large immersive space that supports simultaneous screen interactions by combining (1) user input via personal smartphones and Bluetooth microphones, (2) spatial tracking via an overhead array of Kinect sensors, and (3) WebSocket interfaces to a webpage running on the large screen.
Towards Visually Explaining Variational Autoencoders
We present methods to generate visual attention from the learned latent space, and also demonstrate such attention explanations serve more than just explaining VAE predictions.
Visual Similarity Attention
While there has been substantial progress in learning suitable distance metrics, these techniques in general lack transparency and decision reasoning, i. e., explaining why the input set of images is similar or dissimilar.
Keep Meeting Summaries on Topic: Abstractive Multi-Modal Meeting Summarization
Transcripts of natural, multi-person meetings differ significantly from documents like news articles, which can make Natural Language Generation models for generating summaries unfocused.
Re-Identification with Consistent Attentive Siamese Networks
We propose a new deep architecture for person re-identification (re-id).
Measuring the Temporal Behavior of Real-World Person Re-Identification
Designing real-world person re-identification (re-id) systems requires attention to operational aspects not typically considered in academic research.
Rank Persistence: Assessing the Temporal Performance of Real-World Person Re-Identification
Designing useful person re-identification systems for real-world applications requires attention to operational aspects not typically considered in academic research.
A Systematic Evaluation and Benchmark for Person Re-Identification: Features, Metrics, and Datasets
To ensure a fair comparison, all of the approaches were implemented using a unified code library that includes 11 feature extraction algorithms and 22 metric learning and ranking techniques.
Person Re-Identification With Discriminatively Trained Viewpoint Invariant Dictionaries
This paper introduces a new approach to address the person re-identification problem in cameras with non-overlapping fields of view.
