Search Results for author:
Found 9 papers, 3 papers with code
TAM-VT: Transformation-Aware Multi-scale Video Transformer for Segmentation and Tracking
In this work we propose a novel, clip-based DETR-style encoder-decoder architecture, which focuses on systematically analyzing and addressing aforementioned challenges.
MINOTAUR: Multi-task Video Grounding From Multimodal Queries
Video understanding tasks take many forms, from action detection to visual query localization and spatio-temporal grounding of sentences.
Relational Space-Time Query in Long-Form Videos
In this paper, we propose to study these problems in a joint framework for long video understanding.
Beyond Simple Meta-Learning: Multi-Purpose Models for Multi-Domain, Active and Continual Few-Shot Learning
The first method, Simple CNAPS, employs a hierarchically regularized Mahalanobis-distance based classifier combined with a state of the art neural adaptive feature extractor to achieve strong performance on Meta-Dataset, mini-ImageNet and tiered-ImageNet benchmarks.
UniT: Unified Knowledge Transfer for Any-shot Object Detection and Segmentation
Weakly-supervised approaches draw on image-level labels to build detectors/segmentors, while zero/few-shot methods assume abundant instance-level data for a set of base classes, and none to a few examples for novel classes.
Ensemble based discriminative models for Visual Dialog Challenge 2018
This manuscript describes our approach for the Visual Dialog Challenge 2018.
Improved Few-Shot Visual Classification
Few-shot learning is a fundamental task in computer vision that carries the promise of alleviating the need for exhaustively labeled data.
Ranked #2 on
Few-Shot Image Classification
on Mini-Imagenet 10-way (5-shot)
(using extra training data)
The "something something" video database for learning and evaluating visual common sense
Neural networks trained on datasets such as ImageNet have led to major advances in visual object classification.
Ranked #115 on
Action Recognition
on Something-Something V2
Natural Language Generation through Character-based RNNs with Finite-state Prior Knowledge
Recently Wen et al. (2015) have proposed a Recurrent Neural Network (RNN) approach to the generation of utterances from dialog acts, and shown that although their model requires less effort to develop than a rule-based system, it is able to improve certain aspects of the utterances, in particular their naturalness.
