Search Results for author:
Found 8 papers, 1 papers with code
Early Action Recognition with Action Prototypes
For example, on early recognition observing only the first 10% of each video, our method improves the SOTA by +2. 23 Top-1 accuracy on Something-Something-v2, +3. 55 on UCF-101, +3. 68 on SSsub21, and +5. 03 on EPIC-Kitchens-55, where prior work used either multi-modal inputs (e. g. optical-flow) or batched inference.
SkeleTR: Towrads Skeleton-based Action Recognition in the Wild
It first models the intra-person skeleton dynamics for each skeleton sequence with graph convolutions, and then uses stacked Transformer encoders to capture person interactions that are important for action recognition in general scenarios.
SkeleTR: Towards Skeleton-based Action Recognition in the Wild
It first models the intra-person skeleton dynamics for each skeleton sequence with graph convolutions, and then uses stacked Transformer encoders to capture person interactions that are important for action recognition in the wild.
Ranked #2 on
Human Interaction Recognition
on NTU RGB+D
Large Scale Real-World Multi-Person Tracking
This paper presents a new large scale multi-person tracking dataset -- \texttt{PersonPath22}, which is over an order of magnitude larger than currently available high quality multi-object tracking datasets such as MOT17, HiEve, and MOT20 datasets.
Ranked #1 on
Multi-Object Tracking
on PersonPath22
Self-taught Object Localization with Deep Networks
This paper introduces self-taught object localization, a novel approach that leverages deep convolutional networks trained for whole-image recognition to localize objects in images without additional human supervision, i. e., without using any ground-truth bounding boxes for training.
Leveraging Structure from Motion to Learn Discriminative Codebooks for Scalable Landmark Classification
In this paper we propose a new technique for learning a discriminative codebook for local feature descriptors, specifically designed for scalable landmark classification.
PiCoDes: Learning a Compact Code for Novel-Category Recognition
In contrast to previous approaches to learn compact codes, we optimize explicitly for (an upper bound on) classification performance.
Exploiting weakly-labeled Web images to improve object classification: a domain adaptation approach
In this paper we investigate and compare methods that learn image classifiers by combining very few manually annotated examples (e. g., 1-10 images per class) and a large number of weakly-labeled Web photos retrieved using keyword-based image search.
