Search Results for author:
Found 8 papers, 5 papers with code
Low-Resource Vision Challenges for Foundation Models
Low-resource settings are well-established in natural language processing, where many languages lack sufficient data for deep learning at scale.
Day2Dark: Pseudo-Supervised Activity Recognition beyond Silent Daylight
The main causes are the limited availability of labeled dark videos to learn from, as well as the distribution shift towards the lower color contrast at test-time.
TANet: Transformer-based Asymmetric Network for RGB-D Salient Object Detection
We employ the powerful feature extraction capability of Transformer (PVTv2) to extract global semantic information from RGB data and design a lightweight CNN backbone (LWDepthNet) to extract spatial structure information from depth data without pre-training.
Audio-Adaptive Activity Recognition Across Video Domains
This paper strives for activity recognition under domain shift, for example caused by change of scenery or camera viewpoint.
Repetitive Activity Counting by Sight and Sound
We also introduce a variant of this dataset for repetition counting under challenging vision conditions.
High-Performance Long-Term Tracking with Meta-Updater
Most top-ranked long-term trackers adopt the offline-trained Siamese architectures, thus, they cannot benefit from great progress of short-term trackers with online update.
Ranked #10 on
Visual Object Tracking
on LaSOT-ext
Learning regression and verification networks for long-term visual tracking
Compared with short-term tracking, the long-term tracking task requires determining the tracked object is present or absent, and then estimating the accurate bounding box if present or conducting image-wide re-detection if absent.
Structured Siamese Network for Real-Time Visual Tracking
In this paper, we circumvent this issue by proposing a local structure learning method, which simultaneously considers the local patterns of the target and their structural relationships for more accurate target tracking.
