activity
20162023
most citedScene Labeling using Gated Recurrent Units with Explicit Long Range Conditioning

4 citations · 5 across the 9 of their papers we have counts for

collaborators
Showing cs.CVShow all

9 papers · 1 filter

cs.CV20231 cited

PrObeD: Proactive Object Detection Wrapper

Vishal Asnani, Abhinav Kumar, Suya You +1

Previous research in object detection focuses on various tasks, including detecting objects in generic and camouflaged images. These works are regarded as passive works for ob…

cs.CV2023

SemST: Semantically Consistent Multi-Scale Image Translation via Structure-Texture Alignment

Ganning Zhao, Wenhui Cui, Suya You +1

Unsupervised image-to-image (I2I) translation learns cross-domain image mapping that transfers input from the source domain to output in the target domain while preserving its sema…

cs.CV2023

Unsupervised Green Object Tracker (GOT) without Offline Pre-training

Zhiruo Zhou, Suya You, C. -C. Jay Kuo

Supervised trackers trained on labeled data dominate the single object tracking field for superior tracking accuracy. The labeling cost and the huge computational complexity hinder…

cs.CV2023

SUMMIT: Source-Free Adaptation of Uni-Modal Models to Multi-Modal Targets

Cody Simons, Dripta S. Raychaudhuri, Sk Miraj Ahmed +3

Scene understanding using multi-modal data is necessary in many applications, e.g., autonomous navigation. To achieve this in a variety of situations, existing models must be able…

cs.CV2023

A Study on Improving Realism of Synthetic Data for Machine Learning

Tingwei Shen, Ganning Zhao, Suya You

Synthetic-to-real data translation using generative adversarial learning has achieved significant success in improving synthetic data. Yet, limited studies focus on deep evaluation…

cs.CV2023

Unsupervised Synthetic Image Refinement via Contrastive Learning and Consistent Semantic-Structural Constraints

Ganning Zhao, Tingwei Shen, Suya You +1

Ensuring the realism of computer-generated synthetic images is crucial to deep neural network (DNN) training. Due to different semantic distributions between synthetic and real-wor…