Search Results for author:
Found 10 papers, 9 papers with code
Open-Vocabulary SAM: Segment and Recognize Twenty-thousand Classes Interactively
The CLIP and Segment Anything Model (SAM) are remarkable vision foundation models (VFMs).
EdgeSAM: Prompt-In-the-Loop Distillation for On-Device Deployment of SAM
It is also the first SAM variant that can run at over 30 FPS on an iPhone 14.
Interpret Vision Transformers as ConvNets with Dynamic Convolutions
There has been a debate about the superiority between vision Transformers and ConvNets, serving as the backbone of computer vision models.
Extract Free Dense Labels from CLIP
Contrastive Language-Image Pre-training (CLIP) has made a remarkable breakthrough in open-vocabulary zero-shot image recognition.
NOH-NMS: Improving Pedestrian Detection by Nearby Objects Hallucination
Greedy-NMS inherently raises a dilemma, where a lower NMS threshold will potentially lead to a lower recall rate and a higher threshold introduces more false positives.
Ranked #12 on
Object Detection
on CrowdHuman (full body)
YOLACT++: Better Real-time Instance Segmentation
Then we produce instance masks by linearly combining the prototypes with the mask coefficients.
Ranked #15 on
Real-time Instance Segmentation
on MSCOCO
(using extra training data)
YOLACT: Real-time Instance Segmentation
Then we produce instance masks by linearly combining the prototypes with the mask coefficients.
Ranked #21 on
Real-time Instance Segmentation
on MSCOCO
(using extra training data)
Anomaly Detection via Graphical Lasso
Accordingly, we propose a novel optimization problem that is similar in spirit to Robust Principal Component Analysis (RPCA) and splits the sample covariance matrix $M$ into two parts, $M=F+S$, where $F$ is the cleaned sample covariance whose inverse is sparse and computable by Graphical Lasso, and $S$ contains the outliers in $M$.
Generative Adversarial Active Learning for Unsupervised Outlier Detection
In this paper, we approach outlier detection as a binary-classification issue by sampling potential outliers from a uniform reference distribution.
Anomaly Detection with Robust Deep Autoencoders
Deep autoencoders, and other deep neural networks, have demonstrated their effectiveness in discovering non-linear features across many problem domains.
