Search Results for author:
Found 12 papers, 5 papers with code
Prompt Decoupling for Text-to-Image Person Re-identification
In the first stage, we freeze the two encoders from CLIP and solely focus on optimizing the prompts to alleviate domain gap between the original training data of CLIP and downstream tasks.
A Clustering Algorithm to Organize Satellite Hotspot Data for the Purpose of Tracking Bushfires Remotely
This paper proposes a spatiotemporal clustering algorithm and its implementation in the R package spotoroo.
A Hyperspectral and RGB Dataset for Building Facade Segmentation
Hyperspectral Imaging (HSI) provides detailed spectral information and has been utilised in many real-world applications.
PointCaM: Cut-and-Mix for Open-Set Point Cloud Learning
Specifically, we use the Unknown-Point Simulator to simulate out-of-distribution data in the training stage by manipulating the geometric context of partial known data.
Generalised Co-Salient Object Detection
We propose a new setting that relaxes an assumption in the conventional Co-Salient Object Detection (CoSOD) setting by allowing the presence of "noisy images" which do not show the shared co-salient object.
Curved Geometric Networks for Visual Anomaly Recognition
Learning a latent embedding to understand the underlying nature of data distribution is often formulated in Euclidean spaces with zero curvature.
Towards Open-Set Object Detection and Discovery
This new task aims to extend the ability of open-set object detectors to further discover the categories of unknown objects based on their visual appearance without human effort.
GOSS: Towards Generalized Open-set Semantic Segmentation
In this paper, we present and study a new image segmentation task, called Generalized Open-set Semantic Segmentation (GOSS).
You Only Cut Once: Boosting Data Augmentation with a Single Cut
We present You Only Cut Once (YOCO) for performing data augmentations.
Blind Image Decomposition
We propose and study a novel task named Blind Image Decomposition (BID), which requires separating a superimposed image into constituent underlying images in a blind setting, that is, both the source components involved in mixing as well as the mixing mechanism are unknown.
Reinforced Attention for Few-Shot Learning and Beyond
Few-shot learning aims to correctly recognize query samples from unseen classes given a limited number of support samples, often by relying on global embeddings of images.
Deep Object Co-Segmentation
This work presents a deep object co-segmentation (DOCS) approach for segmenting common objects of the same class within a pair of images.
