Search Results for author:
Found 19 papers, 3 papers with code
MaskDiff: Modeling Mask Distribution with Diffusion Probabilistic Model for Few-Shot Instance Segmentation
Few-shot instance segmentation extends the few-shot learning paradigm to the instance segmentation task, which tries to segment instance objects from a query image with a few annotated examples of novel categories.
Multi-Modal Mutual Information Maximization: A Novel Approach for Unsupervised Deep Cross-Modal Hashing
First, to learn informative representations that can preserve both intra- and inter-modal similarities, we leverage the recent advances in estimating variational lower-bound of MI to maximize the MI between the binary representations and input features and between binary representations of different modalities.
GUNNEL: Guided Mixup Augmentation and Multi-View Fusion for Aquatic Animal Segmentation
Recent years have witnessed great advances in object segmentation research.
Contextual Guided Segmentation Framework for Semi-supervised Video Instance Segmentation
For human instance, we develop skeleton-guided segmentation in a frame along with object flow to correct and refine the result across frames.
Anabranch Network for Camouflaged Object Segmentation
Different from existing networks for segmentation, our proposed network possesses the second branch for classification to predict the probability of containing camouflaged object(s) in an image, which is then fused into the main branch for segmentation to boost up the segmentation accuracy.
Ranked #9 on
Camouflaged Object Segmentation
on CAMO
R2U3D: Recurrent Residual 3D U-Net for Lung Segmentation
In particular, the proposed model integrates 3D convolution into the Recurrent Residual Neural Network based on U-Net.
Camouflaged Instance Segmentation In-The-Wild: Dataset, Method, and Benchmark Suite
We also provide a benchmark suite for the task of camouflaged instance segmentation.
Direct Quantization for Training Highly Accurate Low Bit-width Deep Neural Networks
With this approach, we can learn activation quantizers that minimize the quantization errors in the majority of channels.
Unsupervised Deep Cross-modality Spectral Hashing
This paper presents a novel framework, namely Deep Cross-modality Spectral Hashing (DCSH), to tackle the unsupervised learning problem of binary hash codes for efficient cross-modal retrieval.
MirrorNet: Bio-Inspired Camouflaged Object Segmentation
Differently from existing networks for segmentation, our proposed network possesses two segmentation streams: the main stream and the mirror stream corresponding with the original image and its flipped image, respectively.
Ranked #3 on
Camouflaged Object Segmentation
on CAMO
A Convolutional Transformation Network for Malware Classification
In this paper, we introduce a novel approach to classify malware by using a deep network on images transformed from binary samples.
Simultaneous Feature Aggregating and Hashing for Compact Binary Code Learning
This global vector is then subjected to a hashing function to generate a binary hash code.
From Selective Deep Convolutional Features to Compact Binary Representations for Image Retrieval
In the large-scale image retrieval task, the two most important requirements are the discriminability of image representations and the efficiency in computation and storage of representations.
Novel Evaluation Metrics for Seam Carving based Image Retargeting
Image retargeting effectively resizes images by preserving the recognizability of important image regions.
Smart Mirror: Intelligent Makeup Recommendation and Synthesis
The female facial image beautification usually requires professional editing softwares, which are relatively difficult for common users.
Salient Object Detection with Semantic Priors
Salient object detection has increasingly become a popular topic in cognitive and computational sciences, including computer vision and artificial intelligence research.
Sense Beyond Expressions: Cuteness
With the development of Internet culture, cuteness has become a popular concept.
Salient Object Detection via Augmented Hypotheses
First, our method generates the objectness map via objectness hypotheses.
Adaptive Nonparametric Image Parsing
Instead of fixing $k$ as in traditional non-parametric approaches, here we propose a novel adaptive nonparametric approach which determines the sample-specific k for each test image.
