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Found 24 papers, 8 papers with code
Continuous Spiking Graph Neural Networks
Inspired by recent spiking neural networks (SNNs), which emulate a biological inference process and provide an energy-efficient neural architecture, we incorporate the SNNs with CGNNs in a unified framework, named Continuous Spiking Graph Neural Networks (COS-GNN).
Effectiveness Assessment of Recent Large Vision-Language Models
The advent of large vision-language models (LVLMs) represents a noteworthy advancement towards the pursuit of artificial general intelligence.
MAST: Video Polyp Segmentation with a Mixture-Attention Siamese Transformer
To the best of our knowledge, our MAST is the first transformer model dedicated to video polyp segmentation.
Dynamic Spiking Graph Neural Networks
Current work often uses SNNs instead of Recurrent Neural Networks (RNNs) by using binary features instead of continuous ones for efficient training, which would overlooks graph structure information and leads to the loss of details during propagation.
A Unified Query-based Paradigm for Camouflaged Instance Segmentation
Due to the high similarity between camouflaged instances and the background, the recently proposed camouflaged instance segmentation (CIS) faces challenges in accurate localization and instance segmentation.
Rethinking Polyp Segmentation from an Out-of-Distribution Perspective
Unlike existing fully-supervised approaches, we rethink colorectal polyp segmentation from an out-of-distribution perspective with a simple but effective self-supervised learning approach.
Deep ReLU Networks Have Surprisingly Simple Polytopes
A ReLU network is a piecewise linear function over polytopes.
Rethink Depth Separation with Intra-layer Links
Then, we modify the depth separation theory by showing that a shallow network with intra-layer links does not need to go as wide as before to express some hard functions constructed by a deep network.
Feature Shrinkage Pyramid for Camouflaged Object Detection with Transformers
Vision transformers have recently shown strong global context modeling capabilities in camouflaged object detection.
Memory-aided Contrastive Consensus Learning for Co-salient Object Detection
To learn better group consensus, we propose the Group Consensus Aggregation Module (GCAM) to abstract the common features of each image group; meanwhile, to make the consensus representation more discriminative, we introduce the Memory-based Contrastive Module (MCM), which saves and updates the consensus of images from different groups in a queue of memories.
Energy Efficient Training of SNN using Local Zeroth Order Method
To circumvent the problem surrogate method uses a differentiable approximation of the Heaviside in the backward pass, while the forward pass uses the Heaviside as the spiking function.
Zeroth-Order Negative Curvature Finding: Escaping Saddle Points without Gradients
We consider escaping saddle points of nonconvex problems where only the function evaluations can be accessed.
Balanced Self-Paced Learning for AUC Maximization
Self-paced learning is an effective method for handling noisy data.
On the Intrinsic Structures of Spiking Neural Networks
Initially, we unveil two pivotal components of intrinsic structures: the integration operation and firing-reset mechanism, by elucidating their influence on the expressivity of SNNs.
Lower and Upper Bounds for Numbers of Linear Regions of Graph Convolutional Networks
In particular, we obtain an optimal upper bound for the maximum number of linear regions for one-layer GCNs, and the upper and lower bounds for multi-layer GCNs.
Dynamic Prototype Convolution Network for Few-Shot Semantic Segmentation
Specifically, in DPCN, a dynamic convolution module (DCM) is firstly proposed to generate dynamic kernels from support foreground, then information interaction is achieved by convolution operations over query features using these kernels.
Ranked #32 on
Few-Shot Semantic Segmentation
on PASCAL-5i (1-Shot)
Implicit Motion Handling for Video Camouflaged Object Detection
We propose a new video camouflaged object detection (VCOD) framework that can exploit both short-term dynamics and long-term temporal consistency to detect camouflaged objects from video frames.
Ranked #2 on
Camouflaged Object Segmentation
on Camouflaged Animal Dataset
(using extra training data)
IDENTIFYING CONCEALED OBJECTS FROM VIDEOS
The proposed SLT-Net leverages on both short-term dynamics and long-term temporal consistency to detect concealed objects in continuous video frames.
Perturbation Diversity Certificates Robust Generalisation
It is possibly due to the fact that the conventional adversarial training methods generate adversarial perturbations usually in a supervised way, so that the adversarial samples are highly biased towards the decision boundary, resulting in an inhomogeneous data distribution.
Scale-Aware Graph Neural Network for Few-Shot Semantic Segmentation
However, they fail to fully leverage the high-order appearance relationships between multi-scale features among the support-query image pairs, thus leading to an inaccurate localization of the query objects.
Few-Shot Semantic Segmentation With Cyclic Memory Network
Specifically, we first generate N pairs (key and value) of multi-resolution query features guided by the support feature and its mask.
Learning to Learn Variational Semantic Memory
The variational semantic memory accrues and stores semantic information for the probabilistic inference of class prototypes in a hierarchical Bayesian framework.
Learning to Learn with Variational Information Bottleneck for Domain Generalization
Domain generalization models learn to generalize to previously unseen domains, but suffer from prediction uncertainty and domain shift.
Fast Large-Scale Discrete Optimization Based on Principal Coordinate Descent
Binary optimization, a representative subclass of discrete optimization, plays an important role in mathematical optimization and has various applications in computer vision and machine learning.
