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Found 21 papers, 7 papers with code
Learning Video-independent Eye Contact Segmentation from In-the-Wild Videos
In this work, we address the task of one-way eye contact detection for videos in the wild.
Adaptive Sparse ViT: Towards Learnable Adaptive Token Pruning by Fully Exploiting Self-Attention
The learnable thresholds are optimized in budget-aware training to balance accuracy and complexity, performing the corresponding pruning configurations for different input instances.
Multi-band oscillations emerge from a simple spiking network
These results suggest a simple geometric mechanism behind the emergence of multi-band oscillations without appealing to oscillatory inputs or multiple synaptic or neuronal timescales.
IGN : Implicit Generative Networks
In this work, we build recent advances in distributional reinforcement learning to give a state-of-art distributional variant of the model based on the IQN.
Deeply Supervised Skin Lesions Diagnosis with Stage and Branch Attention
Visual features of skin lesions vary significantly because the images are collected from patients with different lesion colours and morphologies by using dissimilar imaging equipment.
Region-level Contrastive and Consistency Learning for Semi-Supervised Semantic Segmentation
Specifically, we first propose a Region Mask Contrastive (RMC) loss and a Region Feature Contrastive (RFC) loss to accomplish region-level contrastive property.
CATrans: Context and Affinity Transformer for Few-Shot Segmentation
In this work, we effectively integrate the context and affinity information via the proposed novel Context and Affinity Transformer (CATrans) in a hierarchical architecture.
Feature Selective Transformer for Semantic Image Segmentation
In this work, we focus on fusing multi-scale features from Transformer-based backbones for semantic segmentation, and propose a Feature Selective Transformer (FeSeFormer), which aggregates features from all scales (or levels) for each query feature.
Dynamic Group Transformer: A General Vision Transformer Backbone with Dynamic Group Attention
To reduce the quadratic computation complexity caused by each query attending to all keys/values, various methods have constrained the range of attention within local regions, where each query only attends to keys/values within a hand-crafted window.
Pale Transformer: A General Vision Transformer Backbone with Pale-Shaped Attention
To reduce the quadratic computation complexity caused by the global self-attention, various methods constrain the range of attention within a local region to improve its efficiency.
Sparse to Dense Motion Transfer for Face Image Animation
Given a source face image and a sequence of sparse face landmarks, our goal is to generate a video of the face imitating the motion of landmarks.
Fully Transformer Networks for Semantic Image Segmentation
Transformers have shown impressive performance in various natural language processing and computer vision tasks, due to the capability of modeling long-range dependencies.
Model Reduction Captures Stochastic Gamma Oscillations on Low-Dimensional Manifolds
Here we propose a suite of Markovian model reduction methods with varying levels of complexity and applied it to spiking network models exhibiting heterogeneous dynamical regimes, ranging from homogeneous firing to strong synchrony in the gamma band.
Adam with Bandit Sampling for Deep Learning
In this paper, we propose a generalization of Adam, called Adambs, that allows us to also adapt to different training examples based on their importance in the model's convergence.
GINet: Graph Interaction Network for Scene Parsing
GI unit is further improved by the SC-loss to enhance the semantic representations over the exemplar-based semantic graph.
Learning to Recognize the Unseen Visual Predicates
Visual relationship recognition models are limited in the ability to generalize from finite seen predicates to unseen ones.
Consensus Feature Network for Scene Parsing
However, classification networks are dominated by the discriminative portion, so directly applying classification networks to scene parsing will result in inconsistent parsing predictions within one instance and among instances of the same category.
UGAN: Untraceable GAN for Multi-Domain Face Translation
To address this issue, we propose a method called Untraceable GAN, which has a novel source classifier to differentiate which domain an image is translated from, and determines whether the translated image still retains the characteristics of the source domain.
A Bandit Approach to Maximum Inner Product Search
There has been substantial research on sub-linear time approximate algorithms for Maximum Inner Product Search (MIPS).
Tree-structured Kronecker Convolutional Network for Semantic Segmentation
Therefore, it can capture partial information and enlarge the receptive field of filters simultaneously without introducing extra parameters.
CGNet: A Light-weight Context Guided Network for Semantic Segmentation
To tackle this problem, we propose a novel Context Guided Network (CGNet), which is a light-weight and efficient network for semantic segmentation.
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