Search Results for author:
Found 16 papers, 10 papers with code
Gaze-guided Hand-Object Interaction Synthesis: Benchmark and Method
Here, the object motion diffusion model generates sequences of object motions based on gaze conditions, while the hand motion diffusion model produces hand motions based on the generated object motion.
Guidance with Spherical Gaussian Constraint for Conditional Diffusion
We theoretically show the existence of manifold deviation by establishing a certain lower bound for the estimation error of the loss guidance.
Attention-based Interactive Disentangling Network for Instance-level Emotional Voice Conversion
We introduce a two-stage pipeline to effectively train our network: Stage I utilizes inter-speech contrastive learning to model fine-grained emotion and intra-speech disentanglement learning to better separate emotion and content.
APRF: Anti-Aliasing Projection Representation Field for Inverse Problem in Imaging
However, these methods have not considered the correlation between adjacent projection views, resulting in aliasing artifacts on SV sinograms.
Hard Nominal Example-aware Template Mutual Matching for Industrial Anomaly Detection
Additionally, to meet the speed-accuracy demands, we further propose \textbf{P}ixel-level \textbf{T}emplate \textbf{S}election (PTS) to streamline the original template set.
Ranked #12 on
Anomaly Detection
on MVTec LOCO AD
CuNeRF: Cube-Based Neural Radiance Field for Zero-Shot Medical Image Arbitrary-Scale Super Resolution
Medical image arbitrary-scale super-resolution (MIASSR) has recently gained widespread attention, aiming to super sample medical volumes at arbitrary scales via a single model.
Parameter-Free Channel Attention for Image Classification and Super-Resolution
The channel attention mechanism is a useful technique widely employed in deep convolutional neural networks to boost the performance for image processing tasks, eg, image classification and image super-resolution.
AcroFOD: An Adaptive Method for Cross-domain Few-shot Object Detection
Under the domain shift, cross-domain few-shot object detection aims to adapt object detectors in the target domain with a few annotated target data.
Re4: Learning to Re-contrast, Re-attend, Re-construct for Multi-interest Recommendation
Specifically, Re4 encapsulates three backward flows, i. e., 1) Re-contrast, which drives each interest embedding to be distinct from other interests using contrastive learning; 2) Re-attend, which ensures the interest-item correlation estimation in the forward flow to be consistent with the criterion used in final recommendation; and 3) Re-construct, which ensures that each interest embedding can semantically reflect the information of representative items that relate to the corresponding interest.
SimAM: A Simple, Parameter-Free Attention Module for Convolutional Neural Networks
Another advantage of the module is that most of the operators are selected based on the solution to the defined energy function, avoiding too many efforts for structure tuning.
Texture-guided Saliency Distilling for Unsupervised Salient Object Detection
In this paper, we propose a novel USOD method to mine rich and accurate saliency knowledge from both easy and hard samples.
SNN2ANN: A Fast and Memory-Efficient Training Framework for Spiking Neural Networks
The SNN2ANN consists of 2 components: a) a weight sharing architecture between ANN and SNN and b) spiking mapping units.
Exploring Dual-task Correlation for Pose Guided Person Image Generation
Pose Guided Person Image Generation (PGPIG) is the task of transforming a person image from the source pose to a given target pose.
Self-supervised Image-specific Prototype Exploration for Weakly Supervised Semantic Segmentation
Weakly Supervised Semantic Segmentation (WSSS) based on image-level labels has attracted much attention due to low annotation costs.
Ranked #20 on
Weakly-Supervised Semantic Segmentation
on COCO 2014 val
Weakly supervised Semantic Segmentation
Weakly-Supervised Semantic Segmentation
Benchmarking Deep Models for Salient Object Detection
In recent years, deep network-based methods have continuously refreshed state-of-the-art performance on Salient Object Detection (SOD) task.
Activation to Saliency: Forming High-Quality Labels for Completely Unsupervised Salient Object Detection
Moreover, a new loss function is proposed to facilitate the generation of high-quality pseudo labels.
