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Found 19 papers, 4 papers with code
Prior-agnostic Multi-scale Contrastive Text-Audio Pre-training for Parallelized TTS Frontend Modeling
Furthermore, a parallelized TTS frontend model is delicately devised to execute TN, PD, and PBP prediction tasks, respectively in the second stage.
Enhancing Zero-shot Counting via Language-guided Exemplar Learning
Recently, Class-Agnostic Counting (CAC) problem has garnered increasing attention owing to its intriguing generality and superior efficiency compared to Category-Specific Counting (CSC).
GazeCLIP: Towards Enhancing Gaze Estimation via Text Guidance
Over the past decade, visual gaze estimation has garnered growing attention within the research community, thanks to its wide-ranging application scenarios.
Fine-grained Text and Image Guided Point Cloud Completion with CLIP Model
In this work, we propose a novel multimodal fusion network for point cloud completion, which can simultaneously fuse visual and textual information to predict the semantic and geometric characteristics of incomplete shapes effectively.
CPNet: Exploiting CLIP-based Attention Condenser and Probability Map Guidance for High-fidelity Talking Face Generation
Recently, talking face generation has drawn ever-increasing attention from the research community in computer vision due to its arduous challenges and widespread application scenarios, e. g. movie animation and virtual anchor.
FER-former: Multi-modal Transformer for Facial Expression Recognition
The ever-increasing demands for intuitive interactions in Virtual Reality has triggered a boom in the realm of Facial Expression Recognition (FER).
Facial Expression Recognition
Facial Expression Recognition (FER)
GCNet: Probing Self-Similarity Learning for Generalized Counting Network
The class-agnostic counting (CAC) problem has caught increasing attention recently due to its wide societal applications and arduous challenges.
FedCL: Federated Multi-Phase Curriculum Learning to Synchronously Correlate User Heterogeneity
However, a significant challenge in FL is handling the heterogeneity of local data distribution, which often results in a drifted global model that is difficult to converge.
Look Closer to Your Enemy: Learning to Attack via Teacher-Student Mimicking
Deep neural networks have significantly advanced person re-identification (ReID) applications in the realm of the industrial internet, yet they remain vulnerable.
CrowdMLP: Weakly-Supervised Crowd Counting via Multi-Granularity MLP
Existing state-of-the-art crowd counting algorithms rely excessively on location-level annotations, which are burdensome to acquire.
Towards Realistic Visual Dubbing with Heterogeneous Sources
The task of few-shot visual dubbing focuses on synchronizing the lip movements with arbitrary speech input for any talking head video.
Towards Using Clothes Style Transfer for Scenario-aware Person Video Generation
To tackle this problem, most recent AdaIN-based architectures are proposed to extract clothes and scenario features for generation.
Local Aggressive Adversarial Attacks on 3D Point Cloud
However, the perturbations of global point are not effective for misleading the victim model.
Improvement of Normal Estimation for PointClouds via Simplifying Surface Fitting
Firstly, a dynamic top-k selection strategy is introduced to better focus on the most critical points of a given patch, and the points selected by our learning method tend to fit a surface by way of a simple tangent plane, which can dramatically improve the normal estimation results of patches with sharp corners or complex patterns.
Fast and Accurate Normal Estimation for Point Cloud via Patch Stitching
At the stitching stage, we use the learned weights of multi-branch planar experts and distance weights between points to select the best normal from the overlapping parts.
STNet: Scale Tree Network with Multi-level Auxiliator for Crowd Counting
To battle the ingrained issue of accuracy degradation, we propose a novel and powerful network called Scale Tree Network (STNet) for accurate crowd counting.
Interlayer and Intralayer Scale Aggregation for Scale-invariant Crowd Counting
Crowd counting is an important vision task, which faces challenges on continuous scale variation within a given scene and huge density shift both within and across images.
Semi-dense Stereo Matching using Dual CNNs
A robust solution for semi-dense stereo matching is presented.
Multi-scale Convolution Aggregation and Stochastic Feature Reuse for DenseNets
To address this problem, a regularization method named Stochastic Feature Reuse is also presented.
