Search Results for author:
Found 9 papers, 7 papers with code
Combating Data Imbalances in Federated Semi-supervised Learning with Dual Regulators
Theoretically, we show the convergence guarantee of the dual regulators.
Scale-Prior Deformable Convolution for Exemplar-Guided Class-Agnostic Counting
Here we propose a scale-sensitive generalized loss to tackle this problem.
Ranked #5 on
Object Counting
on FSC147
Probing Visual-Audio Representation for Video Highlight Detection via Hard-Pairs Guided Contrastive Learning
Our proposed method mainly leverages the intra-modality encoding and cross-modality co-occurrence encoding for fully representation modeling.
Pyramid Region-based Slot Attention Network for Temporal Action Proposal Generation
Based on PRSlot modules, we present a novel Pyramid Region-based Slot Attention Network termed PRSA-Net to learn a unified visual representation with rich temporal and semantic context for better proposal generation.
Better Teacher Better Student: Dynamic Prior Knowledge for Knowledge Distillation
Besides, our DPK makes the performance of the student model positively correlated with that of the teacher model, which means that we can further boost the accuracy of students by applying larger teachers.
GroupFormer: Group Activity Recognition with Clustered Spatial-Temporal Transformer
It captures spatial-temporal contextual information jointly to augment the individual and group representations effectively with a clustered spatial-temporal transformer.
Video Crowd Localization with Multi-focus Gaussian Neighborhood Attention and a Large-Scale Benchmark
Video crowd localization is a crucial yet challenging task, which aims to estimate exact locations of human heads in the given crowded videos.
N-Bref : A High-fidelity Decompiler Exploiting Programming Structures
In software development, decompilation aims to reverse engineer binary executables.
Differentially-private Federated Neural Architecture Search
To address this problem, we propose federated neural architecture search (FNAS), where different parties collectively search for a differentiable architecture by exchanging gradients of architecture variables without exposing their data to other parties.
