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Found 9 papers, 2 papers with code
ViTGaze: Gaze Following with Interaction Features in Vision Transformers
Our method achieves state-of-the-art (SOTA) performance among all single-modality methods (3. 4% improvement on AUC, 5. 1% improvement on AP) and very comparable performance against multi-modality methods with 59% number of parameters less.
Federated Causality Learning with Explainable Adaptive Optimization
We design a global optimization formula to naturally aggregate the causal graphs from client data and constrain the acyclicity of the global graph without exposing local data.
ResDiff: Combining CNN and Diffusion Model for Image Super-Resolution
Adapting the Diffusion Probabilistic Model (DPM) for direct image super-resolution is wasteful, given that a simple Convolutional Neural Network (CNN) can recover the main low-frequency content.
Long-tail Cross Modal Hashing
LtCMH firstly adopts auto-encoders to mine the individuality and commonality of different modalities by minimizing the dependency between the individuality of respective modalities and by enhancing the commonality of these modalities.
Rainformer: Features Extraction Balanced Network for Radar-Based Precipitation Nowcasting
The experimental results show that Rainformer outperforms seven state of the arts methods on the benchmark database and provides more insights into the high-intensity rainfall prediction task.
Ranked #5 on
Weather Forecasting
on SEVIR
Codebook-Based Beam Tracking for Conformal ArrayEnabled UAV MmWave Networks
Aiming to address the beam tracking difficulties, we propose to integrate the conformal array (CA) with the surface of each UAV, which enables the full spatial coverage and the agile beam tracking in highly dynamic UAV mmWave networks.
Tensor Oriented No-Reference Light Field Image Quality Assessment
Light field image (LFI) quality assessment is becoming more and more important, which helps to better guide the acquisition, processing and application of immersive media.
No-Reference Light Field Image Quality Assessment Based on Spatial-Angular Measurement
In this paper, we propose a No-Reference Light Field image Quality Assessment (NR-LFQA) scheme, where the main idea is to quantify the LFI quality degradation through evaluating the spatial quality and angular consistency.
Learning Vertex Representations for Bipartite Networks
Recent years have witnessed a widespread increase of interest in network representation learning (NRL).
