Search Results for author:
Found 9 papers, 5 papers with code
Vehicular Multi-Tier Distributed Computing with Hybrid THz-RF Transmission in Satellite-Terrestrial Integrated Networks
We formulate a non-convex optimization problem aimed at maximizing computation efficiency by jointly optimizing bandwidth allocation, task allocation, subchannel-vehicle matching and power allocation.
VST++: Efficient and Stronger Visual Saliency Transformer
Our previous work, the Visual Saliency Transformer (VST), addressed this constraint from a transformer-based sequence-to-sequence perspective, to unify RGB and RGB-D SOD.
Discriminative Co-Saliency and Background Mining Transformer for Co-Salient Object Detection
Then, we use two types of pre-defined tokens to mine co-saliency and background information via our proposed contrast-induced pixel-to-token correlation and co-saliency token-to-token correlation modules.
Ranked #1 on
Co-Salient Object Detection
on CoSal2015
AGAD: Adversarial Generative Anomaly Detection
In order to address the lack of abnormal data for robust anomaly detection, we propose Adversarial Generative Anomaly Detection (AGAD), a self-contrast-based anomaly detection paradigm that learns to detect anomalies by generating \textit{contextual adversarial information} from the massive normal examples.
Dissolving Is Amplifying: Towards Fine-Grained Anomaly Detection
DIA is a fine-grained anomaly detection framework for medical images.
Summarize and Search: Learning Consensus-aware Dynamic Convolution for Co-Saliency Detection
In this paper, we propose a novel consensus-aware dynamic convolution model to explicitly and effectively perform the "summarize and search" process.
Ranked #3 on
Co-Salient Object Detection
on CoSal2015
Visual Saliency Transformer
We also develop a token-based multi-task decoder to simultaneously perform saliency and boundary detection by introducing task-related tokens and a novel patch-task-attention mechanism.
Ranked #1 on
RGB-D Salient Object Detection
on NJUD
Learning Selective Mutual Attention and Contrast for RGB-D Saliency Detection
Early fusion and the result fusion schemes fuse RGB and depth information at the input and output stages, respectively, hence incur the problem of distribution gap or information loss.
Visualizing Group Dynamics based on Multiparty Meeting Understanding
Group discussions are usually aimed at sharing opinions, reaching consensus and making good decisions based on group knowledge.
