Search Results for author:
Found 14 papers, 4 papers with code
Renmin University of China at TRECVID 2022: Improving Video Search by Feature Fusion and Negation Understanding
The 2022 edition of the TRECVID benchmark has again been a fruitful participation for the RUCMM team.
UniASM: Binary Code Similarity Detection without Fine-tuning
Binary code similarity detection (BCSD) is widely used in various binary analysis tasks such as vulnerability search, malware detection, clone detection, and patch analysis.
Bridging the gap between target-based and cell-based drug discovery with a graph generative multi-task model
Based on these findings, we utilized a monte carlo based reinforcement learning generative model to generate novel multi-property compounds with both in vitro and in vivo efficacy, thus bridging the gap between target-based and cell-based drug discovery.
Learn to Understand Negation in Video Retrieval
We propose a learning based method for training a negation-aware video retrieval model.
Lightweight Attentional Feature Fusion: A New Baseline for Text-to-Video Retrieval
In this paper we revisit feature fusion, an old-fashioned topic, in the new context of text-to-video retrieval.
Ranked #1 on
Ad-hoc video search
on TRECVID-AVS20 (V3C1)
(using extra training data)
A Novel Framework Integrating AI Model and Enzymological Experiments Promotes Identification of SARS-CoV-2 3CL Protease Inhibitors and Activity-based Probe
The identification of protein-ligand interaction plays a key role in biochemical research and drug discovery.
Prediction of Potential Commercially Available Inhibitors against SARS-CoV-2 by Multi-Task Deep Learning Model
Then, the fine-tuned model was used to select commercially available drugs against SARS-CoV-2 protein targets.
Question Answering via Web Extracted Tables and Pipelined Models
Each QA instance comprises a table of either kind, a natural language question, and a corresponding structured SQL query.
Recent advances and opportunities in scene classification of aerial images with deep models
Scene classification is a fundamental task in interpretation of remote sensing images, and has become an active research topic in remote sensing community due to its important role in a wide range of applications.
Accurate Building Detection in VHR Remote Sensing Images using Geometric Saliency
This paper aims to address the problem of detecting buildings from remote sensing images with very high resolution (VHR).
AID++: An Updated Version of AID on Scene Classification
Aerial image scene classification is a fundamental problem for understanding high-resolution remote sensing images and has become an active research task in the field of remote sensing due to its important role in a wide range of applications.
Exploiting Deep Features for Remote Sensing Image Retrieval: A Systematic Investigation
Over the past two decades, a large amount of research on this task has been carried out, which mainly focuses on the following three core issues: feature extraction, similarity metric and relevance feedback.
AID: A Benchmark Dataset for Performance Evaluation of Aerial Scene Classification
The goal of AID is to advance the state-of-the-arts in scene classification of remote sensing images.
Dense v.s. Sparse: A Comparative Study of Sampling Analysis in Scene Classification of High-Resolution Remote Sensing Imagery
The experimental results on two commonly used datasets show that dense sampling has the best performance among all the strategies but with high spatial and computational complexity, random sampling gives better or comparable results than other sparse sampling methods, like the sophisticated multi-scale key-point operators and the saliency-based methods which are intensively studied and commonly used recently.
