Search Results for author:
Found 14 papers, 9 papers with code
ET-BERT: A Contextualized Datagram Representation with Pre-training Transformers for Encrypted Traffic Classification
In this paper, we propose a new traffic representation model called Encrypted Traffic Bidirectional Encoder Representations from Transformer (ET-BERT), which pre-trains deep contextualized datagram-level representation from large-scale unlabeled data.
Context-I2W: Mapping Images to Context-dependent Words for Accurate Zero-Shot Composed Image Retrieval
Different from Composed Image Retrieval task that requires expensive labels for training task-specific models, Zero-Shot Composed Image Retrieval (ZS-CIR) involves diverse tasks with a broad range of visual content manipulation intent that could be related to domain, scene, object, and attribute.
GraphFit: Learning Multi-scale Graph-Convolutional Representation for Point Cloud Normal Estimation
We propose a precise and efficient normal estimation method that can deal with noise and nonuniform density for unstructured 3D point clouds.
Ranked #4 on
Surface Normals Estimation
on PCPNet
Evaluate Geometry of Radiance Fields with Low-frequency Color Prior
From this insight, given a reconstructed density field and observation images, we design a closed-form method to approximate the color field with low-frequency spherical harmonics, and compute the inverse mean residual color.
6GAN: IPv6 Multi-Pattern Target Generation via Generative Adversarial Nets with Reinforcement Learning
6GAN forces multiple generators to train with a multi-class discriminator and an alias detector to generate non-aliased active targets with different addressing pattern types.
Watermarking Vision-Language Pre-trained Models for Multi-modal Embedding as a Service
Our extensive experiments on various datasets indicate that the proposed watermarking approach is effective and safe for verifying the copyright of VLPs for multi-modal EaaS and robust against model extraction attacks.
SiamHAN: IPv6 Address Correlation Attacks on TLS Encrypted Traffic via Siamese Heterogeneous Graph Attention Network
To do this, we propose an IPv6 address correlation model - SiamHAN.
SC-Tune: Unleashing Self-Consistent Referential Comprehension in Large Vision Language Models
In this paper, we present and delve into the self-consistency capability of LVLMs, a crucial aspect that reflects the models' ability to both generate informative captions for specific objects and subsequently utilize these captions to accurately re-identify the objects in a closed-loop process.
Align before Search: Aligning Ads Image to Text for Accurate Cross-Modal Sponsored Search
Conventional research mainly studies from the view of modeling the implicit correlations between images and texts for query-ads matching, ignoring the alignment of detailed product information and resulting in suboptimal search performance. In this work, we propose a simple alignment network for explicitly mapping fine-grained visual parts in ads images to the corresponding text, which leverages the co-occurrence structure consistency between vision and language spaces without requiring expensive labeled training data.
Ranked #1 on
Image-text matching
on CommercialAdsDataset
Using Non-invertible Data Transformations to Build Adversarial-Robust Neural Networks
Deep neural networks have proven to be quite effective in a wide variety of machine learning tasks, ranging from improved speech recognition systems to advancing the development of autonomous vehicles.
6VecLM: Language Modeling in Vector Space for IPv6 Target Generation
Fast IPv6 scanning is challenging in the field of network measurement as it requires exploring the whole IPv6 address space but limited by current computational power.
6GCVAE: Gated Convolutional Variational Autoencoder for IPv6 Target Generation
IPv6 scanning has always been a challenge for researchers in the field of network measurement.
TTAGN: Temporal Transaction Aggregation Graph Network for Ethereum Phishing Scams Detection
In this paper, we propose a Temporal Transaction Aggregation Graph Network (TTAGN) to enhance phishing scams detection performance on Ethereum.
RIME: Robust Preference-based Reinforcement Learning with Noisy Preferences
Our experiments on robotic manipulation and locomotion tasks demonstrate that RIME significantly enhances the robustness of the current state-of-the-art PbRL method.
