Search Results for author:
Found 38 papers, 11 papers with code
On the Road with GPT-4V(ision): Early Explorations of Visual-Language Model on Autonomous Driving
This has been a significant bottleneck, particularly in the development of common sense reasoning and nuanced scene understanding necessary for safe and reliable autonomous driving.
The Janus Interface: How Fine-Tuning in Large Language Models Amplifies the Privacy Risks
In the attack, one can construct a PII association task, whereby an LLM is fine-tuned using a minuscule PII dataset, to potentially reinstate and reveal concealed PIIs.
Large Language Model Soft Ideologization via AI-Self-Consciousness
Large language models (LLMs) have demonstrated human-level performance on a vast spectrum of natural language tasks.
Reliable Majority Vote Computation with Complementary Sequences for UAV Waypoint Flight Control
In this study, we propose a non-coherent over-the-air computation (OAC) scheme to calculate the majority vote (MV) reliably in fading channels.
DriveDreamer: Towards Real-world-driven World Models for Autonomous Driving
The established world model holds immense potential for the generation of high-quality driving videos, and driving policies for safe maneuvering.
Towards Imbalanced Large Scale Multi-label Classification with Partially Annotated Labels
In this work, we address the issue of label imbalance and investigate how to train classifiers using partial labels in large labeling spaces.
CDUL: CLIP-Driven Unsupervised Learning for Multi-Label Image Classification
Using the aggregated similarity scores as the initial pseudo labels at the training stage, we propose an optimization framework to train the parameters of the classification network and refine pseudo labels for unobserved labels.
MAWSEO: Adversarial Wiki Search Poisoning for Illicit Online Promotion
As a prominent instance of vandalism edits, Wiki search poisoning for illicit promotion is a cybercrime in which the adversary aims at editing Wiki articles to promote illicit businesses through Wiki search results of relevant queries.
D-Score: A White-Box Diagnosis Score for CNNs Based on Mutation Operators
However, concerns have been raised with respect to the trustworthiness of these models: The standard testing method evaluates the performance of a model on a test set, while low-quality and insufficient test sets can lead to unreliable evaluation results, which can have unforeseeable consequences.
SSL-Cleanse: Trojan Detection and Mitigation in Self-Supervised Learning
We evaluated SSL-Cleanse on various datasets using 1200 encoders, achieving an average detection success rate of 82. 2% on ImageNet-100.
OpenOccupancy: A Large Scale Benchmark for Surrounding Semantic Occupancy Perception
Towards a comprehensive benchmarking of surrounding perception algorithms, we propose OpenOccupancy, which is the first surrounding semantic occupancy perception benchmark.
CSDR-BERT: a pre-trained scientific dataset match model for Chinese Scientific Dataset Retrieval
As the number of open and shared scientific datasets on the Internet increases under the open science movement, efficiently retrieving these datasets is a crucial task in information retrieval (IR) research.
Gradient Shaping: Enhancing Backdoor Attack Against Reverse Engineering
Finally, we perform both theoretical and experimental analysis, showing that the GRASP enhancement does not reduce the effectiveness of the stealthy attacks against the backdoor detection methods based on weight analysis, as well as other backdoor mitigation methods without using detection.
FE-TCM: Filter-Enhanced Transformer Click Model for Web Search
In this paper, We use Transformer as the backbone network of feature extraction, add filter layer innovatively, and propose a new Filter-Enhanced Transformer Click Model (FE-TCM) for web search.
Are We Ready for Vision-Centric Driving Streaming Perception? The ASAP Benchmark
To mitigate the problem, we propose the Autonomous-driving StreAming Perception (ASAP) benchmark, which is the first benchmark to evaluate the online performance of vision-centric perception in autonomous driving.
Selective Amnesia: On Efficient, High-Fidelity and Blind Suppression of Backdoor Effects in Trojaned Machine Learning Models
Our idea is to retrain a given DNN model on randomly labeled clean data, to induce a CF on the model, leading to a sudden forget on both primary and backdoor tasks; then we recover the primary task by retraining the randomized model on correctly labeled clean data.
Depth Monocular Estimation with Attention-based Encoder-Decoder Network from Single Image
Depth information is the foundation of perception, essential for autonomous driving, robotics, and other source-constrained applications.
An Effective Approach for Multi-label Classification with Missing Labels
Through comprehensive experiments on three large-scale multi-label image datasets, i. e. MS-COCO, NUS-WIDE, and Pascal VOC12, we show that our method can handle the imbalance between positive labels and negative labels, while still outperforming existing missing-label learning approaches in most cases, and in some cases even approaches with fully labeled datasets.
G2NetPL: Generic Game-Theoretic Network for Partial-Label Image Classification
To effectively address partial-label classification, this paper proposes an end-to-end Generic Game-theoretic Network (G2NetPL) for partial-label learning, which can be applied to most partial-label settings, including a very challenging, but annotation-efficient case where only a subset of the training images are labeled, each with only one positive label, while the rest of the training images remain unlabeled.
Multi-Label Classification
Multi-Label Image Classification
+2
Understanding Impacts of Task Similarity on Backdoor Attack and Detection
With extensive studies on backdoor attack and detection, still fundamental questions are left unanswered regarding the limits in the adversary's capability to attack and the defender's capability to detect.
Order-Disorder: Imitation Adversarial Attacks for Black-box Neural Ranking Models
In this study, we propose an imitation adversarial attack on black-box neural passage ranking models.
Scenario-Adaptive and Self-Supervised Model for Multi-Scenario Personalized Recommendation
we summarize three practical challenges which are not well solved for multi-scenario modeling: (1) Lacking of fine-grained and decoupled information transfer controls among multiple scenarios.
PLMCL: Partial-Label Momentum Curriculum Learning for Multi-Label Image Classification
A special case is to annotate only one positive label in each training image.
Multi-Label Classification
Multi-Label Image Classification
+1
Crafting Monocular Cues and Velocity Guidance for Self-Supervised Multi-Frame Depth Learning
In contrast, multi-frame depth estimation methods improve the depth accuracy thanks to the success of Multi-View Stereo (MVS), which directly makes use of geometric constraints.
MVSTER: Epipolar Transformer for Efficient Multi-View Stereo
Therefore, we present MVSTER, which leverages the proposed epipolar Transformer to learn both 2D semantics and 3D spatial associations efficiently.
PLGAN: Generative Adversarial Networks for Power-Line Segmentation in Aerial Images
Accurate segmentation of power lines in various aerial images is very important for UAV flight safety.
New Benchmark for Household Garbage Image Recognition
Household garbage images are usually faced with complex backgrounds, variable illuminations, diverse angles, and changeable shapes, which bring a great difficulty in garbage image classification.
Domain-Invariant Proposals based on a Balanced Domain Classifier for Object Detection
We embed a domain classification network in the region proposal network~(RPN) using adversarial learning.
Context-aware Heterogeneous Graph Attention Network for User Behavior Prediction in Local Consumer Service Platform
Hence, in this paper, we propose a context-aware heterogeneous graph attention network (CHGAT) to dynamically generate the representation of the user and to estimate the probability for future behavior.
SoK: A Modularized Approach to Study the Security of Automatic Speech Recognition Systems
In this paper, we present our systematization of knowledge for ASR security and provide a comprehensive taxonomy for existing work based on a modularized workflow.
The effect of aspherical stellar wind of giant stars on the symbiotic channel of type Ia supernovae
The progenitor systems accounting for explosions of type Ia supernovae (SNe Ia) is still under debate.
Solar and Stellar Astrophysics
HyMap: eliciting hypotheses in early-stage software startups using cognitive mapping
Regarding the first, it provides a better understanding of the guidance founders use to develop their startups and, for the latter, a technique to identify hypotheses in early-stage software startups.
Computers and Society
SN 2017hpa: A Nearby Carbon-Rich Type Ia Supernova with a Large Velocity Gradient
We present extensive, well-sampled optical and ultraviolet photometry and optical spectra of the Type Ia supernova (SN Ia) 2017hpa.
High Energy Astrophysical Phenomena Solar and Stellar Astrophysics
Towards Dark Jargon Interpretation in Underground Forums
Dark jargons are benign-looking words that have hidden, sinister meanings and are used by participants of underground forums for illicit behavior.
TTPLA: An Aerial-Image Dataset for Detection and Segmentation of Transmission Towers and Power Lines
Accurate detection and segmentation of transmission towers~(TTs) and power lines~(PLs) from aerial images plays a key role in protecting power-grid security and low-altitude UAV safety.
Empirical Standards for Software Engineering Research
Empirical Standards are natural-language models of a scientific community's expectations for a specific kind of study (e. g. a questionnaire survey).
Software Engineering General Literature
Drug–target affinity prediction using graph neural network and contact maps
Computer-aided drug design uses high-performance computers to simulate the tasks in drug design, which is a promising research area.
Ranked #3 on
Drug Discovery
on LIT-PCBA(ALDH1)
Query-Free Attacks on Industry-Grade Face Recognition Systems under Resource Constraints
To launch black-box attacks against a Deep Neural Network (DNN) based Face Recognition (FR) system, one needs to build \textit{substitute} models to simulate the target model, so the adversarial examples discovered from substitute models could also mislead the target model.
