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Found 13 papers, 4 papers with code
EEE-Bench: A Comprehensive Multimodal Electrical And Electronics Engineering Benchmark
In summary, we believe EEE-Bench not only reveals some noteworthy limitations of LMMs but also provides a valuable resource for advancing research on their application in practical engineering tasks, driving future improvements in their capability to handle complex, real-world scenarios.
SafeSora: Towards Safety Alignment of Text2Video Generation via a Human Preference Dataset
To mitigate the risk of harmful outputs from large vision models (LVMs), we introduce the SafeSora dataset to promote research on aligning text-to-video generation with human values.
Knowledge Graph Large Language Model (KG-LLM) for Link Prediction
We first convert structured knowledge graph data into natural language and then use these natural language prompts to fine-tune LLMs to enhance multi-hop link prediction in KGs.
Pre-Training LiDAR-Based 3D Object Detectors Through Colorization
Accurate 3D object detection and understanding for self-driving cars heavily relies on LiDAR point clouds, necessitating large amounts of labeled data to train.
Segment Anything Model (SAM) Enhanced Pseudo Labels for Weakly Supervised Semantic Segmentation
Weakly supervised semantic segmentation (WSSS) aims to bypass the need for laborious pixel-level annotation by using only image-level annotation.
Center-aware Adversarial Augmentation for Single Domain Generalization
Domain generalization (DG) aims to learn a model from multiple training (i. e., source) domains that can generalize well to the unseen test (i. e., target) data coming from a different distribution.
Ranked #3 on
Single-Source Domain Generalization
on Digits-five
Learning with Free Object Segments for Long-Tailed Instance Segmentation
One fundamental challenge in building an instance segmentation model for a large number of classes in complex scenes is the lack of training examples, especially for rare objects.
Learning to Generate the Unknowns for Open-set Domain Adaptation
In this setting, existing techniques focus on the challenging task of isolating the unknown target samples, so as to avoid the negative transfer resulting from aligning the source feature distributions with the broader target one that encompasses the additional unknown classes.
Defending Adversarial Attacks via Semantic Feature Manipulation
In this paper, we propose a one-off and attack-agnostic Feature Manipulation (FM)-Defense to detect and purify adversarial examples in an interpretable and efficient manner.
OIAD: One-for-all Image Anomaly Detection with Disentanglement Learning
Our key insight is that the impact of small perturbation on the latent representation can be bounded for normal samples while anomaly images are usually outside such bounded intervals, referred to as structure consistency.
Backdoor Attacks against Transfer Learning with Pre-trained Deep Learning Models
In this paper, we demonstrate a backdoor threat to transfer learning tasks on both image and time-series data leveraging the knowledge of publicly accessible Teacher models, aimed at defeating three commonly-adopted defenses: \textit{pruning-based}, \textit{retraining-based} and \textit{input pre-processing-based defenses}.
Generating Semantic Adversarial Examples via Feature Manipulation
We further demonstrate the existence of a universal, image-agnostic semantic adversarial example.
Multivariate Arrival Times with Recurrent Neural Networks for Personalized Demand Forecasting
However, buyer purchase patterns are extremely diverse and sparse on a per-product level due to population heterogeneity as well as dependence in purchase patterns across product categories.
