Search Results for author:
Found 11 papers, 5 papers with code
EmojiCloud: a Tool for Emoji Cloud Visualization
This paper proposes EmojiCloud, an open-source Python-based emoji cloud visualization tool, to generate a quick and straightforward understanding of emojis from the perspective of frequency and importance.
Benchmarking the Robustness of UAV Tracking Against Common Corruptions
Addressing this, we propose UAV-C, a large-scale benchmark for assessing robustness of UAV trackers under common corruptions.
S3LLM: Large-Scale Scientific Software Understanding with LLMs using Source, Metadata, and Document
S3LLM demonstrates the potential of using locally deployed open-source LLMs for the rapid understanding of large-scale scientific computing software, eliminating the need for extensive coding expertise, and thereby making the process more efficient and effective.
Efficient Multimodal Semantic Segmentation via Dual-Prompt Learning
Existing approaches often fully fine-tune a dual-branch encoder-decoder framework with a complicated feature fusion strategy for achieving multimodal semantic segmentation, which is training-costly due to the massive parameter updates in feature extraction and fusion.
Ranked #2 on
Semantic Segmentation
on SUN-RGBD
(using extra training data)
Addressing Weak Decision Boundaries in Image Classification by Leveraging Web Search and Generative Models
Our new method is able to (1) identify weak decision boundaries for such classes; (2) construct search queries for Google as well as text for generating images through DALL-E 2 and Stable Diffusion; and (3) show how these newly captured training samples could alleviate population bias issue.
Towards Transferable Targeted Adversarial Examples
In this paper, we propose the Transferable Targeted Adversarial Attack (TTAA), which can capture the distribution information of the target class from both label-wise and feature-wise perspectives, to generate highly transferable targeted adversarial examples.
Towards Fairness-aware Adversarial Network Pruning
Network pruning aims to compress models while minimizing loss in accuracy.
Towards Generating Robust, Fair, and Emotion-Aware Explanations for Recommender Systems
Current explanation generation models are found to exaggerate certain emotions without accurately capturing the underlying tone or the meaning.
COMET: A Novel Memory-Efficient Deep Learning Training Framework by Using Error-Bounded Lossy Compression
In this paper, we propose a novel memory-efficient CNN training framework (called COMET) that leverages error-bounded lossy compression to significantly reduce the memory requirement for training, to allow training larger models or to accelerate training.
Seed Stocking Via Multi-Task Learning
First, we will estimate the yield and risk of each variety as if they were planted at each location.
