Search Results for author:
Found 41 papers, 15 papers with code
TEAM HUB@LT-EDI-EACL2021: Hope Speech Detection Based On Pre-trained Language Model
The data set in the shared task consists of three different languages (English, Tamil, and Malayalam).
HUB@DravidianLangTech-EACL2021: Meme Classification for Tamil Text-Image Fusion
The data of the meme classification task is the fusion data of text and image information.
HUB@DravidianLangTech-EACL2021: Identify and Classify Offensive Text in Multilingual Code Mixing in Social Media
Among the known tasks related to offensive speech detection, this is the first task to detect offensive comments posted in social media comments in the Dravidian language.
AttenST: A Training-Free Attention-Driven Style Transfer Framework with Pre-Trained Diffusion Models
While diffusion models have achieved remarkable progress in style transfer tasks, existing methods typically rely on fine-tuning or optimizing pre-trained models during inference, leading to high computational costs and challenges in balancing content preservation with style integration.
Who's the MVP? A Game-Theoretic Evaluation Benchmark for Modular Attribution in LLM Agents
Large Language Model (LLM) agents frameworks often employ modular architectures, incorporating components such as planning, reasoning, action execution, and reflection to tackle complex tasks.
Fitting Different Interactive Information: Joint Classification of Emotion and Intention
This paper is the first-place solution for ICASSP MEIJU@2025 Track I, which focuses on low-resource multimodal emotion and intention recognition.
MANet: Fine-Tuning Segment Anything Model for Multimodal Remote Sensing Semantic Segmentation
Building upon recent advancements in vision foundation models, particularly the Segment Anything Model (SAM), this study introduces a novel Multimodal Adapter-based Network (MANet) for multimodal remote sensing semantic segmentation.
Bridging and Modeling Correlations in Pairwise Data for Direct Preference Optimization
Firstly, we increase the consistency and informativeness of the pairwise preference signals through targeted modifications, synthesizing a pseudo-winning response by improving the losing response with the winning response as a reference.
Your decision path does matter in pre-training industrial recommenders with multi-source behaviors
Online service platforms offering a wide range of services through miniapps have become crucial for users who visit these platforms with clear intentions to find services they are interested in.
Xiwu: A Basis Flexible and Learnable LLM for High Energy Physics
To address this challenge, a sophisticated large language model system named as Xiwu has been developed, allowing you switch between the most advanced foundation models and quickly teach the model domain knowledge.
LLM-Assisted Light: Leveraging Large Language Model Capabilities for Human-Mimetic Traffic Signal Control in Complex Urban Environments
Specifically, a hybrid framework that augments LLMs with a suite of perception and decision-making tools is proposed, facilitating the interrogation of both the static and dynamic traffic information.
MetaSeg: Content-Aware Meta-Net for Omni-Supervised Semantic Segmentation
Noisy labels, inevitably existing in pseudo segmentation labels generated from weak object-level annotations, severely hampers model optimization for semantic segmentation.
Dataset Distillation via Adversarial Prediction Matching
This ensures the memory efficiency of our method and provides a flexible tradeoff between time and memory budgets, allowing us to distil ImageNet-1K using a minimum of only 6. 5GB of GPU memory.
SAM-Assisted Remote Sensing Imagery Semantic Segmentation with Object and Boundary Constraints
Furthermore, the boundary loss capitalizes on the distinctive features of SGB by directing the model's attention to the boundary information of the object.
PEACE: Prototype lEarning Augmented transferable framework for Cross-domain rEcommendation
To help merchants/customers to provide/access a variety of services through miniapps, online service platforms have occupied a critical position in the effective content delivery, in which how to recommend items in the new domain launched by the service provider for customers has become more urgent.
Which Matters Most in Making Fund Investment Decisions? A Multi-granularity Graph Disentangled Learning Framework
In this paper, we highlight that both conformity and risk preference matter in making fund investment decisions beyond personal interest and seek to jointly characterize these aspects in a disentangled manner.
Bounding and Filling: A Fast and Flexible Framework for Image Captioning
Several models adopted a non-autoregressive manner to speed up the process.
UniKG: A Benchmark and Universal Embedding for Large-Scale Knowledge Graphs
To perform effective learning on the large-scale UniKG, two key measures are taken, including (i) the semantic alignment strategy for multi-attribute entities, which projects the feature description of multi-attribute nodes into a common embedding space to facilitate node aggregation in a large receptive field; (ii) proposing a novel plug-and-play anisotropy propagation module (APM) to learn effective multi-hop anisotropy propagation kernels, which extends methods of large-scale homogeneous graphs to heterogeneous graphs.
Generation of 3D Molecules in Pockets via Language Model
Generative models for molecules based on sequential line notation (e. g.
Bridging the Gap between Chemical Reaction Pretraining and Conditional Molecule Generation with a Unified Model
Chemical reactions are the fundamental building blocks of drug design and organic chemistry research.
Boosting Accuracy and Robustness of Student Models via Adaptive Adversarial Distillation
Thus, recent studies concern about adversarial distillation (AD) that aims to inherit not only prediction accuracy but also adversarial robustness of a robust teacher model under the paradigm of robust optimization.
Does Text Attract Attention on E-Commerce Images: A Novel Saliency Prediction Dataset and Method
E-commerce images are playing a central role in attracting people's attention when retailing and shopping online, and an accurate attention prediction is of significant importance for both customers and retailers, where its research is yet to start.
Ranked #3 on
Saliency Prediction
on SALECI
Delving into Sample Loss Curve to Embrace Noisy and Imbalanced Data
In practice, however, biased samples with corrupted labels and of tailed classes commonly co-exist in training data.
MDistMult: A Multiple Scoring Functions Model for Link Prediction on Antiviral Drugs Knowledge Graph
However, KGs are always incomplete, especially the new constructed COVID-19 KGs.
hub at SemEval-2021 Task 2: Word Meaning Similarity Prediction Model Based on RoBERTa and Word Frequency
What we need to do is to use our method to determine as accurately as possible the meaning of the words in a sentence pair are the same or different.
hub at SemEval-2021 Task 1: Fusion of Sentence and Word Frequency to Predict Lexical Complexity
Use Inception block as a shared layer to learn sentence and word frequency information We described the implementation of our best system and discussed our methods and experiments in the task.
hub at SemEval-2021 Task 7: Fusion of ALBERT and Word Frequency Information Detecting and Rating Humor and Offense
The final scores of the prediction results of the two subtask test sets submitted by our team are task1a 0. 921 (F1), task1a 0. 9364 (Accuracy), task1b 0. 6288 (RMSE), task1c 0. 5333 (F1), task1c 0. 0. 5591 (Accuracy), and task2 0. 5027 (RMSE) respectively.
hub at SemEval-2021 Task 5: Toxic Span Detection Based on Word-Level Classification
This article introduces the system description of the hub team, which explains the related work and experimental results of our team{'}s participation in SemEval 2021 Task 5: Toxic Spans Detection.
WeiPS: a symmetric fusion model framework for large-scale online learning
The recommendation system is an important commercial application of machine learning, where billions of feed views in the information flow every day.
Reimagining City Configuration: Automated Urban Planning via Adversarial Learning
The objective is then to propose an adversarial learning framework that can automatically generate such tensor for an unplanned area.
An Inter- and Intra-Band Loss for Pansharpening Convolutional Neural Networks
Pansharpening aims to fuse panchromatic and multispectral images from the satellite to generate images with both high spatial and spectral resolution.
UBER-GNN: A User-Based Embeddings Recommendation based on Graph Neural Networks
The problem of session-based recommendation aims to predict user next actions based on session histories.
Multiform Fonts-to-Fonts Translation via Style and Content Disentangled Representations of Chinese Character
The main purpose of this paper is to design a network framework that can extract and recombine the content and style of the characters.
Automatic Generation of Chinese Handwriting via Fonts Style Representation Learning
In this paper, we propose and end-to-end deep Chinese font generation system.
Inverse design of multilayer nanoparticles using artificial neural networks and genetic algorithm
Due to the non-unique relationship between physical structures and optical responses, we first train a forward neural network, and then it is applied to the inverse design of multilayer nanoparticles.
u-net CNN based fourier ptychography
Fourier ptychography is a recently explored imaging method for overcoming the diffraction limit of conventional cameras with applications in microscopy and yielding high-resolution images.
Multi-Task Deep Learning with Dynamic Programming for Embryo Early Development Stage Classification from Time-Lapse Videos
Accurate classification of embryo early development stages can provide embryologists valuable information for assessing the embryo quality, and hence is critical to the success of IVF.
Asking the Crowd: Question Analysis, Evaluation and Generation for Open Discussion on Online Forums
For openQG task, we construct OQGenD, the first dataset as far as we know, and propose a model based on conditional generative adversarial networks and our question evaluation model.
A New Method of Region Embedding for Text Classification
In this paper, we propose a new method of learning and utilizing task-specific distributed representations of n-grams, referred to as “region embeddings”.
Towards End-to-End Face Recognition through Alignment Learning
Although these methods differ essentially in many aspects, a common practice of them is to specifically align the facial area based on the prior knowledge of human face structure before feature extraction.
Square Deal: Lower Bounds and Improved Relaxations for Tensor Recovery
The most popular convex relaxation of this problem minimizes the sum of the nuclear norms of the unfoldings of the tensor.
