Search Results for author:
Found 19 papers, 4 papers with code
Modeling Collaborator: Enabling Subjective Vision Classification With Minimal Human Effort via LLM Tool-Use
Our framework leverages recent advances in foundation models, both large language models and vision-language models, to carve out the concept space through conversation and by automatically labeling training data points.
Scaling Up LLM Reviews for Google Ads Content Moderation
This study proposes a method for scaling up LLM reviews for content moderation in Google Ads.
Visual Program Distillation: Distilling Tools and Programmatic Reasoning into Vision-Language Models
We propose Visual Program Distillation (VPD), an instruction tuning framework that produces a vision-language model (VLM) capable of solving complex visual tasks with a single forward pass.
Ranked #1 on
Meme Classification
on Hateful Memes
Non-Intrusive Adaptation: Input-Centric Parameter-efficient Fine-Tuning for Versatile Multimodal Modeling
Intrusive PEFT techniques directly change a model's internal architecture.
Agile Modeling: From Concept to Classifier in Minutes
In reaction, we introduce the problem of Agile Modeling: the process of turning any subjective visual concept into a computer vision model through a real-time user-in-the-loop interactions.
Benchmarking Robustness to Adversarial Image Obfuscations
We evaluate 33 pretrained models on the benchmark and train models with different augmentations, architectures and training methods on subsets of the obfuscations to measure generalization.
CARLS: Cross-platform Asynchronous Representation Learning System
In this work, we propose CARLS, a novel framework for augmenting the capacity of existing deep learning frameworks by enabling multiple components -- model trainers, knowledge makers and knowledge banks -- to concertedly work together in an asynchronous fashion across hardware platforms.
MZET: Memory Augmented Zero-Shot Fine-grained Named Entity Typing
Named entity typing (NET) is a classification task of assigning an entity mention in the context with given semantic types.
Community-preserving Graph Convolutions for Structural and Functional Joint Embedding of Brain Networks
Specifically, we use graph convolutions to learn the structural and functional joint embedding, where the graph structure is defined with structural connectivity and node features are from the functional connectivity.
Graph-RISE: Graph-Regularized Image Semantic Embedding
Learning image representations to capture fine-grained semantics has been a challenging and important task enabling many applications such as image search and clustering.
Ranked #11 on
Image Classification
on iNaturalist
A Self-Organizing Tensor Architecture for Multi-View Clustering
In many real-world applications, data are often unlabeled and comprised of different representations/views which often provide information complementary to each other.
Spectral Collaborative Filtering
Benefiting from the rich information of connectivity existing in the \textit{spectral domain}, SpectralCF is capable of discovering deep connections between users and items and therefore, alleviates the \textit{cold-start} problem for CF.
Bringing Semantic Structures to User Intent Detection in Online Medical Queries
The healthcare status, complex medical information needs of patients are expressed diversely and implicitly in their medical text queries.
Multi-view Graph Embedding with Hub Detection for Brain Network Analysis
Specifically, we propose an auto-weighted framework of Multi-view Graph Embedding with Hub Detection (MVGE-HD) for brain network analysis.
Kernelized Support Tensor Machines
In the context of supervised tensor learning, preserving the structural information and exploiting the discriminative nonlinear relationships of tensor data are crucial for improving the performance of learning tasks.
Multi-Way Multi-Level Kernel Modeling for Neuroimaging Classification
Owing to prominence as a diagnostic tool for probing the neural correlates of cognition, neuroimaging tensor data has been the focus of intense investigation.
Learning from Multi-View Multi-Way Data via Structural Factorization Machines
Real-world relations among entities can often be observed and determined by different perspectives/views.
Online Multi-view Clustering with Incomplete Views
We model the multi-view clustering problem as a joint weighted nonnegative matrix factorization problem and process the multi-view data chunk by chunk to reduce the memory requirement.
Online Unsupervised Multi-view Feature Selection
Third, how to leverage the consistent and complementary information from different views to improve the feature selection in the situation when the data are too big or come in as streams?
