Search Results for author:
Found 16 papers, 6 papers with code
Ferret-v2: An Improved Baseline for Referring and Grounding with Large Language Models
While Ferret seamlessly integrates regional understanding into the Large Language Model (LLM) to facilitate its referring and grounding capability, it poses certain limitations: constrained by the pre-trained fixed visual encoder and failed to perform well on broader tasks.
Ranked #54 on
Visual Question Answering
on MM-Vet
Bringing Back the Context: Camera Trap Species Identification as Link Prediction on Multimodal Knowledge Graphs
In this work, we leverage the structured context associated with the camera trap images to improve out-of-distribution generalization for the task of species identification in camera traps.
Federated Learning of Shareable Bases for Personalization-Friendly Image Classification
FedBasis learns a set of few shareable ``basis'' models, which can be linearly combined to form personalized models for clients.
Learning Fractals by Gradient Descent
Fractals are geometric shapes that can display complex and self-similar patterns found in nature (e. g., clouds and plants).
Making Batch Normalization Great in Federated Deep Learning
We reinvestigate factors that are believed to cause this problem, including the mismatch of BN statistics across clients and the deviation of gradients during local training.
Train-Once-for-All Personalization
We study the problem of how to train a "personalization-friendly" model such that given only the task descriptions, the model can be adapted to different end-users' needs, e. g., for accurately classifying different subsets of objects.
Gradual Domain Adaptation without Indexed Intermediate Domains
This coarse domain sequence then undergoes a fine indexing step via a novel cycle-consistency loss, which encourages the next intermediate domain to preserve sufficient discriminative knowledge of the current intermediate domain.
On the Importance and Applicability of Pre-Training for Federated Learning
To make our findings applicable to situations where pre-trained models are not directly available, we explore pre-training with synthetic data or even with clients' data in a decentralized manner, and found that they can already improve FL notably.
On Bridging Generic and Personalized Federated Learning for Image Classification
On the one hand, we introduce a family of losses that are robust to non-identical class distributions, enabling clients to train a generic predictor with a consistent objective across them.
FedBE: Making Bayesian Model Ensemble Applicable to Federated Learning
Federated learning aims to collaboratively train a strong global model by accessing users' locally trained models but not their own data.
Glyph2Vec: Learning Chinese Out-of-Vocabulary Word Embedding from Glyphs
Chinese NLP applications that rely on large text often contain huge amounts of vocabulary which are sparse in corpus.
Identifying and Compensating for Feature Deviation in Imbalanced Deep Learning
Classifiers trained with class-imbalanced data are known to perform poorly on test data of the "minor" classes, of which we have insufficient training data.
Multiple Text Style Transfer by using Word-level Conditional Generative Adversarial Network with Two-Phase Training
The objective of non-parallel text style transfer, or controllable text generation, is to alter specific attributes (e. g. sentiment, mood, tense, politeness, etc) of a given text while preserving its remaining attributes and content.
Self-Discriminative Learning for Unsupervised Document Embedding
Unsupervised document representation learning is an important task providing pre-trained features for NLP applications.
DEEP-TRIM: REVISITING L1 REGULARIZATION FOR CONNECTION PRUNING OF DEEP NETWORK
State-of-the-art deep neural networks (DNNs) typically have tens of millions of parameters, which might not fit into the upper levels of the memory hierarchy, thus increasing the inference time and energy consumption significantly, and prohibiting their use on edge devices such as mobile phones.
Word Relation Autoencoder for Unseen Hypernym Extraction Using Word Embeddings
Lexicon relation extraction given distributional representation of words is an important topic in NLP.
