Search Results for author:
Found 10 papers, 6 papers with code
A Unified Framework for Connecting Noise Modeling to Boost Noise Detection
In this work, we explore the integration of these two approaches, proposing an interconnected structure with three crucial blocks: noise modeling, source knowledge identification, and enhanced noise detection using noise source-knowledge-integration methods.
MixtureGrowth: Growing Neural Networks by Recombining Learned Parameters
Newly grown layer weights are generated by using a new linear combination of existing templates for a layer.
CHAMMI: A benchmark for channel-adaptive models in microscopy imaging
In this paper, we present a benchmark for investigating channel-adaptive models in microscopy imaging, which consists of 1) a dataset of varied-channel single-cell images, and 2) a biologically relevant evaluation framework.
LP-OVOD: Open-Vocabulary Object Detection by Linear Probing
To address this issue, we propose a novel method, LP-OVOD, that discards low-quality boxes by training a sigmoid linear classifier on pseudo labels retrieved from the top relevant region proposals to the novel text.
Ranked #4 on
Open Vocabulary Object Detection
on MSCOCO
(using extra training data)
Let Models Speak Ciphers: Multiagent Debate through Embeddings
Although natural language is an obvious choice for communication due to LLM's language understanding capability, the token sampling step needed when generating natural language poses a potential risk of information loss, as it uses only one token to represent the model's belief across the entire vocabulary.
Deep Distance Sensitivity Oracles
More specifically, we utilize the combinatorial structure of replacement paths as a concatenation of shortest paths and use deep learning to find the pivot nodes for stitching shortest paths into replacement paths.
A Multi-scale Graph Signature for Persistence Diagrams based on Return Probabilities of Random Walks
Persistence diagrams (PDs), often characterized as sets of death and birth of homology class, have been known for providing a topological representation of a graph structure, which is often useful in machine learning tasks.
Few-shot Object Counting and Detection
We tackle a new task of few-shot object counting and detection.
Ranked #10 on
Object Counting
on FSC147
Emotion analysis and detection during COVID-19
Crises such as natural disasters, global pandemics, and social unrest continuously threaten our world and emotionally affect millions of people worldwide in distinct ways.
Road Damage Detection and Classification with Detectron2 and Faster R-CNN
The results show that the X101-FPN base model for Faster R-CNN with Detectron2's default configurations are efficient and general enough to be transferable to different countries in this challenge.
