Search Results for author:
Found 9 papers, 7 papers with code
Method of diagnosing heart disease based on deep learning ECG signal
Some have tried to combine expertise and signal processing to classify ECG signal by heart disease type.
Prototype Mixture Models for Few-shot Semantic Segmentation
Few-shot segmentation is challenging because objects within the support and query images could significantly differ in appearance and pose.
Beyond Max-Margin: Class Margin Equilibrium for Few-shot Object Detection
Few-shot object detection has made substantial progressby representing novel class objects using the feature representation learned upon a set of base class objects.
Ranked #14 on
Few-Shot Object Detection
on MS-COCO (10-shot)
Collaboration of Pre-trained Models Makes Better Few-shot Learner
In this paper, we propose CoMo, a Collaboration of pre-trained Models that incorporates diverse prior knowledge from various pre-training paradigms for better few-shot learning.
Proposal Distribution Calibration for Few-Shot Object Detection
Adapting object detectors learned with sufficient supervision to novel classes under low data regimes is charming yet challenging.
Prompt, Generate, then Cache: Cascade of Foundation Models makes Strong Few-shot Learners
Our CaFo incorporates CLIP's language-contrastive knowledge, DINO's vision-contrastive knowledge, DALL-E's vision-generative knowledge, and GPT-3's language-generative knowledge.
SEED-Bench: Benchmarking Multimodal LLMs with Generative Comprehension
Based on powerful Large Language Models (LLMs), recent generative Multimodal Large Language Models (MLLMs) have gained prominence as a pivotal research area, exhibiting remarkable capability for both comprehension and generation.
SEED-Bench-2: Benchmarking Multimodal Large Language Models
Multimodal large language models (MLLMs), building upon the foundation of powerful large language models (LLMs), have recently demonstrated exceptional capabilities in generating not only texts but also images given interleaved multimodal inputs (acting like a combination of GPT-4V and DALL-E 3).
EgoPlan-Bench: Benchmarking Egocentric Embodied Planning with Multimodal Large Language Models
Given diverse environmental inputs, including real-time task progress, visual observations, and open-form language instructions, a proficient task planner is expected to predict feasible actions, which is a feat inherently achievable by Multimodal Large Language Models (MLLMs).
