Search Results for author:
Found 12 papers, 6 papers with code
SpatialRGPT: Grounded Spatial Reasoning in Vision Language Models
Vision Language Models (VLMs) have demonstrated remarkable performance in 2D vision and language tasks.
Plot2Code: A Comprehensive Benchmark for Evaluating Multi-modal Large Language Models in Code Generation from Scientific Plots
Furthermore, we propose three automatic evaluation metrics, including code pass rate, text-match ratio, and GPT-4V overall rating, for a fine-grained assessment of the output code and rendered images.
RegionGPT: Towards Region Understanding Vision Language Model
Vision language models (VLMs) have experienced rapid advancements through the integration of large language models (LLMs) with image-text pairs, yet they struggle with detailed regional visual understanding due to limited spatial awareness of the vision encoder, and the use of coarse-grained training data that lacks detailed, region-specific captions.
RAPHAEL: Text-to-Image Generation via Large Mixture of Diffusion Paths
Text-to-image generation has recently witnessed remarkable achievements.
Ranked #11 on
Text-to-Image Generation
on MS COCO
EGC: Image Generation and Classification via a Diffusion Energy-Based Model
Learning image classification and image generation using the same set of network parameters is a challenging problem.
Multi-Level Contrastive Learning for Dense Prediction Task
We extend our pretext task to supervised pre-training, which achieves a similar performance to self-supervised learning.
Rethinking Resolution in the Context of Efficient Video Recognition
Our key finding is that the major cause of degradation is not information loss in the down-sampling process, but rather the mismatch between network architecture and input scale.
Scale-Equivalent Distillation for Semi-Supervised Object Detection
Further, we overcome these challenges by introducing a novel approach, Scale-Equivalent Distillation (SED), which is a simple yet effective end-to-end knowledge distillation framework robust to large object size variance and class imbalance.
Scale-Invariant Teaching for Semi-Supervised Object Detection
Furthermore, the limited annotations in semi-supervised learning scale up the challenges: large variance of object sizes and class imbalance (i. e., the extreme ratio between background and object), hindering the performance of prior arts.
Online Knowledge Distillation via Collaborative Learning
This work presents an efficient yet effective online Knowledge Distillation method via Collaborative Learning, termed KDCL, which is able to consistently improve the generalization ability of deep neural networks (DNNs) that have different learning capacities.
Dynamic Recursive Neural Network
This paper proposes the dynamic recursive neural network (DRNN), which simplifies the duplicated building blocks in deep neural network.
MSFD:Multi-Scale Receptive Field Face Detector
We simultaneously propose an anchor assignment strategy which can cover faces with a wide range of scales to improve the recall rate of small faces and rotated faces.
