19 citations · 42 across the 15 of their papers we have counts for
11 papers · 1 filter
RubricRL: Simple Generalizable Rewards for Text-to-Image Generation
Xuelu Feng, Yunsheng Li, Ziyu Wan +4
Reinforcement learning (RL) has recently emerged as a promising approach for aligning text-to-image generative models with human preferences. A key challenge, however, lies in desi…
Show and Segment: Universal Medical Image Segmentation via In-Context Learning
Yunhe Gao, Di Liu, Zhuowei Li +4
Medical image segmentation remains challenging due to the vast diversity of anatomical structures, imaging modalities, and segmentation tasks. While deep learning has made signific…
Benchmarking Large and Small MLLMs
Xuelu Feng, Yunsheng Li, Dongdong Chen +4
Large multimodal language models (MLLMs) such as GPT-4V and GPT-4o have achieved remarkable advancements in understanding and generating multimodal content, showcasing superior qua…
Olympus: A Universal Task Router for Computer Vision Tasks
Yuanze Lin, Yunsheng Li, Dongdong Chen +3
We introduce Olympus, a new approach that transforms Multimodal Large Language Models (MLLMs) into a unified framework capable of handling a wide array of computer vision tasks. Ut…
Pluralistic Salient Object Detection
Xuelu Feng, Yunsheng Li, Dongdong Chen +4
We introduce pluralistic salient object detection (PSOD), a novel task aimed at generating multiple plausible salient segmentation results for a given input image. Unlike conventio…
Rethinking Visual Prompting for Multimodal Large Language Models with External Knowledge
Yuanze Lin, Yunsheng Li, Dongdong Chen +4
In recent years, multimodal large language models (MLLMs) have made significant strides by training on vast high-quality image-text datasets, enabling them to generally understand…
Fully Authentic Visual Question Answering Dataset from Online Communities
Chongyan Chen, Mengchen Liu, Noel Codella +3
Visual Question Answering (VQA) entails answering questions about images. We introduce the first VQA dataset in which all contents originate from an authentic use case. Sourced fro…
Should All Proposals be Treated Equally in Object Detection?
Yunsheng Li, Yinpeng Chen, Xiyang Dai +7
The complexity-precision trade-off of an object detector is a critical problem for resource constrained vision tasks. Previous works have emphasized detectors implemented with effi…
MicroNet: Improving Image Recognition with Extremely Low FLOPs
Yunsheng Li, Yinpeng Chen, Xiyang Dai +6
This paper aims at addressing the problem of substantial performance degradation at extremely low computational cost (e.g. 5M FLOPs on ImageNet classification). We found that two f…
Revisiting Dynamic Convolution via Matrix Decomposition
Yunsheng Li, Yinpeng Chen, Xiyang Dai +7
Recent research in dynamic convolution shows substantial performance boost for efficient CNNs, due to the adaptive aggregation of K static convolution kernels. It has two limitatio…
MicroNet: Towards Image Recognition with Extremely Low FLOPs
Yunsheng Li, Yinpeng Chen, Xiyang Dai +6
In this paper, we present MicroNet, which is an efficient convolutional neural network using extremely low computational cost (e.g. 6 MFLOPs on ImageNet classification). Such a low…