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20142024
most citedDeep Captioning with Multimodal Recurrent Neural Networks (m-RNN)

652 citations · 1.7k across the 56 of their papers we have counts for

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48 papers · 1 filter

cs.CV2024

ExoViP: Step-by-step Verification and Exploration with Exoskeleton Modules for Compositional Visual Reasoning

Yuxuan Wang, Alan Yuille, Zhuowan Li +1

Compositional visual reasoning methods, which translate a complex query into a structured composition of feasible visual tasks, have exhibited a strong potential in complicated mul…

cs.CV2024

Rethinking Video-Text Understanding: Retrieval from Counterfactually Augmented Data

Wufei Ma, Kai Li, Zhongshi Jiang +5

Recent video-text foundation models have demonstrated strong performance on a wide variety of downstream video understanding tasks. Can these video-text models genuinely understand…

cs.CV2024

CamFreeDiff: Camera-free Image to Panorama Generation with Diffusion Model

Xiaoding Yuan, Shitao Tang, Kejie Li +2

This paper introduces Camera-free Diffusion (CamFreeDiff) model for 360-degree image outpainting from a single camera-free image and text description. This method distinguishes its…

cs.CV20241 cited

ImageNet3D: Towards General-Purpose Object-Level 3D Understanding

Wufei Ma, Guanning Zeng, Guofeng Zhang +5

A vision model with general-purpose object-level 3D understanding should be capable of inferring both 2D (e.g., class name and bounding box) and 3D information (e.g., 3D location a…

cs.CV2024

Autoregressive Pretraining with Mamba in Vision

Sucheng Ren, Xianhang Li, Haoqin Tu +9

The vision community has started to build with the recently developed state space model, Mamba, as the new backbone for a range of tasks. This paper shows that Mamba's visual capab…

cs.CV2024

Medical Vision Generalist: Unifying Medical Imaging Tasks in Context

Sucheng Ren, Xiaoke Huang, Xianhang Li +5

This study presents Medical Vision Generalist (MVG), the first foundation model capable of handling various medical imaging tasks -- such as cross-modal synthesis, image segmentati…