Search Results for author: Jitesh Jain

Found 7 papers, 7 papers with code

DEAP Cache: Deep Eviction Admission and Prefetching for Cache

1 code implementation19 Sep 2020 Ayush Mangal, Jitesh Jain, Keerat Kaur Guliani, Omkar Bhalerao

While previous approaches used the past as an indicator of the future, we instead explicitly model the future frequency and recency in a multi-task fashion with prefetching, leveraging the abilities of deep networks to capture futuristic trends and use them for learning eviction and admission.

BIG-bench Machine Learning Density Estimation

SeMask: Semantically Masked Transformers for Semantic Segmentation

1 code implementation arXiv 2021 Jitesh Jain, Anukriti Singh, Nikita Orlov, Zilong Huang, Jiachen Li, Steven Walton, Humphrey Shi

To achieve this, we propose SeMask, a simple and effective framework that incorporates semantic information into the encoder with the help of a semantic attention operation.

Semantic Segmentation

Keys to Better Image Inpainting: Structure and Texture Go Hand in Hand

1 code implementation5 Aug 2022 Jitesh Jain, Yuqian Zhou, Ning Yu, Humphrey Shi

We claim that the performance of inpainting algorithms can be better judged by the generated structures and textures.

Image Inpainting Texture Synthesis

OneFormer: One Transformer to Rule Universal Image Segmentation

2 code implementations CVPR 2023 Jitesh Jain, Jiachen Li, MangTik Chiu, Ali Hassani, Nikita Orlov, Humphrey Shi

However, such panoptic architectures do not truly unify image segmentation because they need to be trained individually on the semantic, instance, or panoptic segmentation to achieve the best performance.

Instance Segmentation Panoptic Segmentation +3

Matting Anything

1 code implementation8 Jun 2023 Jiachen Li, Jitesh Jain, Humphrey Shi

In this paper, we propose the Matting Anything Model (MAM), an efficient and versatile framework for estimating the alpha matte of any instance in an image with flexible and interactive visual or linguistic user prompt guidance.

Image Matting Referring Image Matting

VCoder: Versatile Vision Encoders for Multimodal Large Language Models

1 code implementation21 Dec 2023 Jitesh Jain, Jianwei Yang, Humphrey Shi

Secondly, we leverage the images from COCO and outputs from off-the-shelf vision perception models to create our COCO Segmentation Text (COST) dataset for training and evaluating MLLMs on the object perception task.

Image Captioning Image Generation +4

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