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This paper's primary objective is to develop a robust generalist perception model capable of addressing multiple tasks under constraints of computational resources and limited training data.
Learning to recognize objects in egocentric activities
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Language models are few-shot learners
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End-to-end object detection with transformers
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Towards robust monocular depth estimation: Mixing datasets for zero-shot cross-dataset transfer
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From fidelity to perceptual quality: A semi-supervised approach for low-light image enhancement
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Diversedepth: Affine-invariant depth prediction using diverse data
Wei Yin, Xinlong Wang, Chunhua Shen, Yifan Liu, Zhi Tian, Songcen Xu, Changming Sun, and Dou Renyin · 2020
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Emerging properties in self-supervised vision transformers
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Domain adaptation for traffic density estimation
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Omnidata: A scalable pipeline for making multi-task mid-level vision datasets from 3d scans
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Lora: Low-rank adaptation of large language models
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Privacy-preserving portrait matting
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Deep automatic natural image matting
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Learning transferable visual models from natural language supervision
Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al · 2021
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Vision transformers for dense prediction
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Hypersim: A photorealistic synthetic dataset for holistic indoor scene understanding
Mike Roberts, Jason Ramapuram, Anurag Ranjan, Atulit Kumar, Miguel Angel Bautista, Nathan Paczan, Russ Webb, and Joshua M Susskind · 2021
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Towards real-world prohibited item detection: A large-scale x-ray benchmark
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ishape: A first step towards irregular shape instance segmentation
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Building normalizing flows with stochastic interpolants
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Zerowaste dataset: Towards deformable object segmentation in cluttered scenes
Dina Bashkirova, Mohamed Abdelfattah, Ziliang Zhu, James Akl, Fadi Alladkani, Ping Hu, Vitaly Ablavsky, Berk Calli, Sarah Adel Bargal, and Kate Saenko · 2022
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3-d instance segmentation of mvs buildings
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Masked-attention mask transformer for universal image segmentation
Bowen Cheng, Ishan Misra, Alexander G Schwing, Alexander Kirillov, and Rohit Girdhar · 2022
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Night and Day Instance Segmented Park (NDISPark) Dataset: a Collection of Images taken by Day and by Night for Vehicle Detection, Segmentation and Counting in Parking Areas, May 2022
Luca Ciampi, Carlos Santiago, Joao Costeira, Claudio Gennaro, and Giuseppe Amato · 2022
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Semantic segmentation in art paintings
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Rescaling egocentric vision: Collection, pipeline and challenges for epic-kitchens-100
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Epic-kitchens visor benchmark: Video segmentations and object relations
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Instance segmentation for autonomous log grasping in forestry operations
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Classifier-free diffusion guidance, 2022
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Video diffusion models
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Flow matching for generative modeling
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Depthfm: Fast monocular depth estimation with flow matching
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Regiongpt: Towards region understanding vision language model
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Repurposing diffusion-based image generators for monocular depth estimation
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Explora: Parameter-efficient extended pre-training to adapt vision transformers under domain shifts
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Lisa: Reasoning segmentation via large language model
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Matching anything by segmenting anything
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Visual instruction tuning
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Learning temporally consistent video depth from video diffusion priors
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