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20182021
most citediSAID: A Large-scale Dataset for Instance Segmentation in Aerial Images

175 citations · 219 across the 6 of their papers we have counts for

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cs.CV2021

OW-DETR: Open-world Detection Transformer

Akshita Gupta, Sanath Narayan, K J Joseph +3

Open-world object detection (OWOD) is a challenging computer vision problem, where the task is to detect a known set of object categories while simultaneously identifying unknown o…

cs.CV2021

Discriminative Region-based Multi-Label Zero-Shot Learning

Sanath Narayan, Akshita Gupta, Salman Khan +3

Multi-label zero-shot learning (ZSL) is a more realistic counter-part of standard single-label ZSL since several objects can co-exist in a natural image. However, the occurrence of…

cs.CV2021★ 9 cited

Generative Multi-Label Zero-Shot Learning

Akshita Gupta, Sanath Narayan, Salman Khan +3

Multi-label zero-shot learning strives to classify images into multiple unseen categories for which no data is available during training. The test samples can additionally contain…

cs.CV2020★ 33 cited

Latent Embedding Feedback and Discriminative Features for Zero-Shot Classification

Sanath Narayan, Akshita Gupta, Fahad Shahbaz Khan +2

Zero-shot learning strives to classify unseen categories for which no data is available during training. In the generalized variant, the test samples can further belong to seen or…

cs.CV2019★ 175 cited

iSAID: A Large-scale Dataset for Instance Segmentation in Aerial Images

Syed Waqas Zamir, Aditya Arora, Akshita Gupta +7

Existing Earth Vision datasets are either suitable for semantic segmentation or object detection. In this work, we introduce the first benchmark dataset for instance segmentation i…