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20212026
most citedHubs and Hyperspheres: Reducing Hubness and Improving Transductive Few-shot Learning with Hyperspherical Embeddings

2 citations · 2 across the 6 of their papers we have counts for

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cs.CV2026

Not Another Text Benchmark: Putting the "Visual" Back in Visual Question Answering for Large Video Models

Rwiddhi Chakraborty, Yinong, Yinong Oliver Wang +9

Large video models have exhibited impressive performance on a wide range of visual question answering tasks, owing to the rise of powerful, pretrained text and vision encoders. The…

cs.CV2026

Suppressing Non-Semantic Noise in Masked Image Modeling Representations

Martine Hjelkrem-Tan, Marius Aasan, Rwiddhi Chakraborty +3

Masked Image Modeling (MIM) has become a ubiquitous self-supervised vision paradigm. In this work, we show that MIM objectives cause the learned representations to retain non-seman…

cs.CV2024

Visual Data Diagnosis and Debiasing with Concept Graphs

Rwiddhi Chakraborty, Yinong Wang, Jialu Gao +3

The widespread success of deep learning models today is owed to the curation of extensive datasets significant in size and complexity. However, such models frequently pick up inher…

cs.CV2024

ExMap: Leveraging Explainability Heatmaps for Unsupervised Group Robustness to Spurious Correlations

Rwiddhi Chakraborty, Adrian Sletten, Michael Kampffmeyer

Group robustness strategies aim to mitigate learned biases in deep learning models that arise from spurious correlations present in their training datasets. However, most existing…

cs.CV2023★ 2 cited

Hubs and Hyperspheres: Reducing Hubness and Improving Transductive Few-shot Learning with Hyperspherical Embeddings

Daniel J. Trosten, Rwiddhi Chakraborty, Sigurd Løkse +3

Distance-based classification is frequently used in transductive few-shot learning (FSL). However, due to the high-dimensionality of image representations, FSL classifiers are pron…