Search Results for author: Daniel Glasner

Found 9 papers, 1 papers with code

A Global Approach for Solving Edge-Matching Puzzles

no code implementations21 Sep 2014 Shahar Z. Kovalsky, Daniel Glasner, Ronen Basri

We consider apictorial edge-matching puzzles, in which the goal is to arrange a collection of puzzle pieces with colored edges so that the colors match along the edges of adjacent pieces.

Hot or Not: Exploring Correlations Between Appearance and Temperature

no code implementations ICCV 2015 Daniel Glasner, Pascal Fua, Todd Zickler, Lihi Zelnik-Manor

In this paper we explore interactions between the appearance of an outdoor scene and the ambient temperature.

Toward Perceptually-Consistent Stereo: A Scanline Study

no code implementations ICCV 2017 Jialiang Wang, Daniel Glasner, Todd Zickler

Two types of information exist in a stereo pair: correlation (matching) and decorrelation (half-occlusion).

Understanding Robustness of Transformers for Image Classification

no code implementations ICCV 2021 Srinadh Bhojanapalli, Ayan Chakrabarti, Daniel Glasner, Daliang Li, Thomas Unterthiner, Andreas Veit

We find that when pre-trained with a sufficient amount of data, ViT models are at least as robust as the ResNet counterparts on a broad range of perturbations.

Classification General Classification +1

Less is more: Selecting informative and diverse subsets with balancing constraints

no code implementations26 Apr 2021 Srikumar Ramalingam, Daniel Glasner, Kaushal Patel, Raviteja Vemulapalli, Sadeep Jayasumana, Sanjiv Kumar

Deep learning has yielded extraordinary results in vision and natural language processing, but this achievement comes at a cost.

Image Classification

Balancing Robustness and Sensitivity using Feature Contrastive Learning

no code implementations19 May 2021 Seungyeon Kim, Daniel Glasner, Srikumar Ramalingam, Cho-Jui Hsieh, Kishore Papineni, Sanjiv Kumar

It is generally believed that robust training of extremely large networks is critical to their success in real-world applications.

Contrastive Learning

Less data is more: Selecting informative and diverse subsets with balancing constraints

no code implementations29 Sep 2021 Srikumar Ramalingam, Daniel Glasner, Kaushal Patel, Raviteja Vemulapalli, Sadeep Jayasumana, Sanjiv Kumar

Deep learning has yielded extraordinary results in vision and natural language processing, but this achievement comes at a cost.

Rethinking FID: Towards a Better Evaluation Metric for Image Generation

2 code implementations30 Nov 2023 Sadeep Jayasumana, Srikumar Ramalingam, Andreas Veit, Daniel Glasner, Ayan Chakrabarti, Sanjiv Kumar

It is an unbiased estimator that does not make any assumptions on the probability distribution of the embeddings and is sample efficient.

Image Generation

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