Search Results for author:
Found 8 papers, 4 papers with code
Explaining the Implicit Neural Canvas: Connecting Pixels to Neurons by Tracing their Contributions
We call the aggregate of these contribution maps the Implicit Neural Canvas and we use this concept to demonstrate that the INRs which we study learn to ''see'' the frames they represent in surprising ways.
A Video is Worth 10,000 Words: Training and Benchmarking with Diverse Captions for Better Long Video Retrieval
To provide a more thorough evaluation of the capabilities of long video retrieval systems, we propose a pipeline that leverages state-of-the-art large language models to carefully generate a diverse set of synthetic captions for long videos.
Do text-free diffusion models learn discriminative visual representations?
We find that the intermediate feature maps of the U-Net are diverse, discriminative feature representations.
Diffusion Models Beat GANs on Image Classification
We explore optimal methods for extracting and using these embeddings for classification tasks, demonstrating promising results on the ImageNet classification task.
Beyond Supervised vs. Unsupervised: Representative Benchmarking and Analysis of Image Representation Learning
In this paper, we compare methods using performance-based benchmarks such as linear evaluation, nearest neighbor classification, and clustering for several different datasets, demonstrating the lack of a clear front-runner within the current state-of-the-art.
Rethinking Common Assumptions to Mitigate Racial Bias in Face Recognition Datasets
Many existing works have made great strides towards reducing racial bias in face recognition.
Fair Comparison: Quantifying Variance in Resultsfor Fine-grained Visual Categorization
From this analysis, we both highlight the importance of reporting and comparing methods based on information beyond overall accuracy, as well as point out techniques that mitigate variance in FGVC results.
Machine Translationese: Effects of Algorithmic Bias on Linguistic Complexity in Machine Translation
Recent studies in the field of Machine Translation (MT) and Natural Language Processing (NLP) have shown that existing models amplify biases observed in the training data.
