30 citations · 61 across the 9 of their papers we have counts for
9 papers
Learning Human Action Recognition Representations Without Real Humans
Howard Zhong, Samarth Mishra, Donghyun Kim +7
Pre-training on massive video datasets has become essential to achieve high action recognition performance on smaller downstream datasets. However, most large-scale video datasets…
GeRA: Label-Efficient Geometrically Regularized Alignment
Dustin Klebe, Tal Shnitzer, Mikhail Yurochkin +2
Pretrained unimodal encoders incorporate rich semantic information into embedding space structures. To be similarly informative, multi-modal encoders typically require massive amou…
TAP: Targeted Prompting for Task Adaptive Generation of Textual Training Instances for Visual Classification
M. Jehanzeb Mirza, Leonid Karlinsky, Wei Lin +3
Vision and Language Models (VLMs), such as CLIP, have enabled visual recognition of a potentially unlimited set of categories described by text prompts. However, for the best visua…
Dense and Aligned Captions (DAC) Promote Compositional Reasoning in VL Models
Sivan Doveh, Assaf Arbelle, Sivan Harary +9
Vision and Language (VL) models offer an effective method for aligning representation spaces of images and text, leading to numerous applications such as cross-modal retrieval, vis…
Comparison of Multilingual Self-Supervised and Weakly-Supervised Speech Pre-Training for Adaptation to Unseen Languages
Andrew Rouditchenko, Sameer Khurana, Samuel Thomas +6
Recent models such as XLS-R and Whisper have made multilingual speech technologies more accessible by pre-training on audio from around 100 spoken languages each. However, there ar…
Constructive Assimilation: Boosting Contrastive Learning Performance through View Generation Strategies
Ligong Han, Seungwook Han, Shivchander Sudalairaj +8
Transformations based on domain expertise (expert transformations), such as random-resized-crop and color-jitter, have proven critical to the success of contrastive learning techni…