29 citations · 59 across the 7 of their papers we have counts for
7 papers
Do Vision and Language Encoders Represent the World Similarly?
Mayug Maniparambil, Raiymbek Akshulakov, Yasser Abdelaziz Dahou Djilali +4
Aligned text-image encoders such as CLIP have become the de facto model for vision-language tasks. Furthermore, modality-specific encoders achieve impressive performances in their…
DrNet: Dynamic Reversible Dual-Residual Networks for Memory-Efficient Finetuning
Chen Zhao, Shuming Liu, Karttikeya Mangalam +5
Large pretrained models are increasingly crucial in modern computer vision tasks. These models are typically used in downstream tasks by end-to-end finetuning, which is highly memo…
EgoSchema: A Diagnostic Benchmark for Very Long-form Video Language Understanding
Karttikeya Mangalam, Raiymbek Akshulakov, Jitendra Malik
We introduce EgoSchema, a very long-form video question-answering dataset, and benchmark to evaluate long video understanding capabilities of modern vision and language systems. De…
Diffusion Models as Masked Autoencoders
Chen Wei, Karttikeya Mangalam, Po-Yao Huang +7
There has been a longstanding belief that generation can facilitate a true understanding of visual data. In line with this, we revisit generatively pre-training visual representati…
Reversible Vision Transformers
Karttikeya Mangalam, Haoqi Fan, Yanghao Li +4
We present Reversible Vision Transformers, a memory efficient architecture design for visual recognition. By decoupling the GPU memory requirement from the depth of the model, Reve…
Overcoming Mode Collapse with Adaptive Multi Adversarial Training
Karttikeya Mangalam, Rohin Garg
Generative Adversarial Networks (GANs) are a class of generative models used for various applications, but they have been known to suffer from the mode collapse problem, in which s…