75 citations · 303 across the 30 of their papers we have counts for
7 papers · 1 filter
Adaptive Human Trajectory Prediction via Latent Corridors
Neerja Thakkar, Karttikeya Mangalam, Andrea Bajcsy +1
Human trajectory prediction is typically posed as a zero-shot generalization problem: a predictor is learnt on a dataset of human motion in training scenes, and then deployed on un…
Sequential Modeling Enables Scalable Learning for Large Vision Models
Yutong Bai, Xinyang Geng, Karttikeya Mangalam +5
We introduce a novel sequential modeling approach which enables learning a Large Vision Model (LVM) without making use of any linguistic data. To do this, we define a common format…
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…
PaReprop: Fast Parallelized Reversible Backpropagation
Tyler Zhu, Karttikeya Mangalam
The growing size of datasets and deep learning models has made faster and memory-efficient training crucial. Reversible transformers have recently been introduced as an exciting ne…
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…
Speculative Decoding with Big Little Decoder
Sehoon Kim, Karttikeya Mangalam, Suhong Moon +4
The recent emergence of Large Language Models based on the Transformer architecture has enabled dramatic advancements in the field of Natural Language Processing. However, these mo…