26 citations · 29 across the 11 of their papers we have counts for
13 papers
Linear Scaling Video VLMs for Long Video Understanding
Cristobal Eyzaguirre, Jiajun Wu, Juan Carlos Niebles
Video vision-language models (VLMs) are increasingly used in long-horizon and streaming settings, yet most video encoders still rely on spatiotemporal self-attention, causing compu…
Autoregressive Flow Matching for Motion Prediction
Johnathan Xie, Stefan Stojanov, Cristobal Eyzaguirre +2
Motion prediction has been studied in different contexts with models trained on narrow distributions and applied to downstream tasks in human motion prediction and robotics. Simult…
Taming generative video models for zero-shot optical flow extraction
Seungwoo Kim, Khai Loong Aw, Klemen Kotar +8
Extracting optical flow from videos remains a core computer vision problem. Motivated by the recent success of large general-purpose models, we ask whether frozen self-supervised v…
Understanding Complexity in VideoQA via Visual Program Generation
Cristobal Eyzaguirre, Igor Vasiljevic, Achal Dave +5
We propose a data-driven approach to analyzing query complexity in Video Question Answering (VideoQA). Previous efforts in benchmark design have relied on human expertise to design…
T*: Re-thinking Temporal Search for Long-Form Video Understanding
Jinhui Ye, Zihan Wang, Haosen Sun +9
Efficiently understanding long-form videos remains a significant challenge in computer vision. In this work, we revisit temporal search paradigms for long-form video understanding…
Streaming Detection of Queried Event Start
Cristobal Eyzaguirre, Eric Tang, Shyamal Buch +3
Robotics, autonomous driving, augmented reality, and many embodied computer vision applications must quickly react to user-defined events unfolding in real time. We address this se…
IKEA Manuals at Work: 4D Grounding of Assembly Instructions on Internet Videos
Yunong Liu, Cristobal Eyzaguirre, Manling Li +6
Shape assembly is a ubiquitous task in daily life, integral for constructing complex 3D structures like IKEA furniture. While significant progress has been made in developing auton…
HourVideo: 1-Hour Video-Language Understanding
Keshigeyan Chandrasegaran, Agrim Gupta, Lea M. Hadzic +7
We present HourVideo, a benchmark dataset for hour-long video-language understanding. Our dataset consists of a novel task suite comprising summarization, perception (recall, track…
Failures to Find Transferable Image Jailbreaks Between Vision-Language Models
Rylan Schaeffer, Dan Valentine, Luke Bailey +12
The integration of new modalities into frontier AI systems offers exciting capabilities, but also increases the possibility such systems can be adversarially manipulated in undesir…
Self-Supervised Learning of Representations for Space Generates Multi-Modular Grid Cells
Rylan Schaeffer, Mikail Khona, Tzuhsuan Ma +3
To solve the spatial problems of mapping, localization and navigation, the mammalian lineage has developed striking spatial representations. One important spatial representation is…
Revisiting the "Video" in Video-Language Understanding
Shyamal Buch, Cristóbal Eyzaguirre, Adrien Gaidon +3
What makes a video task uniquely suited for videos, beyond what can be understood from a single image? Building on recent progress in self-supervised image-language models, we revi…
DACT-BERT: Differentiable Adaptive Computation Time for an Efficient BERT Inference
Cristóbal Eyzaguirre, Felipe del Río, Vladimir Araujo +1
Large-scale pre-trained language models have shown remarkable results in diverse NLP applications. Unfortunately, these performance gains have been accompanied by a significant inc…
Differentiable Adaptive Computation Time for Visual Reasoning
Cristobal Eyzaguirre, Alvaro Soto
This paper presents a novel attention-based algorithm for achieving adaptive computation called DACT, which, unlike existing ones, is end-to-end differentiable. Our method can be u…