Search Results for author:
Found 12 papers, 4 papers with code
Message-Enhanced DeGroot Model
The Message-Enhanced DeGroot model, combining the Bounded Brownian Message model with the traditional DeGroot model, quantitatively describes the evolution of agents' opinions under the influence of messages.
VideoPrism: A Foundational Visual Encoder for Video Understanding
We introduce VideoPrism, a general-purpose video encoder that tackles diverse video understanding tasks with a single frozen model.
Herd Behavior in Optimal Investment: A Dual-Agent Approach with Investment Opinion and Rational Decision Decomposition
Furthermore, to quantify the preference for their rational decision over that of the other agent, we introduce the agent's investment opinion.
VideoPoet: A Large Language Model for Zero-Shot Video Generation
We present VideoPoet, a language model capable of synthesizing high-quality video, with matching audio, from a large variety of conditioning signals.
Ranked #3 on
Text-to-Video Generation
on MSR-VTT
PolyMaX: General Dense Prediction with Mask Transformer
Despite this shift, methods based on the per-pixel prediction paradigm still dominate the benchmarks on the other dense prediction tasks that require continuous outputs, such as depth estimation and surface normal prediction.
Ranked #2 on
Surface Normals Estimation
on NYU Depth v2
SANPO: A Scene Understanding, Accessibility, Navigation, Pathfinding, Obstacle Avoidance Dataset
All synthetic sessions and a subset of real sessions have temporally consistent dense panoptic segmentation labels.
VideoGLUE: Video General Understanding Evaluation of Foundation Models
We evaluate existing foundation models video understanding capabilities using a carefully designed experiment protocol consisting of three hallmark tasks (action recognition, temporal localization, and spatiotemporal localization), eight datasets well received by the community, and four adaptation methods tailoring a foundation model (FM) for a downstream task.
Alternating Gradient Descent and Mixture-of-Experts for Integrated Multimodal Perception
We conduct extensive empirical studies and reveal the following key insights: 1) Performing gradient descent updates by alternating on diverse modalities, loss functions, and tasks, with varying input resolutions, efficiently improves the model.
Ranked #1 on
Zero-Shot Action Recognition
on Kinetics
(using extra training data)
Identity Encoder for Personalized Diffusion
Our approach greatly reduces the overhead for personalized image generation and is more applicable in many potential applications.
Taming Encoder for Zero Fine-tuning Image Customization with Text-to-Image Diffusion Models
This paper proposes a method for generating images of customized objects specified by users.
MovieCLIP: Visual Scene Recognition in Movies
Longform media such as movies have complex narrative structures, with events spanning a rich variety of ambient visual scenes.
Spatiotemporal Contrastive Video Representation Learning
Our representations are learned using a contrastive loss, where two augmented clips from the same short video are pulled together in the embedding space, while clips from different videos are pushed away.
Ranked #1 on
Self-Supervised Action Recognition
on Kinetics-600
