Search Results for author:
Found 11 papers, 2 papers with code
Rotationally-Temporally Consistent Novel View Synthesis of Human Performance Video
Second, we introduce a novel loss to explicitly enforce consistency across generated views both in space and in time.
Token-Level Adversarial Prompt Detection Based on Perplexity Measures and Contextual Information
Our work aims to address this concern by introducing a novel approach to detecting adversarial prompts at a token level, leveraging the LLM's capability to predict the next token's probability.
VADER: Video Alignment Differencing and Retrieval
We propose VADER, a spatio-temporal matching, alignment, and change summarization method to help fight misinformation spread via manipulated videos.
Privacy Aware Experiments without Cookies
We propose a two-stage experimental design, where the two brands only need to agree on high-level aggregate parameters of the experiment to test the alternate experiences.
Video Manipulations Beyond Faces: A Dataset with Human-Machine Analysis
To this end, we present VideoSham, a dataset consisting of 826 videos (413 real and 413 manipulated).
Show Me What I Like: Detecting User-Specific Video Highlights Using Content-Based Multi-Head Attention
We propose a method to detect individualized highlights for users on given target videos based on their preferred highlight clips marked on previous videos they have watched.
HighlightMe: Detecting Highlights from Human-Centric Videos
We train our network to map the activity- and interaction-based latent structural representations of the different modalities to per-frame highlight scores based on the representativeness of the frames.
Optimal Bidding Strategy without Exploration in Real-time Bidding
Earlier works on optimal bidding strategy apply model-based batch reinforcement learning methods which can not generalize to unknown budget and time constraint.
Scalable Bid Landscape Forecasting in Real-time Bidding
The highest bidding advertiser wins but pays only the second-highest bid (known as the winning price).
Deep Relational Factorization Machines
Factorization Machines (FMs) is an important supervised learning approach due to its unique ability to capture feature interactions when dealing with high-dimensional sparse data.
From Thumbnails to Summaries - A single Deep Neural Network to Rule Them All
The encoder selects a subset from the input video while the decoder seeks to reconstruct the video from the selection.
