86 citations · 187 across the 10 of their papers we have counts for
10 papers
Beyond Trial-and-Error: Agentic Optimization for Image-to-Video Adherence
Aman Tyagi, Hemanth Boinpally, Jonathan Chen +2
Modern black-box Image-to-Video (I2V) models offer powerful capabilities in automated content creation, yet their lack of fine-grained control and reliability presents significant…
Cropper: Vision-Language Model for Image Cropping through In-Context Learning
Seung Hyun Lee, Jijun Jiang, Yiran Xu +10
The goal of image cropping is to identify visually appealing crops in an image. Conventional methods are trained on specific datasets and fail to adapt to new requirements. Recent…
Floors are Flat: Leveraging Semantics for Real-Time Surface Normal Prediction
Steven Hickson, Karthik Raveendran, Alireza Fathi +2
We propose 4 insights that help to significantly improve the performance of deep learning models that predict surface normals and semantic labels from a single RGB image. These ins…
Efficient Hierarchical Graph-Based Segmentation of RGBD Videos
Steven Hickson, Stan Birchfield, Irfan Essa +1
We present an efficient and scalable algorithm for segmenting 3D RGBD point clouds by combining depth, color, and temporal information using a multistage, hierarchical graph-based…
Let's Dance: Learning From Online Dance Videos
Daniel Castro, Steven Hickson, Patsorn Sangkloy +4
In recent years, deep neural network approaches have naturally extended to the video domain, in their simplest case by aggregating per-frame classifications as a baseline for actio…
Object category learning and retrieval with weak supervision
Steven Hickson, Anelia Angelova, Irfan Essa +1
We consider the problem of retrieving objects from image data and learning to classify them into meaningful semantic categories with minimal supervision. To that end, we propose a…