Search Results for author:
Found 15 papers, 1 papers with code
LEMaRT: Label-Efficient Masked Region Transform for Image Harmonization
We present a simple yet effective self-supervised pre-training method for image harmonization which can leverage large-scale unannotated image datasets.
Selective Structured State-Spaces for Long-Form Video Understanding
To address this limitation, we present a novel Selective S4 (i. e., S5) model that employs a lightweight mask generator to adaptively select informative image tokens resulting in more efficient and accurate modeling of long-term spatiotemporal dependencies in videos.
Ranked #2 on
Video Classification
on Breakfast
Scalable Temporal Localization of Sensitive Activities in Movies and TV Episodes
To help customers make better-informed viewing choices, video-streaming services try to moderate their content and provide more visibility into which portions of their movies and TV episodes contain age-appropriate material (e. g., nudity, sex, violence, or drug-use).
Robust Cross-Modal Representation Learning with Progressive Self-Distillation
The learning objective of vision-language approach of CLIP does not effectively account for the noisy many-to-many correspondences found in web-harvested image captioning datasets, which contributes to its compute and data inefficiency.
Ranked #98 on
Image Classification
on ObjectNet
(using extra training data)
Depth-Guided Sparse Structure-from-Motion for Movies and TV Shows
We demonstrate that our approach: (a) significantly improves the quality of 3-D reconstruction for our small-parallax setting, (b) does not cause any degradation for data with large-parallax, and (c) maintains the generalizability and scalability of geometry-based sparse SfM.
Movies2Scenes: Using Movie Metadata to Learn Scene Representation
However, labeling individual scenes is a time-consuming process.
Shot Contrastive Self-Supervised Learning for Scene Boundary Detection
To assess the effectiveness of ShotCoL on novel applications of scene boundary detection, we take on the problem of finding timestamps in movies and TV episodes where video-ads can be inserted while offering a minimally disruptive viewing experience.
Geospatial Correspondences for Multimodal Registration
The growing availability of very high resolution (<1 m/pixel) satellite and aerial images has opened up unprecedented opportunities to monitor and analyze the evolution of land-cover and land-use across the world.
Large-Scale Damage Detection Using Satellite Imagery
Satellite imagery is a valuable source of information for assessing damages in distressed areas undergoing a calamity, such as an earthquake or an armed conflict.
Hardware Compliant Approximate Image Codes
In recent years, several feature encoding schemes for the bags-of-visual-words model have been proposed.
Fast Approximate Matching of Cell-Phone Videos for Robust Background Subtraction
We identify a novel instance of the background subtraction problem that focuses on extracting near-field foreground objects captured using handheld cameras.
piCholesky: Polynomial Interpolation of Multiple Cholesky Factors for Efficient Approximate Cross-Validation
The dominant cost in solving least-square problems using Newton's method is often that of factorizing the Hessian matrix over multiple values of the regularization parameter ($\lambda$).
Compact Random Feature Maps
Kernel approximation using randomized feature maps has recently gained a lot of interest.
Large-Scale Video Summarization Using Web-Image Priors
Given the enormous growth in user-generated videos, it is becoming increasingly important to be able to navigate them efficiently.
Dense Non-rigid Point-Matching Using Random Projections
We present a robust and efficient technique for matching dense sets of points undergoing non-rigid spatial transformations.
