Search Results for author:
Found 23 papers, 3 papers with code
Learning Video Representations by Transforming Time
Our representations can be learned from data without human annotation and provide a substantial boost to the training of neural networks on small labeled data sets for tasks such as action recognition, which require to accurately distinguish the motion of objects.
FINEMATCH: Aspect-based Fine-grained Image and Text Mismatch Detection and Correction
To address this, we propose FineMatch, a new aspect-based fine-grained text and image matching benchmark, focusing on text and image mismatch detection and correction.
Concept Weaver: Enabling Multi-Concept Fusion in Text-to-Image Models
While there has been significant progress in customizing text-to-image generation models, generating images that combine multiple personalized concepts remains challenging.
No More Shortcuts: Realizing the Potential of Temporal Self-Supervision
To address these issues, we propose 1) a more challenging reformulation of temporal self-supervision as frame-level (rather than clip-level) recognition tasks and 2) an effective augmentation strategy to mitigate shortcuts.
DECORAIT -- DECentralized Opt-in/out Registry for AI Training
We report a prototype of DECORAIT, which explores hierarchical clustering and a combination of on/off-chain storage to create a scalable decentralized registry to trace the provenance of GenAI training data in order to determine training consent and reward creatives who contribute that data.
Meta-Personalizing Vision-Language Models to Find Named Instances in Video
Large-scale vision-language models (VLM) have shown impressive results for language-guided search applications.
EKILA: Synthetic Media Provenance and Attribution for Generative Art
We present EKILA; a decentralized framework that enables creatives to receive recognition and reward for their contributions to generative AI (GenAI).
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.
Audio-Visual Contrastive Learning with Temporal Self-Supervision
We propose a self-supervised learning approach for videos that learns representations of both the RGB frames and the accompanying audio without human supervision.
Spatio-Temporal Crop Aggregation for Video Representation Learning
We propose Spatio-temporal Crop Aggregation for video representation LEarning (SCALE), a novel method that enjoys high scalability at both training and inference time.
SImProv: Scalable Image Provenance Framework for Robust Content Attribution
We present SImProv - a scalable image provenance framework to match a query image back to a trusted database of originals and identify possible manipulations on the query.
Video-ReTime: Learning Temporally Varying Speediness for Time Remapping
Furthermore, we propose an optimization for video re-timing that enables precise control over the target duration and performs more robustly on longer videos than prior methods.
Representation Learning by Detecting Incorrect Location Embeddings
We represent object parts with image tokens and train a ViT to detect which token has been combined with an incorrect positional embedding.
Ranked #91 on
Image Classification
on ObjectNet
(using extra training data)
Learning to Deblur and Rotate Motion-Blurred Faces
Our method handles the complexity of face blur by implicitly learning the geometry and motion of faces through the joint training on three large datasets: FFHQ and 300VW, which are publicly available, and a new Bern Multi-View Face Dataset (BMFD) that we built.
Time-Equivariant Contrastive Video Representation Learning
We introduce a novel self-supervised contrastive learning method to learn representations from unlabelled videos.
VPN: Video Provenance Network for Robust Content Attribution
We present VPN - a content attribution method for recovering provenance information from videos shared online.
Self-Supervised Multi-View Synchronization Learning for 3D Pose Estimation
Current state-of-the-art methods cast monocular 3D human pose estimation as a learning problem by training neural networks on large data sets of images and corresponding skeleton poses.
Video Representation Learning by Recognizing Temporal Transformations
Our representations can be learned from data without human annotation and provide a substantial boost to the training of neural networks on small labeled data sets for tasks such as action recognition, which require to accurately distinguish the motion of objects.
Steering Self-Supervised Feature Learning Beyond Local Pixel Statistics
Based on this criterion, we introduce a novel image transformation that we call limited context inpainting (LCI).
Learning to Have an Ear for Face Super-Resolution
To combine the aural and visual modalities, we propose a method to first build the latent representations of a face from the lone audio track and then from the lone low-resolution image.
On Stabilizing Generative Adversarial Training with Noise
We notice that the distributions of real and generated data should match even when they undergo the same filtering.
Deep Bilevel Learning
Our approach is based on the principles of cross-validation, where a validation set is used to limit the model overfitting.
Self-Supervised Feature Learning by Learning to Spot Artifacts
To generate images with artifacts, we pre-train a high-capacity autoencoder and then we use a damage and repair strategy: First, we freeze the autoencoder and damage the output of the encoder by randomly dropping its entries.
