21 citations · 34 across the 7 of their papers we have counts for
10 papers
Inside the Loop: A Mechanistic Study of Weight-Tied Transformers on Depth-Bound Algorithmic Tasks
Magnus Sesodia, Juil Sock, Philip Torr +1
Solving algorithmic tasks with many sequential steps typically requires scaling up transformer depth or chain-of-thought length, incurring unfavorable parameter and inference-compu…
Towards Better Exploration in Sequential Test-Time Scaling
Joseph Rance, Fabio Pizzati, Juil Sock +4
Test-time scaling improves language model reasoning by spending additional compute at inference. However, both classes of existing methods often fail to continue improving over lon…
Towards Reliable Identification of Diffusion-based Image Manipulations
Alex Costanzino, Woody Bayliss, Juil Sock +5
Changing facial expressions, gestures, or background details may dramatically alter the meaning conveyed by an image. Notably, recent advances in diffusion models greatly improve t…
Toward Robust Real-World Audio Deepfake Detection: Closing the Explainability Gap
Georgia Channing, Juil Sock, Ronald Clark +2
The rapid proliferation of AI-manipulated or generated audio deepfakes poses serious challenges to media integrity and election security. Current AI-driven detection solutions lack…
MAD-Sherlock: Multi-Agent Debate for Visual Misinformation Detection
Kumud Lakara, Georgia Channing, Christian Rupprecht +4
One of the most challenging forms of misinformation involves pairing images with misleading text to create false narratives. Existing AI-driven detection systems often require doma…
Introducing Pose Consistency and Warp-Alignment for Self-Supervised 6D Object Pose Estimation in Color Images
Juil Sock, Guillermo Garcia-Hernando, Anil Armagan +1
Most successful approaches to estimate the 6D pose of an object typically train a neural network by supervising the learning with annotated poses in real world images. These annota…
A Review on Object Pose Recovery: from 3D Bounding Box Detectors to Full 6D Pose Estimators
Caner Sahin, Guillermo Garcia-Hernando, Juil Sock +1
Object pose recovery has gained increasing attention in the computer vision field as it has become an important problem in rapidly evolving technological areas related to autonomou…
Active 6D Multi-Object Pose Estimation in Cluttered Scenarios with Deep Reinforcement Learning
Juil Sock, Guillermo Garcia-Hernando, Tae-Kyun Kim
In this work, we explore how a strategic selection of camera movements can facilitate the task of 6D multi-object pose estimation in cluttered scenarios while respecting real-world…
Instance- and Category-level 6D Object Pose Estimation
Caner Sahin, Guillermo Garcia-Hernando, Juil Sock +1
6D object pose estimation is an important task that determines the 3D position and 3D rotation of an object in camera-centred coordinates. By utilizing such a task, one can propose…
Multi-Task Deep Networks for Depth-Based 6D Object Pose and Joint Registration in Crowd Scenarios
Juil Sock, Kwang In Kim, Caner Sahin +1
In bin-picking scenarios, multiple instances of an object of interest are stacked in a pile randomly, and hence, the instances are inherently subjected to the challenges: severe oc…