115 citations · 331 across the 37 of their papers we have counts for
8 papers · 1 filter
From Variance to Veracity: Unbundling and Mitigating Gradient Variance in Differentiable Bundle Adjustment Layers
Swaminathan Gurumurthy, Karnik Ram, Bingqing Chen +2
Various pose estimation and tracking problems in robotics can be decomposed into a correspondence estimation problem (often computed using a deep network) followed by a weighted le…
DART: Implicit Doppler Tomography for Radar Novel View Synthesis
Tianshu Huang, John Miller, Akarsh Prabhakara +4
Simulation is an invaluable tool for radio-frequency system designers that enables rapid prototyping of various algorithms for imaging, target detection, classification, and tracki…
Single Image Backdoor Inversion via Robust Smoothed Classifiers
Mingjie Sun, J. Zico Kolter
Backdoor inversion, a central step in many backdoor defenses, is a reverse-engineering process to recover the hidden backdoor trigger inserted into a machine learning model. Existi…
Finetune like you pretrain: Improved finetuning of zero-shot vision models
Sachin Goyal, Ananya Kumar, Sankalp Garg +2
Finetuning image-text models such as CLIP achieves state-of-the-art accuracies on a variety of benchmarks. However, recent works like WiseFT (Wortsman et al., 2021) and LP-FT (Kuma…
Unified Fully and Timestamp Supervised Temporal Action Segmentation via Sequence to Sequence Translation
Nadine Behrmann, S. Alireza Golestaneh, Zico Kolter +2
This paper introduces a unified framework for video action segmentation via sequence to sequence (seq2seq) translation in a fully and timestamp supervised setup. In contrast to cur…
Improving Adversarial Robustness via Joint Classification and Multiple Explicit Detection Classes
Sina Baharlouei, Fatemeh Sheikholeslami, Meisam Razaviyayn +1
This work concerns the development of deep networks that are certifiably robust to adversarial attacks. Joint robust classification-detection was recently introduced as a certified…
Defending Multimodal Fusion Models against Single-Source Adversaries
Karren Yang, Wan-Yi Lin, Manash Barman +2
Beyond achieving high performance across many vision tasks, multimodal models are expected to be robust to single-source faults due to the availability of redundant information bet…
On Physical Adversarial Patches for Object Detection
Mark Lee, Zico Kolter
In this paper, we demonstrate a physical adversarial patch attack against object detectors, notably the YOLOv3 detector. Unlike previous work on physical object detection attacks,…