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Found 6 papers, 4 papers with code
LiFT: A Surprisingly Simple Lightweight Feature Transform for Dense ViT Descriptors
We present a simple self-supervised method to enhance the performance of ViT features for dense downstream tasks.
Multi-entity Video Transformers for Fine-Grained Video Representation Learning
In this work, we advance the state-of-the-art for this area by re-examining the design of transformer architectures for video representation learning.
TIJO: Trigger Inversion with Joint Optimization for Defending Multimodal Backdoored Models
We present a Multimodal Backdoor Defense technique TIJO (Trigger Inversion using Joint Optimization).
Teaching Matters: Investigating the Role of Supervision in Vision Transformers
We compare ViTs trained through different methods of supervision, and show that they learn a diverse range of behaviors in terms of their attention, representations, and downstream performance.
Dual-Key Multimodal Backdoors for Visual Question Answering
This is challenging for the attacker as the detector can distort or ignore the visual trigger entirely, which leads to models where backdoors are over-reliant on the language trigger.
APRICOT: A Dataset of Physical Adversarial Attacks on Object Detection
Physical adversarial attacks threaten to fool object detection systems, but reproducible research on the real-world effectiveness of physical patches and how to defend against them requires a publicly available benchmark dataset.
