19,387 research outputs found
S-Adapter: Generalizing Vision Transformer for Face Anti-Spoofing with Statistical Tokens
Face Anti-Spoofing (FAS) aims to detect malicious attempts to invade a face
recognition system by presenting spoofed faces. State-of-the-art FAS techniques
predominantly rely on deep learning models but their cross-domain
generalization capabilities are often hindered by the domain shift problem,
which arises due to different distributions between training and testing data.
In this study, we develop a generalized FAS method under the Efficient
Parameter Transfer Learning (EPTL) paradigm, where we adapt the pre-trained
Vision Transformer models for the FAS task. During training, the adapter
modules are inserted into the pre-trained ViT model, and the adapters are
updated while other pre-trained parameters remain fixed. We find the
limitations of previous vanilla adapters in that they are based on linear
layers, which lack a spoofing-aware inductive bias and thus restrict the
cross-domain generalization. To address this limitation and achieve
cross-domain generalized FAS, we propose a novel Statistical Adapter
(S-Adapter) that gathers local discriminative and statistical information from
localized token histograms. To further improve the generalization of the
statistical tokens, we propose a novel Token Style Regularization (TSR), which
aims to reduce domain style variance by regularizing Gram matrices extracted
from tokens across different domains. Our experimental results demonstrate that
our proposed S-Adapter and TSR provide significant benefits in both zero-shot
and few-shot cross-domain testing, outperforming state-of-the-art methods on
several benchmark tests. We will release the source code upon acceptance
Context-Aware Zero-Shot Recognition
We present a novel problem setting in zero-shot learning, zero-shot object
recognition and detection in the context. Contrary to the traditional zero-shot
learning methods, which simply infers unseen categories by transferring
knowledge from the objects belonging to semantically similar seen categories,
we aim to understand the identity of the novel objects in an image surrounded
by the known objects using the inter-object relation prior. Specifically, we
leverage the visual context and the geometric relationships between all pairs
of objects in a single image, and capture the information useful to infer
unseen categories. We integrate our context-aware zero-shot learning framework
into the traditional zero-shot learning techniques seamlessly using a
Conditional Random Field (CRF). The proposed algorithm is evaluated on both
zero-shot region classification and zero-shot detection tasks. The results on
Visual Genome (VG) dataset show that our model significantly boosts performance
with the additional visual context compared to traditional methods
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