1,559 research outputs found
DIRV: Dense Interaction Region Voting for End-to-End Human-Object Interaction Detection
Recent years, human-object interaction (HOI) detection has achieved
impressive advances. However, conventional two-stage methods are usually slow
in inference. On the other hand, existing one-stage methods mainly focus on the
union regions of interactions, which introduce unnecessary visual information
as disturbances to HOI detection. To tackle the problems above, we propose a
novel one-stage HOI detection approach DIRV in this paper, based on a new
concept called interaction region for the HOI problem. Unlike previous methods,
our approach concentrates on the densely sampled interaction regions across
different scales for each human-object pair, so as to capture the subtle visual
features that is most essential to the interaction. Moreover, in order to
compensate for the detection flaws of a single interaction region, we introduce
a novel voting strategy that makes full use of those overlapped interaction
regions in place of conventional Non-Maximal Suppression (NMS). Extensive
experiments on two popular benchmarks: V-COCO and HICO-DET show that our
approach outperforms existing state-of-the-arts by a large margin with the
highest inference speed and lightest network architecture. We achieved 56.1 mAP
on V-COCO without addtional input. Our code is publicly available at:
https://github.com/MVIG-SJTU/DIRVComment: Paper is accepted. Code available at:
https://github.com/MVIG-SJTU/DIR
GIFD: A Generative Gradient Inversion Method with Feature Domain Optimization
Federated Learning (FL) has recently emerged as a promising distributed
machine learning framework to preserve clients' privacy, by allowing multiple
clients to upload the gradients calculated from their local data to a central
server. Recent studies find that the exchanged gradients also take the risk of
privacy leakage, e.g., an attacker can invert the shared gradients and recover
sensitive data against an FL system by leveraging pre-trained generative
adversarial networks (GAN) as prior knowledge. However, performing gradient
inversion attacks in the latent space of the GAN model limits their expression
ability and generalizability. To tackle these challenges, we propose
\textbf{G}radient \textbf{I}nversion over \textbf{F}eature \textbf{D}omains
(GIFD), which disassembles the GAN model and searches the feature domains of
the intermediate layers. Instead of optimizing only over the initial latent
code, we progressively change the optimized layer, from the initial latent
space to intermediate layers closer to the output images. In addition, we
design a regularizer to avoid unreal image generation by adding a small
ball constraint to the searching range. We also extend GIFD to the
out-of-distribution (OOD) setting, which weakens the assumption that the
training sets of GANs and FL tasks obey the same data distribution. Extensive
experiments demonstrate that our method can achieve pixel-level reconstruction
and is superior to the existing methods. Notably, GIFD also shows great
generalizability under different defense strategy settings and batch sizes.Comment: ICCV 202
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