33 research outputs found
Decomposed Soft Prompt Guided Fusion Enhancing for Compositional Zero-Shot Learning
Compositional Zero-Shot Learning (CZSL) aims to recognize novel concepts
formed by known states and objects during training. Existing methods either
learn the combined state-object representation, challenging the generalization
of unseen compositions, or design two classifiers to identify state and object
separately from image features, ignoring the intrinsic relationship between
them. To jointly eliminate the above issues and construct a more robust CZSL
system, we propose a novel framework termed Decomposed Fusion with Soft Prompt
(DFSP)1, by involving vision-language models (VLMs) for unseen composition
recognition. Specifically, DFSP constructs a vector combination of learnable
soft prompts with state and object to establish the joint representation of
them. In addition, a cross-modal decomposed fusion module is designed between
the language and image branches, which decomposes state and object among
language features instead of image features. Notably, being fused with the
decomposed features, the image features can be more expressive for learning the
relationship with states and objects, respectively, to improve the response of
unseen compositions in the pair space, hence narrowing the domain gap between
seen and unseen sets. Experimental results on three challenging benchmarks
demonstrate that our approach significantly outperforms other state-of-the-art
methods by large margins.Comment: 10 pages included reference, conferenc
CNS-Net: Conservative Novelty Synthesizing Network for Malware Recognition in an Open-set Scenario
We study the challenging task of malware recognition on both known and novel
unknown malware families, called malware open-set recognition (MOSR). Previous
works usually assume the malware families are known to the classifier in a
close-set scenario, i.e., testing families are the subset or at most identical
to training families. However, novel unknown malware families frequently emerge
in real-world applications, and as such, require to recognize malware instances
in an open-set scenario, i.e., some unknown families are also included in the
test-set, which has been rarely and non-thoroughly investigated in the
cyber-security domain. One practical solution for MOSR may consider jointly
classifying known and detecting unknown malware families by a single classifier
(e.g., neural network) from the variance of the predicted probability
distribution on known families. However, conventional well-trained classifiers
usually tend to obtain overly high recognition probabilities in the outputs,
especially when the instance feature distributions are similar to each other,
e.g., unknown v.s. known malware families, and thus dramatically degrades the
recognition on novel unknown malware families. In this paper, we propose a
novel model that can conservatively synthesize malware instances to mimic
unknown malware families and support a more robust training of the classifier.
Moreover, we also build a new large-scale malware dataset, named MAL-100, to
fill the gap of lacking large open-set malware benchmark dataset. Experimental
results on two widely used malware datasets and our MAL-100 demonstrate the
effectiveness of our model compared with other representative methods.Comment: 16 pages, 8 figure