45 research outputs found
Rethinking Domain Generalization for Face Anti-spoofing: Separability and Alignment
This work studies the generalization issue of face anti-spoofing (FAS) models
on domain gaps, such as image resolution, blurriness and sensor variations.
Most prior works regard domain-specific signals as a negative impact, and apply
metric learning or adversarial losses to remove them from feature
representation. Though learning a domain-invariant feature space is viable for
the training data, we show that the feature shift still exists in an unseen
test domain, which backfires on the generalizability of the classifier. In this
work, instead of constructing a domain-invariant feature space, we encourage
domain separability while aligning the live-to-spoof transition (i.e., the
trajectory from live to spoof) to be the same for all domains. We formulate
this FAS strategy of separability and alignment (SA-FAS) as a problem of
invariant risk minimization (IRM), and learn domain-variant feature
representation but domain-invariant classifier. We demonstrate the
effectiveness of SA-FAS on challenging cross-domain FAS datasets and establish
state-of-the-art performance.Comment: Accepted in CVPR202
ChatGPT is a Knowledgeable but Inexperienced Solver: An Investigation of Commonsense Problem in Large Language Models
Large language models (LLMs) such as ChatGPT and GPT-4 have made significant
progress in NLP. However, their ability to memorize, represent, and leverage
commonsense knowledge has been a well-known pain point for LLMs. It remains
unclear that: (1) Can GPTs effectively answer commonsense questions? (2) Are
GPTs knowledgeable in commonsense? (3) Are GPTs aware of the underlying
commonsense knowledge for answering a specific question? (4) Can GPTs
effectively leverage commonsense for answering questions? To evaluate the above
commonsense problems, we conduct a series of experiments to evaluate ChatGPT's
commonsense abilities, and the experimental results show that: (1) GPTs can
achieve good QA accuracy in commonsense tasks, while they still struggle with
certain types of knowledge. (2) ChatGPT is knowledgeable, and can accurately
generate most of the commonsense knowledge using knowledge prompts. (3) Despite
its knowledge, ChatGPT is an inexperienced commonsense problem solver, which
cannot precisely identify the needed commonsense knowledge for answering a
specific question, i.e., ChatGPT does not precisely know what commonsense
knowledge is required to answer a question. The above findings raise the need
to investigate better mechanisms for utilizing commonsense knowledge in LLMs,
such as instruction following, better commonsense guidance, etc
Mitigating Large Language Model Hallucinations via Autonomous Knowledge Graph-based Retrofitting
Incorporating factual knowledge in knowledge graph is regarded as a promising
approach for mitigating the hallucination of large language models (LLMs).
Existing methods usually only use the user's input to query the knowledge
graph, thus failing to address the factual hallucination generated by LLMs
during its reasoning process. To address this problem, this paper proposes
Knowledge Graph-based Retrofitting (KGR), a new framework that incorporates
LLMs with KGs to mitigate factual hallucination during the reasoning process by
retrofitting the initial draft responses of LLMs based on the factual knowledge
stored in KGs. Specifically, KGR leverages LLMs to extract, select, validate,
and retrofit factual statements within the model-generated responses, which
enables an autonomous knowledge verifying and refining procedure without any
additional manual efforts. Experiments show that KGR can significantly improve
the performance of LLMs on factual QA benchmarks especially when involving
complex reasoning processes, which demonstrates the necessity and effectiveness
of KGR in mitigating hallucination and enhancing the reliability of LLMs