11 accepted papers
Contrastive learning (CL) is a popular learning paradigm that excels in extracting meaningful representations from unlabeled data. Recent studies have shown that CL is highly vulnerable to backdoor attacks. Current defenses against backdoor attacks in CL are primarily reactive and post-training. Tha
Despite their success, large language models (LLMs) suffer from notorious hallucination issue. By introducing external knowledge stored in knowledge graphs (KGs), existing methods use paths as the medium to represent the graph information that send into LLMs. However, paths only contain limited grap…
Post-Training low-bit Quantization (PTQ) is useful to accelerate DNNs due to its high efficiency, the current SOTAs of which mostly adopt feature reconstruction with self-distillation finetuning. However, when bitwidth goes to be extremely low, we find the current reconstruction optimization space i…
Gaze estimation methods encounter significant performance deterioration when being evaluated across different domains, because of the domain gap between the testing and training data. Existing methods try to solve this issue by reducing the deviation of data distribution, however, they ignore the ex…
Test-time adaptation with pre-trained vision-language models shows impressive zero-shot classification abilities, and training-free methods further improve the performance without any optimization burden. However, existing training-free test-time adaptation methods typically rely on entropy criteria…
Benchmark contamination has become a significant concern in the LLM evaluation community. Previous Agents-as-an-Evaluator address this issue by involving agents in the generation of questions. Despite their success, the biases in Agents-as-an-Evaluator methods remain largely unexplored. In this pape…
We consider the line spectral estimation (LSE) from general linear/nonlinear measurements obtained through a generalized linear model (GLM). This paper develops expectation propagation (EP) based LSE (EPLSE) method. The proposed method automatically estimates the model order, noise variance, and can…
"The ability of gaze estimation models to generalize is often significantly hindered by various factors unrelated to gaze, especially when the training dataset is limited. Current strategies aim to address this challenge through different domain generalization techniques, yet they have had limited s…
Supervised fine-tuning (SFT) is a crucial step for large language models (LLMs), enabling them to align with human instructions and enhance their capabilities in downstream tasks. Substantially increasing instruction data is a direct solution to align the model with a broader range of downstream tas…
We consider the problem of recovering an unknown signal from general nonlinear measurements obtained through a generalized linear model (GLM). Based on the unitary transform approximate message passing (UAMP) and expectation propagation, a unitary transform based generalized AMP (GUAMP) algorithm is…
Visual surface inspection is a challenging task due to the highly inconsistent appearance of the target surfaces and the abnormal regions. Most of the state-of-the-art methods are highly dependent on the labelled training samples, which are difficult to collect in practical industrial applications.…