Out-of-distribution (OOD) detection is critical for ensuring the reliability of deep learning models in open-world applications. While post-hoc methods are favored for their efficiency and ease of deployment, existing approaches often underexploit the rich information embedded in the model’s logits space. In this paper, we propose LogitGap, a novel post-hoc OOD detection method that explicitly exploits the relationship between the maximum logit and the remaining logits to enhance the separability between in-distribution (ID) and OOD samples. To further improve its effectiveness, we refine LogitGap by focusing on a more compact and informative subset of the logit space. Specifically, we introduce a training-free strategy that automatically identifies the most informative logits for scoring. We provide both theoretical analysis and empirical evidence to validate the effectiveness of our approach. Extensive experiments on both vision-language and vision-only models demonstrate that LogitGap consistently achieves state-of-the-art performance across diverse OOD detection scenarios and benchmarks.

Contrastive Language-Image Pre-training (CLIP) models exhibit impressive zero-shot performance across various downstream cross-modal tasks by simply computing the dot product between image and text features. CLIP is pre-trained on large-scale image-text pairs using the InfoNCE loss, which maximizes the cosine similarity of positive image-text pairs while minimizing the similarity of negative pairs. However, an objective mismatch exists between the downstream usage and the pre-training phase, as the inference phase fails to exploit information from negative samples. Intuitively, since the CLIP model has been optimized based on the InfoNCE loss, the downstream usage should also be in alignment. In this paper, we start from analyzing the InfoNCE loss and derive its upper bound. Our derivation reveals that the dot-product operation serves a zero-order approximation of this upper bound, while a centralization operation represents a first-order approximation. To address the objective mismatch problem, we propose a novel method, Inference Calibration (IC), which leverages the first-order and second-order moments of data distribution to calibrate features for zero-shot and few-shot scenarios. Experiments on various cross-modal tasks demonstrate the effectiveness of IC in both zero-shot and few-shot scenarios over dot-product operation and other comparative methods. • The downstream usage of CLIP model mismatches its pre-training objective. • Previous popular inference methods are the approximation of pre-training objective. • Proposed method mitigate objective mismatch problem under both zero-shot and few-shot settings.

In this paper, we propose a novel modular network framework, called Scalable Modular Network (SMN), which enables adaptive learning capability and supports integration of new modules after pre-training for better adaptation. This adaptive capability comes from a novel design of router within SMN, named agreement router, which selects and composes different specialist modules through an iterative message passing process. The agreement router iteratively computes the agreements among a set of input and outputs of all modules to allocate inputs to specific module. During the iterative routing, messages of modules are passed to each other, which improves the module selection process with consideration of both local interactions (between a single module and input) and global interactions involving multiple other modules. To validate our contributions, we conduct experiments on two problems: a toy min-max game and few-shot image classification task. Our experimental results demonstrate that SMN can generalize to new distributions and exhibit sample-efficient adaptation to new tasks. Furthermore, SMN can achieve a better adaptation capability when new modules are introduced after pre-training. Our code is available at https://github.com/hu-my/ScalableModularNetwork.

Pre-trained vision-language models (e.g., CLIP) have shown powerful zero-shot transfer capabilities. But they still struggle with domain shifts and typically require labeled data to adapt to downstream tasks, which could be costly. In this work, we aim to leverage unlabeled data that naturally spans multiple domains to enhance the transferability of vision-language models. Under this unsupervised multi-domain setting, we have identified inherent model bias within CLIP, notably in its visual and text encoders. Specifically, we observe that CLIP’s visual encoder tends to prioritize encoding domain over discriminative category information, meanwhile its text encoder exhibits a preference for domain-relevant classes. To mitigate this model bias, we propose a training-free and label-free feature calibration method, Unsupervised Multi-domain Feature Calibration (UMFC). UMFC estimates image-level biases from domain-specific features and text-level biases from the direction of domain transition. These biases are subsequently subtracted from original image and text features separately, to render them domain-invariant. We evaluate our method on multiple settings including transductive learning and test-time adaptation. Extensive experiments show that our method outperforms CLIP and performs on par with the state-of-the-arts that need additional annotations or optimization. Our code is available at https://github.com/GIT-LJc/UMFC.

Few-shot learning (FSL) aims to learn novel tasks with very few labeled samples by leveraging experience from \emph{related} training tasks. In this paper, we try to understand FSL by exploring two key questions: (1) How to quantify the relationship between \emph{ training} and \emph{novel} tasks? (2) How does the relationship affect the \emph{adaptation difficulty} on novel tasks for different models? To answer the first question, we propose Task Attribute Distance (TAD) as a metric to quantify the task relatedness via attributes. Unlike other metrics, TAD is independent of models, making it applicable to different FSL models. To address the second question, we utilize TAD metric to establish a theoretical connection between task relatedness and task adaptation difficulty. By deriving the generalization error bound on a novel task, we discover how TAD measures the adaptation difficulty on novel tasks for different models. To validate our theoretical results, we conduct experiments on three benchmarks. Our experimental results confirm that TAD metric effectively quantifies the task relatedness and reflects the adaptation difficulty on novel tasks for various FSL methods, even if some of them do not learn attributes explicitly or human-annotated attributes are not provided. Our code is available at \href{https://github.com/hu-my/TaskAttributeDistance}{https://github.com/hu-my/TaskAttributeDistance}.

Learning graph structure for graph neural networks (GNNs) is crucial to facilitate the GNN-based downstream learning tasks. It is challenging due to the non-differentiable discrete graph structure and lack of ground-truth. In this paper, we address these problems and propose a novel graph structure learning framework for GNNs. Firstly, we directly model the continuous graph structure with dual-normalization, which implicitly imposes sparse constraint and reduces the influence of noisy edges. Secondly, we formulate the whole training process as a bilevel programming problem, where the inner objective is to optimize the GNNs given learned graphs, while the outer objective is to optimize the graph structure to minimize the generalization error of downstream task. Moreover, for bilevel optimization, we propose an improved Neumann-IFT algorithm to obtain an approximate solution, which is more stable and accurate than existing optimization methods. Besides, it makes the bilevel optimization process memory-efficient and scalable to large graphs. Experiments on node classification and scene graph generation show that our method can outperform related methods, especially with noisy graphs.