Test-time adaptation (TTA) has emerged as a popular paradigm for improving the performance of vision-language models (e.g., CLIP) on downstream tasks. Among existing CLIP-based TTA methods, Test-Time Prompt Tuning (TPT) is a pioneering work that optimizes textual prompts using multiple test-time augmentations and remains a strong baseline to date. In this work, we revisit TPT and reveal that its optimization can be interpreted as implicitly learning from self-generated pseudo labels. Building on this perspective, we propose a unified self-ensembling framework (USE) that ensures consistency between the optimization and inference stages. During optimization, we introduce a simple yet effective self-ensembling (SE) strategy that emphasizes the test image itself over its augmented views adaptively to obtain more reliable pseudo labels. To fully exploit the potential of augmentations, we further apply the same strategy at inference time, unifying the objectives of both stages. Notably, SE can also act as a lightweight optimization-free TTA method. Extensive experiments across multiple datasets demonstrate that SE and USE outperform their counterparts, respectively. Furthermore, SE yields consistent performance gains when integrated with existing TTA methods. The code is available at https://github.com/sirujiang/USE.
The results suggest that competitive MLLM can emerge from alignment alone, reducing multimodal extension to a lightweight projector-training problem that generalizes across modalities and adapts rapidly to each new LLM release.
Xuanru Zhou, Yiwen Shao, Jiahong Li et al.· 1 citation
AutoDavis is introduced, a first-of-its-kind automatic and dynamic evaluation protocol that enables on-demand benchmarking of LVLMs across specific capability dimensions and shows effectiveness and reliability, offering a new paradigm for dynamic benchmarking of multimodal intelligence.
Han Bao, Yue Huang, Yanbo Wang et al.· Proceedings of the 32nd ACM...· 0 citations
SAMPLe (Sharpness-Aware Minimization Prompt Learning), a plug-in sharpness-aware optimizer that enhances prompt generalizability by accounting for loss landscape sharpness, is introduced, establishing itself as a robust, model-agnostic solution for prompt learning.
Text Prompt Boosting (TPB), an AdaBoost-inspired framework that treats each text-prompt-based classifier as a weak learner and sequentially aggregates them into a strong ensemble by explicitly targeting hard, misclassified examples, is proposed.
Seokhee Jin, C. Sung, Sunung Mun et al.· 0 citations
FISA is proposed, a framework for MLLM self-improvement that constructs augmented images from the model's own failure cases that generates visually challenging yet answer-preserving image complications, verifies their utility through self-examination, and applies dual fidelity filtering to avoid semantic distortion.
Chunyang Jiang, Pingping Zhang, Yuzhi Zhao et al.· 0 citations
Test-time adaptation (TTA) has emerged as a key strategy to enhance vision-language models (VLMs) under real-world distribution changes. However, existing methods always face two problems: 1) The fundamental trade-off dilemma: parameter-free TTA retains inference efficiency but fails to correct modality misalignment, while prompt tuning adapts to shifts, incurs high computational costs, and lacks knowledge retention. 2) Discriminative collapse also exists in TTA when faced with fine-grained downstream tasks. To alleviate these two bottlenecks, we introduce Style-aware Contrastive Test-Time Adaptation (SCTTA), a novel framework that jointly addresses modality misalignment and discriminative collapse. Firstly, we introduce Style-aware Embedding Adaptation (SEA), which dynamically refines text embeddings by incorporating domain-specific style attributes, improving alignment between visual and textual modalities. Secondly, we propose Fine-grained Contrastive Adaptation (FCA), which enhances feature separation by enforcing contrastive learning with adaptive prototypes, reducing inter-class feature overlap in fine-grained tasks. In addition, we introduce Dual-Cache Model (DCM), which extends prior unimodal cache model to a multimodal cache for the first time. Eventually, it accumulates adaptation knowledge through a visual-cache (capturing evolving domain styles) and a textual-cache (retaining discriminative semantics), enabling long-term adaptation without additional overhead. Extensive experiments on 15 datasets demonstrate that our approach achieves state-of-the-art performance for both fine-grained and out-of-distribution dataset benchmarks. Furthermore, SCTTA continuously improves as more test samples accumulate, validating its sustainable adaptation capacity. Our code is available at https://github.com/alusi123/SCTTA
Shanshan Wang, .. ALuSi, Xun Yang et al.· IEEE Transactions on Image P...· 0 citations