Adaptive cross-modal alignment via symmetric prompt tuning for few-shot vision–language learning

Cross-modal alignment enhancement for lightweight large vision language models

A Low-Complexity Cross-Modal Alignment via Projection (LCAP) network is proposed, which introduces Projective Token Compression (PTC), which leverages Mish activation and adaptive average pooling to reduce feature redundancy while enhancing discriminative information, and Positional Spatial Enhancement (PSE), which explicitly injects positional cues into the compressed representations and strengthens spatial structure.

Adaptive prompt-guided local cross-modal alignment for zero-shot vision recognition

The AP-LCA approach introduces a novel local cross-modal alignment strategy that utilizes image cropping and similarity-based semantic contribution assessment to precisely map fine-grained descriptions to relevant local image regions.

CoDA: Co-Adaptive Dual-Path Alignment for Vision-Language Models

In CoDA, a new adaptation framework that explicitly disentangles and coordinates cross-modal semantic alignment and intra-modal structural consistency is proposed, and it is shown that CoDA outperforms state-of-the-art parameter-efficient methods, particularly under few-shot learning and distribution-shift scenarios.

MLDA: Test-Time Multi-Level Adaptation with Dynamic Alignment for Compositional Zero-Shot Learning

Tiper: Tokenwise Prompt-Free Learning

Vision-language models (VLMs), such as contrastive language-image pre-training (CLIP), exhibit powerful zero-shot generalization capabilities. Parameter-efficient fine-tuning (PEFT) techniques, notably prompt learning, have been extensively explored to adapt these models to downstream tasks. However, their efficacy remains constrained when transferred to specialized domains like remote sensing. We argue that the bottleneck stems not merely from the limited parameters of prompts, but essentially from the disruption of the input’s original image–text features and the lack of deep cross-modal alignment. In particular, existing methods typically rely on global attention or coarse-grained feature mapping. This inadvertently corrupts the original input representations, thereby impairing the model’s inherent generalization. Furthermore, their isolated unimodal gradient updates fail to bridge the semantic gap inherent in complex remote sensing scenes. To address these challenges, we propose tokenwise prompt-free learning (Tiper), shifting the optimization paradigm from introducing external prompts to precisely recalibrating the critical tokens that govern classification outputs. In particular, Tiper employs a hierarchical learner to supersede global prompts. Crucially, this learner intervenes exclusively on the specific core tokens (i.e., the CLS token in the visual branch and the EOT token in the textual branch), leaving other original input representations unperturbed. This fine-grained strategy effectively balances domain adaptation with the preservation of inherent generalization. Finally, we design the learner as a cross-modal coupled bridge with shared weights, enabling it to synchronously receive gradient feedback from both modalities and fostering profound multimodal collaboration. Extensive experiments validate our method on eight public remote sensing datasets covering diverse scenes and resolutions. In the base-to-new generalization task, Tiper outperforms the strong baseline MaPLe with a significant 3.7% improvement in the harmonic mean (HM). Notably, without relying on any external large-scale domain models, Tiper surpasses the latest domain-specific prompt learning methods (e.g., domain-controlled prompt learning (DCPL), domain prompt learning with quaternion networks (DPLQ)), demonstrating its superior adaptability for remote sensing image scene classification.

SeCo-SBIR: Semantically Consistent Prompt Learning for Zero-Shot Sketch-Based Image Retrieval

Adapting CLIP for zero-shot sketch-based image retrieval (ZS-SBIR) via prompt learning faces a fundamental tension: the model must bridge the sketch-photo domain gap through task-specific adaptation, yet the added flexibility risks overfitting to seen training categories and eroding CLIP's zero-shot generalization. We present SeCo-SBIR, a semantically consistent prompt learning framework that resolves this tension from both sides. First, a text-guided multi-modal prompting strategy routes learnable prompt vectors through CLIP's text encoder and projects the resulting intermediate representations into the visual encoder at every layer via learnable coupling functions. Because the text encoder has already learned robust, abstract category-level semantics from large-scale language supervision, this mechanism injects transferable semantic knowledge directly into the visual pathway - adapting the model to the sketch-photo domain while inherently favoring generalization to unseen classes. Second, a perturbation-based consistency constraint addresses the residual overfitting risk from the learnable coupling functions by aligning the adapted model with a frozen CLIP reference branch using an asymmetric InfoNCE objective - augmented inputs feed the frozen branch while clean inputs feed the trainable branch - anchoring the learned representations to CLIP's generalizable feature space. Together with lightweight adapters and a multi-objective loss combining triplet, NT-Xent, and classification terms, SeCo-SBIR achieves state-of-the-art results on all three standard ZS-SBIR benchmarks across categorical, generalized, and across-dataset settings.