Object image retrieval based on self-supervised learning and zero-shot learning

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

SeCo-SBIR is presented, a semantically consistent prompt learning framework that achieves state-of-the-art results on all three standard ZS-SBIR benchmarks across categorical, generalized, and across-dataset settings.

Attention-enhanced vision transformer hashing for hybrid image retrieval

Large-scale image retrieval requires compact representations without substantially sacrificing retrieval accuracy. However, Vision Transformer Hashing (VTS) concatenates all output tokens before hash projection, resulting in a high-dimensional hashing head with considerable model and memory overhead. We replace this to...

G2D: Generative-to-Discriminative Collaborative Inference for Zero-Shot Image Classification

G2D is proposed, a training-free framework that uses a generative VLM to verify CLIP-retrieved candidates against the image and transfers to DCLIP, WaffleCLIP, and CuPL, supporting a practical interface between discriminative proposal and generative visual reasoning.

Few-shot image classification algorithm based on deep learning and feature fusion

Few-shot image classification remains difficult because a model must identify novel classes from only one or a few labeled examples while preserving discriminative local information. Metric-learning methods based on Earth Mover’s Distance (EMD) improve local correspondence by representing an image as a set of regional...

Rethinking Text-Based Image Retrieval in Specific Domain

The Semantic-Aware Fine-Tuning (SAFT) framework is proposed to address semantic compression in specific domains, which incorporates Semantic-Aware Soft-Label Supervision and Intra-modal Structural Distillation to establish a promising paradigm for domain-specific TBIR tasks.

Hybrid CNN-ViT feature embedding for image retrieval with multi-loss optimization