8 accepted papers
Pre-trained Vision-Language Models (VLMs) struggle with Zero-Shot Anomaly Detection (ZSAD) due to a critical adaptation gap: they lack the local inductive biases required for dense prediction and employ inflexible feature fusion paradigms. We address these limitations through an Architectural Co-Des…
Vision-Language Models (VLMs) are widely used in tasks like Open-Vocabulary Object Detection and zero-shot Classification, owing to their powerful generalization. However, recent research reveals that VLMs exhibit significant performance instability when tasked with recognizing concepts at varying g
Open-vocabulary detectors (OvOD) inherit tightly coupled cross-modal knowledge from web-scale pretraining, creating privacy, copyright, and compliance risks. Existing machine unlearning methods face geometric entanglement interference in OvOD: forgetting updates inevitably distort preserved knowledg
The past few years have witnessed the immense development of small object detection, which is aimed at detecting size-limited targets in high-resolution images. The prevailing methods focus on extracting fine-grained information by expanding the receptive fields and then generating the potential sma…
In federated learning (FL), multiple clients train a global model by sharing gradients. Since the client data remains locally, federated learning is considered privacy-safe. Although previous studies have demonstrated the feasibility of recovering client data from shared gradients, most studies assu…
Defect detection is a fundamental task in industrial image analysis, crucial for identifying and delineating defect regions. However, existing models, often struggle to learn critical features effectively under conditions of noisy interference. In this study, we introduce the Fine-Grained Hierarchic…
Large image-language models(LLM) have made significant progress in zero-shot anomaly detection(ZSAD), however, the semantic gap between images and text limits their performance in hierarchical learning. In this paper, we propose the hierarchical alignment clip(HieClip) framework, to achieve hierarch…
Medical Visual Question Answering (Med-VQA) interprets complex medical imagery using user instructions for precise diagnostics, yet faces challenges due to diverse, inadequately annotated images. In this paper, we introduce the Universal Instruction-Vision Navigator (Uni-Med) framework for extractin…