9 accepted papers
Large Vision-Language Models (LVLMs) excel in visual understanding and reasoning, but the excessive visual tokens lead to high inference costs. Although recent token reduction methods mitigate this issue, they mainly target single-turn Visual Question Answering (VQA), leaving the more practical mult
Vertical federated learning (VFL) is a collaborative learning scheme where clients share some overlapping samples but have different feature spaces. Existing VFL schemes are restricted in model performance and deployment feasibility due to the scarcity of overlapping labeled samples and high communi…
The prevalent use of commercial and open-source diffusion models (DMs) for text-to-image generation prompts risk mitigation to prevent undesired behaviors. Existing concept erasing methods in academia are all based on full parameter or specification-based fine-tuning from which we observe the follow…
The superior performances of Vision Transformers often come with higher training costs. Compared to their CNN counterpart, Transformer models are hungry for large-scale data and their training schedules are usually prolonged. This sets great restrictions on training Transformers with limited resourc…
Multi-label zero-shot learning (ML-ZSL), with the difficulty of both multi-label learning and zero-shot learning, aims to recognize various unseen objects that are not observed during training. Previous methods mainly use a single directional visual-semantic mapping to associate the visual and seman…
Existing open vocabulary object detection (OVD) works expand the object detector toward open categories by replacing the classifier with the category text embeddings and optimizing the region-text alignment on data of the base categories. However, both the class-agnostic proposal generator and the c…
Knowledge distillation aims to learn a small student model by leveraging knowledge from a larger teacher model. The gap between these heterogeneous models hinder their knowledge transfer and it would be more challenging when the teacher model is from another task. Previous methods view the teacher m…
In visual domain adaptation (DA), separating the domain-specific characteristics from the domain-invariant representations is an ill-posed problem. Existing methods apply different kinds of priors or directly minimize the domain discrepancy to address this problem, which lack flexibility in handling…
Intellectual property protection(IPP) have received more and more attention recently due to the development of the global e-commerce platforms. brand recognition plays a significant role in IPP. Recent studies for brand recognition and detection are based on small-scale datasets that are not compreh…