10 citations · 13 across the 9 of their papers we have counts for
9 papers
Visual-Seeker: Towards Visual-Native Multimodal Agentic Search via Active Visual Reasoning
Zhengbo Zhang, Changtao Miao, Jinbo Su +10
Multimodal large language models (MLLMs) have demonstrated impressive capabilities in many visual tasks, but they often struggle with factual grounding when confronted with complex…
AddressVLM: Cross-view Alignment Tuning for Image Address Localization using Large Vision-Language Models
Shixiong Xu, Chenghao Zhang, Lubin Fan +5
Large visual language models (LVLMs) have demonstrated impressive performance in coarse-grained geo-localization at the country or city level, but they struggle with fine-grained s…
Re-ranking Reasoning Context with Tree Search Makes Large Vision-Language Models Stronger
Qi Yang, Chenghao Zhang, Lubin Fan +3
Recent advancements in Large Vision Language Models (LVLMs) have significantly improved performance in Visual Question Answering (VQA) tasks through multimodal Retrieval-Augmented…
A Survey of Low-shot Vision-Language Model Adaptation via Representer Theorem
Kun Ding, Ying Wang, Gaofeng Meng +1
The advent of pre-trained vision-language foundation models has revolutionized the field of zero/few-shot (i.e., low-shot) image recognition. The key challenge to address under the…
Calibrated Cache Model for Few-Shot Vision-Language Model Adaptation
Kun Ding, Qiang Yu, Haojian Zhang +2
Cache-based approaches stand out as both effective and efficient for adapting vision-language models (VLMs). Nonetheless, the existing cache model overlooks three crucial aspects.…
AddressCLIP: Empowering Vision-Language Models for City-wide Image Address Localization
Shixiong Xu, Chenghao Zhang, Lubin Fan +3
In this study, we introduce a new problem raised by social media and photojournalism, named Image Address Localization (IAL), which aims to predict the readable textual address whe…
Enhancing Visual Continual Learning with Language-Guided Supervision
Bolin Ni, Hongbo Zhao, Chenghao Zhang +4
Continual learning (CL) aims to empower models to learn new tasks without forgetting previously acquired knowledge. Most prior works concentrate on the techniques of architectures,…
Defying Imbalanced Forgetting in Class Incremental Learning
Shixiong Xu, Gaofeng Meng, Xing Nie +3
We observe a high level of imbalance in the accuracy of different classes in the same old task for the first time. This intriguing phenomenon, discovered in replay-based Class Incr…
Alleviating Mode Collapse in GAN via Diversity Penalty Module
Sen Pei, Richard Yi Da Xu, Shiming Xiang +1
The vanilla GAN (Goodfellow et al. 2014) suffers from mode collapse deeply, which usually manifests as that the images generated by generators tend to have a high similarity amongs…