12 citations · 16 across the 10 of their papers we have counts for
8 papers · 1 filter
ALoRE: Efficient Visual Adaptation via Aggregating Low Rank Experts
Sinan Du, Guosheng Zhang, Keyao Wang +7
Parameter-efficient transfer learning (PETL) has become a promising paradigm for adapting large-scale vision foundation models to downstream tasks. Typical methods primarily levera…
Continual SFT Matches Multimodal RLHF with Negative Supervision
Ke Zhu, Yu Wang, Yanpeng Sun +4
Multimodal RLHF usually happens after supervised finetuning (SFT) stage to continually improve vision-language models' (VLMs) comprehension. Conventional wisdom holds its superiori…
FullAnno: A Data Engine for Enhancing Image Comprehension of MLLMs
Jing Hao, Yuxiang Zhao, Song Chen +6
Multimodal Large Language Models (MLLMs) have shown promise in a broad range of vision-language tasks with their strong reasoning and generalization capabilities. However, they hea…
Add-SD: Rational Generation without Manual Reference
Lingfeng Yang, Xinyu Zhang, Xiang Li +7
Diffusion models have exhibited remarkable prowess in visual generalization. Building on this success, we introduce an instruction-based object addition pipeline, named Add-SD, whi…
LaMI-DETR: Open-Vocabulary Detection with Language Model Instruction
Penghui Du, Yu Wang, Yifan Sun +7
Existing methods enhance open-vocabulary object detection by leveraging the robust open-vocabulary recognition capabilities of Vision-Language Models (VLMs), such as CLIP.However,…
OVLW-DETR: Open-Vocabulary Light-Weighted Detection Transformer
Yu Wang, Xiangbo Su, Qiang Chen +6
Open-vocabulary object detection focusing on detecting novel categories guided by natural language. In this report, we propose Open-Vocabulary Light-Weighted Detection Transformer…
LW-DETR: A Transformer Replacement to YOLO for Real-Time Detection
Qiang Chen, Xiangbo Su, Xinyu Zhang +12
In this paper, we present a light-weight detection transformer, LW-DETR, which outperforms YOLOs for real-time object detection. The architecture is a simple stack of a ViT encoder…
VRP-SAM: SAM with Visual Reference Prompt
Yanpeng Sun, Jiahui Chen, Shan Zhang +7
In this paper, we propose a novel Visual Reference Prompt (VRP) encoder that empowers the Segment Anything Model (SAM) to utilize annotated reference images as prompts for segmenta…