19 citations · 48 across the 15 of their papers we have counts for
15 papers
HoloCount: A Holistic Visual Counting Benchmark for MLLMs
Jinhong Deng, Limeng Qiao, Guanglu Wan
Visual counting is a fundamental pillar of multimodal intelligence, requiring a seamless integration of fine-grained grounding and spatial reasoning. While Multimodal Large Languag…
Mapping Text to Multiplex Graph: Prompt Compression as Lévy Walk-Guided Graph Pruning
Yaxin Gao, Yao Lu, Jinhong Deng +7
Existing prompt compression methods treat text as flat token sequences, failing to capture the distributed nature of important information, which is often spread across multiple lo…
Deformation-based In-Context Learning for Point Cloud Understanding
Chengxing Lin, Jinhong Deng, Yinjie Lei +1
Recent advances in point cloud In-Context Learning (ICL) have demonstrated strong multitask capabilities. Existing approaches typically adopt a Masked Point Modeling (MPM)-based pa…
Dataset Color Quantization: A Training-Oriented Framework for Dataset-Level Compression
Chenyue Yu, Lingao Xiao, Jinhong Deng +2
Large-scale image datasets are fundamental to deep learning, but their high storage demands pose challenges for deployment in resource-constrained environments. While existing appr…
Instance-Free Domain Adaptive Object Detection
Hengfu Yu, Jinhong Deng, Lixin Duan +1
While Domain Adaptive Object Detection (DAOD) has made significant strides, most methods rely on unlabeled target data that is assumed to contain sufficient foreground instances. H…
Object-Centric Vision Token Pruning for Vision Language Models
Guangyuan Li, Rongzhen Zhao, Jinhong Deng +2
In Vision Language Models (VLMs), vision tokens are quantity-heavy yet information-dispersed compared with language tokens, thus consume too much unnecessary computation. Pruning r…
SCOPE: Saliency-Coverage Oriented Token Pruning for Efficient Multimodel LLMs
Jinhong Deng, Wen Li, Joey Tianyi Zhou +1
Multimodal Large Language Models (MLLMs) typically process a large number of visual tokens, leading to considerable computational overhead, even though many of these tokens are red…
Towards Unsupervised Model Selection for Domain Adaptive Object Detection
Hengfu Yu, Jinhong Deng, Wen Li +1
Evaluating the performance of deep models in new scenarios has drawn increasing attention in recent years. However, while it is possible to collect data from new scenarios, the ann…
ResCLIP: Residual Attention for Training-free Dense Vision-language Inference
Yuhang Yang, Jinhong Deng, Wen Li +1
While vision-language models like CLIP have shown remarkable success in open-vocabulary tasks, their application is currently confined to image-level tasks, and they still struggle…
Domain Adaptive Detection of MAVs: A Benchmark and Noise Suppression Network
Yin Zhang, Jinhong Deng, Peidong Liu +2
Visual detection of Micro Air Vehicles (MAVs) has attracted increasing attention in recent years due to its important application in various tasks. The existing methods for MAV det…
Motion and Appearance Adaptation for Cross-Domain Motion Transfer
Borun Xu, Biao Wang, Jinhong Deng +5
Motion transfer aims to transfer the motion of a driving video to a source image. When there are considerable differences between object in the driving video and that in the source…
Revisiting AP Loss for Dense Object Detection: Adaptive Ranking Pair Selection
Dongli Xu, Jinhong Deng, Wen Li
Average precision (AP) loss has recently shown promising performance on the dense object detection task. However,a deep understanding of how AP loss affects the detector from a pai…
Cross-domain Detection Transformer based on Spatial-aware and Semantic-aware Token Alignment
Jinhong Deng, Xiaoyue Zhang, Wen Li +1
Detection transformers like DETR have recently shown promising performance on many object detection tasks, but the generalization ability of those methods is still quite challengin…
Undoing the Damage of Label Shift for Cross-domain Semantic Segmentation
Yahao Liu, Jinhong Deng, Jiale Tao +3
Existing works typically treat cross-domain semantic segmentation (CDSS) as a data distribution mismatch problem and focus on aligning the marginal distribution or conditional dist…
Unbiased Mean Teacher for Cross-domain Object Detection
Jinhong Deng, Wen Li, Yuhua Chen +1
Cross-domain object detection is challenging, because object detection model is often vulnerable to data variance, especially to the considerable domain shift between two distincti…