11 citations · 30 across the 16 of their papers we have counts for
18 papers
Mixture of Style Experts for Diverse Image Stylization
Shihao Zhu, Ziheng Ouyang, Yijia Kang +5
Diffusion-based stylization has advanced significantly, yet existing methods are limited to color-driven transformations, neglecting complex semantics and material details. We intr…
RGBX-R1: Visual Modality Chain-of-Thought Guided Reinforcement Learning for Multimodal Grounding
Jiahe Wu, Bing Cao, Qilong Wang +3
Multimodal Large Language Models (MLLM) are primarily pre-trained on the RGB modality, thereby limiting their performance on other modalities, such as infrared, depth, and event da…
Decoupled Multi-Predictor Optimization for Inference-Efficient Model Tuning
Liwei Luo, Shuaitengyuan Li, Dongwei Ren +3
Recently, remarkable progress has been made in large-scale pre-trained model tuning, and inference efficiency is becoming more crucial for practical deployment. Early exiting in co…
AM-Net: Adaptively Aligned Multi-Scale Moment for Few-Shot Action Recognition
Zilin Gao, Qilong Wang, Bingbing Zhang +2
Thanks to capability to alleviate the cost of large-scale annotation, few-shot action recognition (FSAR) has attracted increased attention of researchers in recent years. Existing…
Constrained Prompt Enhancement for Improving Zero-Shot Generalization of Vision-Language Models
Xiaojie Yin, Qilong Wang, Qinghua Hu
Vision-language models (VLMs) pre-trained on web-scale data exhibit promising zero-shot generalization but often suffer from semantic misalignment due to domain gaps between pre-tr…
DALIP: Distribution Alignment-based Language-Image Pre-Training for Domain-Specific Data
Junjie Wu, Jiangtao Xie, Zhaolin Zhang +4
Recently, Contrastive Language-Image Pre-training (CLIP) has shown promising performance in domain-specific data (e.g., biology), and has attracted increasing research attention. E…
BackMix: Regularizing Open Set Recognition by Removing Underlying Fore-Background Priors
Yu Wang, Junxian Mu, Hongzhi Huang +3
Open set recognition (OSR) requires models to classify known samples while detecting unknown samples for real-world applications. Existing studies show impressive progress using un…
CoE: Chain-of-Explanation via Automatic Visual Concept Circuit Description and Polysemanticity Quantification
Wenlong Yu, Qilong Wang, Chuang Liu +2
Explainability is a critical factor influencing the wide deployment of deep vision models (DVMs). Concept-based post-hoc explanation methods can provide both global and local insig…
: Synonymous Semantic Space for Improving Zero-Shot Generalization of Vision-Language Models
Xiaojie Yin, Qilong Wang, Bing Cao +1
Recently, many studies have been conducted to enhance the zero-shot generalization ability of vision-language models (e.g., CLIP) by addressing the semantic misalignment between im…
Conditional Controllable Image Fusion
Bing Cao, Xingxin Xu, Pengfei Zhu +2
Image fusion aims to integrate complementary information from multiple input images acquired through various sources to synthesize a new fused image. Existing methods usually emplo…
TAMT: Temporal-Aware Model Tuning for Cross-Domain Few-Shot Action Recognition
Yilong Wang, Zilin Gao, Qilong Wang +3
Going beyond few-shot action recognition (FSAR), cross-domain FSAR (CDFSAR) has attracted recent research interests by solving the domain gap lying in source-to-target transfer lea…
Efficient Masked AutoEncoder for Video Object Counting and A Large-Scale Benchmark
Bing Cao, Quanhao Lu, Jiekang Feng +3
The dynamic imbalance of the fore-background is a major challenge in video object counting, which is usually caused by the sparsity of target objects. This remains understudied in…
Fine-Grained Domain Generalization with Feature Structuralization
Wenlong Yu, Dongyue Chen, Qilong Wang +1
Fine-grained domain generalization (FGDG) is a more challenging task than traditional DG tasks due to its small inter-class variations and relatively large intra-class disparities.…
AMU-Tuning: Effective Logit Bias for CLIP-based Few-shot Learning
Yuwei Tang, Zhenyi Lin, Qilong Wang +2
Recently, pre-trained vision-language models (e.g., CLIP) have shown great potential in few-shot learning and attracted a lot of research interest. Although efforts have been made…
Temporal-attentive Covariance Pooling Networks for Video Recognition
Zilin Gao, Qilong Wang, Bingbing Zhang +2
For video recognition task, a global representation summarizing the whole contents of the video snippets plays an important role for the final performance. However, existing video…
Boosting Weakly Supervised Object Detection via Learning Bounding Box Adjusters
Bowen Dong, Zitong Huang, Yuelin Guo +3
Weakly-supervised object detection (WSOD) has emerged as an inspiring recent topic to avoid expensive instance-level object annotations. However, the bounding boxes of most existin…
Detection, Tracking, and Counting Meets Drones in Crowds: A Benchmark
Longyin Wen, Dawei Du, Pengfei Zhu +4
To promote the developments of object detection, tracking and counting algorithms in drone-captured videos, we construct a benchmark with a new drone-captured largescale dataset, n…
SoT: Delving Deeper into Classification Head for Transformer
Jiangtao Xie, Ruiren Zeng, Qilong Wang +2
Transformer models are not only successful in natural language processing (NLP) but also demonstrate high potential in computer vision (CV). Despite great advance, most of works on…