7 citations · 20 across the 19 of their papers we have counts for
19 papers
MuRA: Multi-Rank Adaptation for Efficient and Effective Test-Time Vision-Language Generalization
Gengyuan Liu, Nanzhou Wang, Chang Liu +5
Vision-language models exhibit remarkable zero-shot capabilities but suffer significant performance degradation under distribution shifts. While test-time adaptation (TTA) via Low-…
RT-DETRv4: Painlessly Furthering Real-Time Object Detection with Vision Foundation Models
Zijun Liao, Yian Zhao, Xin Shan +5
Real-time object detection has achieved substantial progress through meticulously designed architectures and optimization strategies. However, the pursuit of high-speed inference v…
FaceChain-SuDe: Building Derived Class to Inherit Category Attributes for One-shot Subject-Driven Generation
Pengchong Qiao, Lei Shang, Chang Liu +3
Subject-driven generation has garnered significant interest recently due to its ability to personalize text-to-image generation. Typical works focus on learning the new subject's p…
Towards Real-World Burst Image Super-Resolution: Benchmark and Method
Pengxu Wei, Yujing Sun, Xingbei Guo +4
Despite substantial advances, single-image super-resolution (SISR) is always in a dilemma to reconstruct high-quality images with limited information from one input image, especial…
U-RED: Unsupervised 3D Shape Retrieval and Deformation for Partial Point Clouds
Yan Di, Chenyangguang Zhang, Ruida Zhang +6
In this paper, we propose U-RED, an Unsupervised shape REtrieval and Deformation pipeline that takes an arbitrary object observation as input, typically captured by RGB images or s…
WiCo: Win-win Cooperation of Bottom-up and Top-down Referring Image Segmentation
Zesen Cheng, Peng Jin, Hao Li +5
The top-down and bottom-up methods are two mainstreams of referring segmentation, while both methods have their own intrinsic weaknesses. Top-down methods are chiefly disturbed by…
Backdoor Attacks Against Incremental Learners: An Empirical Evaluation Study
Yiqi Zhong, Xianming Liu, Deming Zhai +2
Large amounts of incremental learning algorithms have been proposed to alleviate the catastrophic forgetting issue arises while dealing with sequential data on a time series. Howev…
Video-Text as Game Players: Hierarchical Banzhaf Interaction for Cross-Modal Representation Learning
Peng Jin, Jinfa Huang, Pengfei Xiong +5
Contrastive learning-based video-language representation learning approaches, e.g., CLIP, have achieved outstanding performance, which pursue semantic interaction upon pre-defined…
Multi-granularity Interaction Simulation for Unsupervised Interactive Segmentation
Kehan Li, Yian Zhao, Zhennan Wang +6
Interactive segmentation enables users to segment as needed by providing cues of objects, which introduces human-computer interaction for many fields, such as image editing and med…
Parallel Vertex Diffusion for Unified Visual Grounding
Zesen Cheng, Kehan Li, Peng Jin +4
Unified visual grounding pursues a simple and generic technical route to leverage multi-task data with less task-specific design. The most advanced methods typically present boxes…
DiffusionRet: Generative Text-Video Retrieval with Diffusion Model
Peng Jin, Hao Li, Zesen Cheng +5
Existing text-video retrieval solutions are, in essence, discriminant models focused on maximizing the conditional likelihood, i.e., p(candidates|query). While straightforward, thi…
UATVR: Uncertainty-Adaptive Text-Video Retrieval
Bo Fang, Wenhao Wu, Chang Liu +6
With the explosive growth of web videos and emerging large-scale vision-language pre-training models, e.g., CLIP, retrieving videos of interest with text instructions has attracted…
Disjoint Masking with Joint Distillation for Efficient Masked Image Modeling
Xin Ma, Chang Liu, Chunyu Xie +3
Masked image modeling (MIM) has shown great promise for self-supervised learning (SSL) yet been criticized for learning inefficiency. We believe the insufficient utilization of tra…
Proposal Distribution Calibration for Few-Shot Object Detection
Bohao Li, Chang Liu, Mengnan Shi +3
Adapting object detectors learned with sufficient supervision to novel classes under low data regimes is charming yet challenging. In few-shot object detection (FSOD), the two-step…
Fuzzy Positive Learning for Semi-supervised Semantic Segmentation
Pengchong Qiao, Zhidan Wei, Yu Wang +6
Semi-supervised learning (SSL) essentially pursues class boundary exploration with less dependence on human annotations. Although typical attempts focus on ameliorating the inevita…
Distilling Representations from GAN Generator via Squeeze and Span
Yu Yang, Xiaotian Cheng, Chang Liu +2
In recent years, generative adversarial networks (GANs) have been an actively studied topic and shown to successfully produce high-quality realistic images in various domains. The…
Local Manifold Augmentation for Multiview Semantic Consistency
Yu Yang, Wing Yin Cheung, Chang Liu +1
Multiview self-supervised representation learning roots in exploring semantic consistency across data of complex intra-class variation. Such variation is not directly accessible an…
ILSGAN: Independent Layer Synthesis for Unsupervised Foreground-Background Segmentation
Qiran Zou, Yu Yang, Wing Yin Cheung +2
Unsupervised foreground-background segmentation aims at extracting salient objects from cluttered backgrounds, where Generative Adversarial Network (GAN) approaches, especially lay…
Out-of-Candidate Rectification for Weakly Supervised Semantic Segmentation
Zesen Cheng, Pengchong Qiao, Kehan Li +6
Weakly supervised semantic segmentation is typically inspired by class activation maps, which serve as pseudo masks with class-discriminative regions highlighted. Although tremendo…