3 citations · 6 across the 20 of their papers we have counts for
25 papers
Image Diffusion Models Are Good 3D Human Motion Generators
Siyuan Yang, Chong Wang, Hao Cheng +4
Text-to-motion generation typically relies on dataset-specific skeletal representations, with models trained separately for benchmarks with different joint layouts. We investigate…
TAME:Topology-Aware Text-Driven Motion Editing across Heterogeneous Humanoid Skeletons
Qichen Zheng, Siyuan Yang, Chong Wang +4
Text-driven motion editing modifies an existing motion sequence according to a text instruction while preserving the content of the source motion. Existing methods are typically bu…
Skeleton-to-Image Encoding: Enabling Skeleton Representation Learning via Vision-Pretrained Models
Siyuan Yang, Jun Liu, Hao Cheng +5
Recent advances in large-scale pretrained vision models have demonstrated impressive capabilities across a wide range of downstream tasks, including cross-modal and multi-modal sce…
Boosting SAM for Cross-Domain Few-Shot Segmentation via Conditional Point Sparsification
Jiahao Nie, Yun Xing, Wenbin An +6
Motivated by the success of the Segment Anything Model (SAM) in promptable segmentation, recent studies leverage SAM to develop training-free solutions for few-shot segmentation, w…
E.M.Ground: A Temporal Grounding Vid-LLM with Holistic Event Perception and Matching
Jiahao Nie, Wenbin An, Gongjie Zhang +4
Despite recent advances in Video Large Language Models (Vid-LLMs), Temporal Video Grounding (TVG), which aims to precisely localize time segments corresponding to query events, rem…
Cross-Domain Few-Shot Segmentation via Multi-view Progressive Adaptation
Jiahao Nie, Guanqiao Fu, Wenbin An +3
Cross-Domain Few-Shot Segmentation aims to segment categories in data-scarce domains conditioned on a few exemplars. Typical methods first establish few-shot capability in a large-…
ContextFusion and Bootstrap: An Effective Approach to Improve Slot Attention-Based Object-Centric Learning
Pinzhuo Tian, Shengjie Yang, Hang Yu +1
A key human ability is to decompose a scene into distinct objects and use their relationships to understand the environment. Object-centric learning aims to mimic this process in a…
Temporal Unlearnable Examples: Preventing Personal Video Data from Unauthorized Exploitation by Object Tracking
Qiangqiang Wu, Yi Yu, Chenqi Kong +5
With the rise of social media, vast amounts of user-uploaded videos (e.g., YouTube) are utilized as training data for Visual Object Tracking (VOT). However, the VOT community has l…
MTL-UE: Learning to Learn Nothing for Multi-Task Learning
Yi Yu, Song Xia, Siyuan Yang +5
Most existing unlearnable strategies focus on preventing unauthorized users from training single-task learning (STL) models with personal data. Nevertheless, the paradigm has recen…
Open-set Anomaly Segmentation in Complex Scenarios
Song Xia, Yi Yu, Henghui Ding +4
Precise segmentation of out-of-distribution (OoD) objects, herein referred to as anomalies, is crucial for the reliable deployment of semantic segmentation models in open-set, safe…
Digital Staining with Knowledge Distillation: A Unified Framework for Unpaired and Paired-But-Misaligned Data
Ziwang Xu, Lanqing Guo, Satoshi Tsutsui +3
Staining is essential in cell imaging and medical diagnostics but poses significant challenges, including high cost, time consumption, labor intensity, and irreversible tissue alte…
Theoretical Insights in Model Inversion Robustness and Conditional Entropy Maximization for Collaborative Inference Systems
Song Xia, Yi Yu, Wenhan Yang +5
By locally encoding raw data into intermediate features, collaborative inference enables end users to leverage powerful deep learning models without exposure of sensitive raw data…
Reconciling Stochastic and Deterministic Strategies for Zero-shot Image Restoration using Diffusion Model in Dual
Chong Wang, Lanqing Guo, Zixuan Fu +4
Plug-and-play (PnP) methods offer an iterative strategy for solving image restoration (IR) problems in a zero-shot manner, using a learned \textit{discriminative denoiser} as the i…
Temporal-Guided Spiking Neural Networks for Event-Based Human Action Recognition
Siyuan Yang, Shilin Lu, Shizheng Wang +3
This paper explores the promising interplay between spiking neural networks (SNNs) and event-based cameras for privacy-preserving human action recognition (HAR). The unique feature…
Class-Independent Increment: An Efficient Approach for Multi-label Class-Incremental Learning
Chenhao Ding, Songlin Dong, Zhengdong Zhou +4
Current research on class-incremental learning primarily focuses on single-label classification tasks. However, real-world applications often involve multi-label scenarios, such as…
Robust and Transferable Backdoor Attacks Against Deep Image Compression With Selective Frequency Prior
Yi Yu, Yufei Wang, Wenhan Yang +5
Recent advancements in deep learning-based compression techniques have surpassed traditional methods. However, deep neural networks remain vulnerable to backdoor attacks, where pre…
See What You Seek: Semantic Contextual Integration for Cloth-Changing Person Re-Identification
Xiyu Han, Xian Zhong, Wenxin Huang +3
Cloth-changing person re-identification (CC-ReID) aims to match individuals across surveillance cameras despite variations in clothing. Existing methods typically mitigate the impa…
Vid-Morp: Video Moment Retrieval Pretraining from Unlabeled Videos in the Wild
Peijun Bao, Chenqi Kong, Zihao Shao +3
Given a natural language query, video moment retrieval aims to localize the described temporal moment in an untrimmed video. A major challenge of this task is its heavy dependence…
SimBase: A Simple Baseline for Temporal Video Grounding
Peijun Bao, Alex C. Kot
This paper presents SimBase, a simple yet effective baseline for temporal video grounding. While recent advances in temporal grounding have led to impressive performance, they have…
Unlearnable Examples Detection via Iterative Filtering
Yi Yu, Qichen Zheng, Siyuan Yang +6
Deep neural networks are proven to be vulnerable to data poisoning attacks. Recently, a specific type of data poisoning attack known as availability attacks has led to the failure…
Single-Image Shadow Removal Using Deep Learning: A Comprehensive Survey
Laniqng Guo, Chong Wang, Yufei Wang +5
Shadow removal aims at restoring the image content within shadow regions, pursuing a uniform distribution of illumination that is consistent between shadow and non-shadow regions.…
From Chaos to Clarity: 3DGS in the Dark
Zhihao Li, Yufei Wang, Alex Kot +1
Novel view synthesis from raw images provides superior high dynamic range (HDR) information compared to reconstructions from low dynamic range RGB images. However, the inherent noi…
MMRel: Benchmarking Relation Understanding in Multi-Modal Large Language Models
Jiahao Nie, Gongjie Zhang, Wenbin An +4
Though Multi-modal Large Language Models (MLLMs) have recently achieved significant progress, they often struggle to understand diverse and complicated inter-object relations. Spec…
ContextGS: Compact 3D Gaussian Splatting with Anchor Level Context Model
Yufei Wang, Zhihao Li, Lanqing Guo +3
Recently, 3D Gaussian Splatting (3DGS) has become a promising framework for novel view synthesis, offering fast rendering speeds and high fidelity. However, the large number of Gau…