70 citations · 139 across the 33 of their papers we have counts for
36 papers
Evidence-Grounded Trustworthy Multimodal Reasoning and Evaluation Benchmark in Complex Urban Scenes
Zhaoyang Wei, Bowen Jiang, Xumeng Han +6
While Multimodal Large Language Models (MLLMs) demonstrate impressive performance in benign scenarios, their cognitive reliability deteriorates significantly in complex scenes unde…
Foresee-to-Ground: From Predictive Temporal Perception to Evidence-Driven Reasoning for Video Temporal Grounding
Zelin Zheng, Xinyan Liu, Ruixin Li +4
Current Video-LLM approaches for Video Temporal Grounding (VTG) typically rely on direct timestamp generation from an unstructured visual-token stream, often leading to brittle num…
SAPNet++: Evolving Point-Prompted Instance Segmentation with Semantic and Spatial Awareness
Zhaoyang Wei, Xumeng Han, Xuehui Yu +4
Single-point annotation is increasingly prominent in visual tasks for labeling cost reduction. However, it challenges tasks requiring high precision, such as the point-prompted ins…
Exploring the Temporal Consistency for Point-Level Weakly-Supervised Temporal Action Localization
Yunchuan Ma, Laiyun Qing, Guorong Li +3
Point-supervised Temporal Action Localization (PTAL) adopts a lightly frame-annotated paradigm (\textit{i.e.}, labeling only a single frame per action instance) to train a model to…
Boosting Point-supervised Temporal Action Localization via Text Refinement and Alignment
Yunchuan Ma, Laiyun Qing, Guorong Li +3
Recently, point-supervised temporal action localization has gained significant attention for its effective balance between labeling costs and localization accuracy. However, curren…
Towards Universal Modal Tracking with Online Dense Temporal Token Learning
Yaozong Zheng, Bineng Zhong, Qihua Liang +4
We propose a universal video-level modality-awareness tracking model with online dense temporal token learning (called {\modaltracker}). It is designed to support various tracking…
Evidence-Grounded Trustworthy Multimodal Reasoning and Evaluation Benchmark in Complex Urban Scenes
Zhaoyang Wei, Bowen Jiang, Xumeng Han +6
While Multimodal Large Language Models (MLLMs) demonstrate impressive performance in benign scenarios, their cognitive reliability deteriorates significantly in complex scenes unde…
SDVPT: Semantic-Driven Visual Prompt Tuning for Open-World Object Counting
Yiming Zhao, Guorong Li, Laiyun Qing +5
Open-world object counting leverages the robust text-image alignment of pre-trained vision-language models (VLMs) to enable counting of arbitrary categories in images specified by…
P2Object: Single Point Supervised Object Detection and Instance Segmentation
Pengfei Chen, Xuehui Yu, Xumeng Han +5
Object recognition using single-point supervision has attracted increasing attention recently. However, the performance gap compared with fully-supervised algorithms remains large.…
The Devil is in the Distributions: Explicit Modeling of Scene Content is Key in Zero-Shot Video Captioning
Mingkai Tian, Guorong Li, Yuankai Qi +4
Zero-shot video captioning requires that a model generate high-quality captions without human-annotated video-text pairs for training. State-of-the-art approaches to the problem le…
MambaLCT: Boosting Tracking via Long-term Context State Space Model
Xiaohai Li, Bineng Zhong, Qihua Liang +3
Effectively constructing context information with long-term dependencies from video sequences is crucial for object tracking. However, the context length constructed by existing wo…
Towards Interactive Global Geolocation Assistant
Zhiyang Dou, Zipeng Wang, Xumeng Han +3
Global geolocation, which seeks to predict the geographical location of images captured anywhere in the world, is one of the most challenging tasks in the field of computer vision.…
ClickTrack: Towards Real-time Interactive Single Object Tracking
Kuiran Wang, Xuehui Yu, Wenwen Yu +5
Single object tracking(SOT) relies on precise object bounding box initialization. In this paper, we reconsidered the deficiencies in the current approaches to initializing single o…
RETTA: Retrieval-Enhanced Test-Time Adaptation for Zero-Shot Video Captioning
Yunchuan Ma, Laiyun Qing, Guorong Li +4
Despite the significant progress of fully-supervised video captioning, zero-shot methods remain much less explored. In this paper, we propose a novel zero-shot video captioning fra…
CPR++: Object Localization via Single Coarse Point Supervision
Xuehui Yu, Pengfei Chen, Kuiran Wang +5
Point-based object localization (POL), which pursues high-performance object sensing under low-cost data annotation, has attracted increased attention. However, the point annotatio…
Weakly Supervised Video Individual CountingWeakly Supervised Video Individual Counting
Xinyan Liu, Guorong Li, Yuankai Qi +5
Video Individual Counting (VIC) aims to predict the number of unique individuals in a single video. % Existing methods learn representations based on trajectory labels for individu…
Dynamic Erasing Network Based on Multi-Scale Temporal Features for Weakly Supervised Video Anomaly Detection
Chen Zhang, Guorong Li, Yuankai Qi +4
The goal of weakly supervised video anomaly detection is to learn a detection model using only video-level labeled data. However, prior studies typically divide videos into fixed-l…
Semantic-aware SAM for Point-Prompted Instance Segmentation
Zhaoyang Wei, Pengfei Chen, Xuehui Yu +3
Single-point annotation in visual tasks, with the goal of minimizing labelling costs, is becoming increasingly prominent in research. Recently, visual foundation models, such as Se…
Towards Subject-Oriented Video Captioning via User-Specified Targets
Chang Teng, Yunchuan Ma, Guorong Li +3
Describing video content according to users' needs is a long-held goal. Although existing video captioning methods have made significant progress, the generated captions may not fo…
P2RBox: Point Prompt Oriented Object Detection with SAM
Guangming Cao, Xuehui Yu, Wenwen Yu +5
Single-point annotation in oriented object detection of remote sensing scenarios is gaining increasing attention due to its cost-effectiveness. However, due to the granularity ambi…
Towards Unified Token Learning for Vision-Language Tracking
Yaozong Zheng, Bineng Zhong, Qihua Liang +3
In this paper, we present a simple, flexible and effective vision-language (VL) tracking pipeline, termed \textbf{MMTrack}, which casts VL tracking as a token generation task. Trad…
Spatial Self-Distillation for Object Detection with Inaccurate Bounding Boxes
Di Wu, Pengfei Chen, Xuehui Yu +3
Object detection via inaccurate bounding boxes supervision has boosted a broad interest due to the expensive high-quality annotation data or the occasional inevitability of low ann…
Progressive Multi-resolution Loss for Crowd Counting
Ziheng Yan, Yuankai Qi, Guorong Li +4
Crowd counting is usually handled in a density map regression fashion, which is supervised via a L2 loss between the predicted density map and ground truth. To effectively regulate…
Exploiting Completeness and Uncertainty of Pseudo Labels for Weakly Supervised Video Anomaly Detection
Chen Zhang, Guorong Li, Yuankai Qi +4
Weakly supervised video anomaly detection aims to identify abnormal events in videos using only video-level labels. Recently, two-stage self-training methods have achieved signific…