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20232026
most citedDisentangled Counterfactual Learning for Physical Audiovisual Commonsense Reasoning

2 citations · 7 across the 42 of their papers we have counts for

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13 papers · 1 filter

cs.AI2026

ExPhy: A Benchmark for Explicit Physical Property Learning in Multi-Object Trajectory Forecasting

Rui Wang, Yeteng Wu, Xianlin Zhang +1

Understanding object dynamics requires not only predicting future trajectories but also examining whether a model captures the physical properties that govern motion. However, exis…

cs.CV2026

VTO: Visual Tool Orchestration for Video Anomaly Detection

Rui Wang, Yeteng Wu, Xianling Zhang +1

Video anomaly detection (VAD) is a critical yet challenging task due to the complex and diverse nature of real-world scenarios. Traditional deep learning approaches are fundamental…

cs.CV2026

Boosting Robustness for All-Weather Self-Supervised Depth Estimation in Autonomous Driving

Mengshi Qi, Xiaoyang Bi, Xianlin Zhang +1

Self-supervised depth estimation is challenging for safe autonomous driving under various adverse weather conditions due to sensor perception degradation. These challenges arise fr…

cs.CV2026

CritiqueDriveVLM: From Verifier-Guided Reinforcement Learning to Latent Thought Distillation for Autonomous Driving

Zhaohong Liu, Hao Ye, Xianlin Zhang +1

End-to-end Vision-Language Models (VLMs) show immense potential in autonomous driving. However, standard Supervised Fine-Tuning (SFT) often suffers from reasoning hallucinations an…

cs.CV2026

A DVDrive Approach for doScenes Instructed Driving Challenge

Zijian Fu, Xiangyang Chu, Mengshi Qi +3

Instruction-conditioned trajectory prediction is an emerging problem in autonomous driving, where a model predicts the future ego trajectory not only from visual scene context and…

cs.CV2026

Question-Aware Evidence Ledgers for Video Relational Reasoning

Yilin Ou, Mengshi Qi, Huadong Ma

The VRR-QA challenge evaluates visual relational reasoning in videos, where answers often depend on implicit spatial relations, event boundaries, target identity, and dialogue cont…

cs.CV2026

SGFormer++: Semantic Graph Transformer for Incremental 3D Scene Graph Generation

Mengshi Qi, Changsheng Lv, Zijian Fu +2

In this paper, we propose SGFormer++, a novel Semantic Graph Transformer for 3D scene graph generation (SGG), which aims to parse point cloud scenes into semantic structural graphs…

cs.CV2026

Leveraging Metric Depth for Relative Depth Prediction

Xiaoyang Bi, Shuaikun Liu, Zhaohong Liu +5

We present our solution to the 2025 SoccerNet Monocular Depth Estimation Competition Challenge. Predicting the relative depth in football scenarios is challenging, especially with…

cs.CV2026

A VideoMAE-v2 Approach to Zero-Shot Traffic Accident Anticipation

Siyuan Li, Xiaoyang Bi, Mengshi Qi

Traffic accident anticipation -- predicting the likelihood of an imminent collision at every frame of a dashcam video -- is safety-critical yet difficult to scale, because collecti…

cs.CV2026

Claude Code-Driving Scenario Mining for the Argoverse 2 Challenge

Wei Deng, Caoshengzhe Xue, Shuaikun Liu +3

We present our submission to the CVPR 2026 Argoverse 2 Scenario Mining Challenge. Our system uses a four-stage pipeline: (1) autonomous code generation via a Claude Code agent powe…

cs.CV2026

Global-Local Monte Carlo Tree Search in Vision-Language Models for Text-to-3D Indoor Scene Generation

Mengshi Qi, Wei Deng, Xianlin Zhang +1

Large Vision-Language Models have achieved significant reasoning performance in various tasks. However, there are few studies on text-to-3D indoor scene generation with LVLMs. The…

cs.CV2026

Active Exploring like a Pigeon: Reinforcing Spatial Reasoning via Agentic Vision-Language Models

Wei Deng, Xianlin Zhang, Mengshi Qi

Enabling Vision-Language Models (VLMs) to perform spatial reasoning remains challenging. Existing approaches treat VLMs as passive observers, which is difficult for real-world appl…

cs.CV2026

Reason-Then-Retrieve for CoVR-R with Structured Edit Prompts and Dense-Sparse Fusion

Dongqing Liu, Mengshi Qi, Hongwei Ji

CoVR-R studies reason-aware composed video retrieval: given a reference video and an edit instruction, the system must retrieve the target video that satisfies the edit. The main d…