1

Sparse Beats Dense: Rethinking Supervision in Radar-Camera Depth Completion
It is widely believed that sparse supervision is worse than dense supervision in the field of depth completion, but the underlying …
PDF Code
Sparse Beats Dense: Rethinking Supervision in Radar-Camera Depth Completion
Diagnosing the Compositional Knowledge of Vision Language Models from a Game-Theoretic View
Compositional reasoning capabilities are usually considered as fundamental skills to characterize human perception. Recent studies show …
PDF Cite Project
Diagnosing the Compositional Knowledge of Vision Language Models from a Game-Theoretic View
MMT-Bench: A Comprehensive Multimodal Benchmark for Evaluating Large Vision-Language Models Towards Multitask AGI
Large Vision-Language Models (LVLMs) show significant strides in general-purpose multimodal applications such as visual dialogue and …
PDF Cite Code Dataset Project
MMT-Bench: A Comprehensive Multimodal Benchmark for Evaluating Large Vision-Language Models Towards Multitask AGI
Towards Understanding the Generalization of Deepfake Detectors from a Game-Theoretical View
This paper aims to explain the generalization of deepfake detectors from the novel perspective of multi-order interactions among visual …
PDF Cite
Towards Understanding the Generalization of Deepfake Detectors from a Game-Theoretical View
Implicit Identity Leakage: The Stumbling Block to Improving Deepfake Detection Generalization
In this paper, we analyse the generalization ability of binary classifiers for the task of deepfake detection. We find that the …
PDF Cite Code Poster Slides Video DOI
Implicit Identity Leakage: The Stumbling Block to Improving Deepfake Detection Generalization
Explaining Deepfake Detection by Analysing Image Matching
This paper aims to interpret how deepfake detection models learn artifact features of images when just supervised by binary labels. To …
PDF Cite Code Poster Video DOI
Explaining Deepfake Detection by Analysing Image Matching
Interpretable Generative Adversarial Networks
Learning a disentangled representation is still a challenge in the field of the interpretability of generative adversarial networks …
PDF Cite Poster Slides Video DOI
Interpretable Generative Adversarial Networks