16 accepted papers
Scenes with water surfaces present a significant challenge for Gaussian Splatting due to the simultaneous presence of refraction and reflection, as well as the difficulty of accurately estimating the geometry of transparent water surfaces. To address this, we propose a novel framework for reconstruc
Existing human mesh recovery methods assume a standard human body structure, overlooking diverse anatomical conditions such as limb loss. This assumption introduces bias with applied to individuals with amputations, a limitation further exacerbated by the scarcity of suitable datasets. To address th…
We introduce a deepfake video detection approach that exploits pixel-wise temporal inconsistencies, which traditional spatial frequency-based detectors often overlook. The traditional detectors represent temporal information merely by stacking spatial frequency spectra across frames, resulting in th…
Domain Generalizable Face Anti-Spoofing (DG-FAS) methods effectively capture domain-invariant features by aligning the directions (weights) of local decision boundaries across domains. However, the bias terms associated with these boundaries remain misaligned, leading to inconsistent classification…
Recent research on LiDAR-based 3D object detectors has shown strong performance; however, evaluations typically focus on dominant classes, overlooking rare classes, such as strollers, which could be critical in real autonomous driving scenarios. This oversight is problematic because state-of-the-art…
This paper presents a new approach for the detection of fake videos based on the analysis of style latent vectors and their abnormal behavior in temporal changes in the generated videos. We discovered that the generated facial videos suffer from the temporal distinctiveness in the temporal changes o…
We propose a text-guided variational image generation method to address the challenge of getting clean data for anomaly detection in industrial manufacturing. Our method utilizes text information about the target object learned from extensive text library documents to generate non-defective data ima…
This paper introduces a novel approach for topic modeling utilizing latent codebooks from Vector-Quantized Variational Auto-Encoder~(VQ-VAE), discretely encapsulating the rich information of the pre-trained embeddings such as the pre-trained language model. From the novel interpretation of the laten…
The class-wise training losses often diverge as a result of the various levels of intra-class and inter-class appearance variation, and we find that the diverging class-wise training losses cause the uncalibrated prediction with its reliability. To resolve the issue, we propose a new calibration met…
"While recent advances in generative models benefit the society, the generated images can be abused for malicious purposes, like fraud, defamation, and false news. To prevent such cases, vigorous research is conducted on distinguishing the generated images from the real ones, but challenges still re…
Various deepfake detectors have been proposed, but challenges still exist to detect images of unknown categories or GAN models outside of the training settings. Such issues arise from the overfitting issue, which we discover from our own analysis and the previous studies to originate from the frequ…
Active Learning for discriminative models has largely been studied with the focus on individual samples, with less emphasis on how classes are distributed or which classes are hard to deal with. In this work, we show that this is harmful. We propose a method based on the Bayes' rule, that can natura…
We propose a new context-aware correlation filter based tracking framework to achieve both high computational speed and state-of-the-art performance among real-time trackers. The major contribution to the high computational speed lies in the proposed deep feature compression that is achieved by a co…
This paper proposes a novel tracker which is controlled by sequentially pursuing actions learned by deep reinforcement learning. In contrast to the existing trackers using deep networks, the proposed tracker is designed to achieve a light computation as well as satisfactory tracking accuracy in both…
We propose a new tracking framework with an attentional mechanism that chooses a subset of the associated correlation filters for increased robustness and computational efficiency. The subset of filters is adaptively selected by a deep attentional network according to the dynamic properties of the t…
In this paper, we present a novel attention-modulated visual tracking algorithm that decomposes an object into multiple cognitive units, and trains multiple elementary trackers in order to modulate the distribution of attention according to various feature and kernel types. In the integration stage…