29 accepted papers
We present Creat3r, an iterative next-best-view (NBV) selection framework for efficient, high-quality 3D reconstruction. Starting from a small seed set of image-pose pairs, Creat3r repeatedly selects the most informative next camera pose. After each pose is chosen, the corresponding image is acquire…
Synthetic image generators evolve rapidly, challenging detectors to generalize across current methods and adapt to new ones. We study domain-incremental synthetic image detection with a two-phase evaluation. Phase I trains on either diffusion- or GAN-based data and tests on the combined group to qua…
Principal Component Analysis (PCA), a classical dimensionality reduction technique, and 2D Gaussian representation, an adaptation of 3D Gaussian Splatting for image representation, offer distinct approaches to modeling visual data. We present EigenGS, a novel method that bridges these paradigms thro…
Token compression techniques, such as token merging and pruning, are essential for alleviating the substantial computational burden caused by the proliferation of tokens within attention mechanisms. However, current methods often rely on token-to-token distances or similarity metrics to evaluate tok…
Accurate identification and localization of objects in 3-D scenes are essential for advancing comprehensive 3-D scene understanding. Although diffusion models have demonstrated impressive capabilities across a broad spectrum of computer vision tasks, their potential in both 2-D and 3-D object detect…
Face anti-spoofing (FAS) plays a pivotal role in ensuring the security and reliability of face recognition systems. With advancements in vision-language pretrained (VLP) models, recent two-class FAS techniques have leveraged the advantages of using VLP guidance, while this potential remains unexplor…
Pixel tracking in single-view video sequences has recently emerged as a significant area of research. While previous work has primarily concentrated on tracking within a given video, we propose to expand pixel correspondence estimation into multi-view scenarios. The central concept involves utilizin…
We propose a unified approach to simultaneously addressing the conventional setting of binary deepfake classification and a more challenging scenario of uncovering what facial components have been forged as well as the exact order of the manipulations. To solve the former task we consider multiple i…
Many face anti-spoofing (FAS) methods have focused on learning discriminative features from both live and spoof training data to strengthen the security of face recognition systems. However since not every possible attack type is available in the training stage these FAS methods usually fail to dete…
"Existing generalized few-shot 3D segmentation (GFS3DS) methods typically prioritize enhancing the training of base-class prototypes while neglecting the rich semantic information within background regions for future novel classes. We introduce a novel GFS3DS learner that strategically leverages bac…
This paper describes an attention-driven approach to 3-D point cloud sampling. We establish our method based on a structure-aware attention discriminant analysis that explores geometric and semantic relations embodied among points and their clusters. The proposed attention discriminant sampling (ADS…
Object detection over a long-tailed large-scale dataset is practical, challenging, and comprehensively under-explored. Recently proposed methods mainly focus on eliminating the imbalanced classification problem. However, only a few attempts have been made to consider the quality of the predicted bou…
Hinted by a modest support set, few-shot action detection (FSAD) aims at localizing the action instances of unseen classes within an untrimmed query video. Existing FSAD techniques mostly rely on generating a set of class-agnostic action proposals from the query video and then finding the most plaus…
We present a shape-guided expert-learning framework to tackle the problem of unsupervised 3D anomaly detection. Our method is established on the effectiveness of two specialized expert models and their synergy to localize anomalous regions from color and shape modalities. The first expert utilizes g…
Learning to capture human motion is essential to 3D human pose and shape estimation from monocular video. However, the existing methods mainly rely on recurrent or convolutional operation to model such temporal information, which limits the ability to capture non-local context relations of human mot…
"Contrastive learning (CL) is one of the most successful paradigms for self-supervised learning (SSL). In a principled way, it considers two augmented views of the same image as positive to be pulled closer, and all other images negative to be pushed further apart. However, behind the impressive suc…
We present a pose adaptive few-shot learning procedure and a two-stage data interpolation regularization, termed Pose Adaptive Dual Mixup (PADMix), for single-image 3D reconstruction. While augmentations via interpolating feature-label pairs are effective in classification tasks, they fall short in…
Self-supervised training that elegantly couples contrastive learning with a wide spectrum of data augmentation techniques has been shown to be a successful paradigm for representation learning. However, current methods implicitly maximize the agreement between differently augmented views of the same…
"Video anomaly detection (VAD) aims at localizing unexpected actions or activities in a video sequence. Existing mainstream VAD techniques are based on either the one-class formulation, which assumes all training data are normal, or weakly-supervised, which requires only video-level normal/anomaly l…
One-shot object detection tackles a challenging task that aims at identifying within a target image all object instances of the same class, implied by a query image patch. The main difficulty lies in the situation that the class label of the query patch and its respective examples are not available…
We address the problem of generalized zero-shot learning (GZSL) where the task is to predict the class label of a target image whether its label belongs to the seen or unseen category. Similar to ZSL, the learning setting assumes that all class-level semantic features are given, while only the image…
Anomaly detection (AD) aims to address the task of classification or localization of image anomalies. This paper addresses two pivotal issues of reconstruction-based approaches to AD in images, namely, model adaptation and reconstruction gap. The former generalizes an AD model to tackling a broad ra…
This paper aims to tackle the problem of referring image segmentation, which is targeted at reasoning the region of interest referred by a query natural language sentence. One key issue to address the referring image segmentation is how to establish the cross-modal representation for encoding the tw…
This paper addresses a new task called referring 3D instance segmentation, which aims to segment out the target instance in a 3D scene given a query sentence. Previous work on scene understanding has explored visual grounding with natural language guidance, yet the emphasis is mostly constrained on…
Delineation of cancerous regions in gigapixel whole slide images (WSIs) is a crucial diagnostic procedure in digital pathology. This process is time-consuming because of the large search space in the gigapixel WSIs, causing chances of omission and misinterpretation at indistinct tumor lesions. To ta…
This paper aims to tackle the challenging problem of one-shot object detection. Given a query image patch whose class label is not included in the training data, the goal of the task is to detect all instances of the same class in a target image. To this end, we develop a novel {\em co-attention and…
Motivated by the conventional grouping techniques to image segmentation, we develop their DNN counterpart to tackle the referring variant. The proposed method is driven by a convolutional-recurrent neural network (ConvRNN) that iteratively carries out top-down processing of bottom-up segmentation cu…
Automatic saliency prediction in 360° videos is critical for viewpoint guidance applications (e.g., Facebook 360 Guide). We propose a spatial-temporal network which is (1) unsupervisedly trained and (2) tailor-made for 360° viewing sphere. Note that most existing methods are less scalable since they…
Varying types of shots is a fundamental element in the language of film, commonly used by a visual storytelling director to convey the emotion, ideas, and art. To classify such types of shots from images, we present a new framework that facilitates the intriguing task by addressing two key issues. W…