29 accepted papers
Existing RGB-T salient object detection methods predominantly rely on manually aligned and annotated datasets, struggling to handle real-world scenarios with raw, unaligned RGB-T image pairs. In practical applications, due to significant cross-modal disparities such as spatial misalignment, scale va
Classifying the status of NSCLC PD-L1 on chest CT is a cost-effective and non-invasive method. The existing multiple instance learning (MIL) methods are not effective for this task, due to the lack of an efficient feature encoder for 3D instances and ignoring the importance of representative instanc…
Existing fine-grained image retrieval (FGIR) methods predominantly rely on supervision from predefined categories to learn discriminative representations for retrieving fine-grained objects. However, they inadvertently introduce category-specific semantics into the retrieval representation, creating…
The unique optical characteristics of the underwater environment, such as light refraction and loss of salient features, pose a significant challenge to traditional vision sensors, especially in the swarm operation scenario where multiple autonomous underwater vehicles (AUVs) cooperate with the moth
Guided Depth Super-Resolution (GDSR) enhances low-resolution (LR) depth maps by leveraging high-resolution (HR) color images. The primary challenges involve achieving effective cross-modal data alignment and fusion, as well as incorporating multi-scale information within the Transformer architecture…
Few-shot learning addresses the challenges of image classification with limited samples, but current methods often fail to fully utilize sample correlations and external semantic information, leading to low accuracy. To overcome these limitations, we propose a few-shot image classification method ba…
Recent improvements in visual synthesis have significantly enhanced the depiction of generated human photos, which are pivotal due to their wide applicability and demand. Nonetheless, the existing text-to-image or text-to-video models often generate low-quality human photos that might differ conside…
Self-supervised monocular depth estimation from RGB images has seen significant advancements recently, primarily because it eliminates the need for ground truth data during training. However, applying this technique to thermal images remains challenging due to their inherent characteristics, such as…
In domain adaption (DA), joint maximum mean discrepancy (JMMD), as a famous distribution-distance metric, aims to measure joint probability distribution difference between the source domain and target domain, while it is still not fully explored and especially hard to be applied into a subspace-lear…
Self-supervised depth estimation from videos involves predicting the depth map of a target frame and the pose changes between source and target frames. The reconstructed source frame is aligned with the target view using the predicted pose and depth information. Precise pose estimation significantly…
Automatic anime sketch colorization aims to generate a color image from a sketch image, which is challenging due to limited structure and semantic understanding, leading to constrained style, and semantic color inconsistency. In this paper, we introduce a sketch to color diffusion model with semanti…
"We present a novel approach for synthesizing 3D facial motions from audio sequences using key motion embeddings. Despite recent advancements in data-driven techniques, accurately mapping between audio signals and 3D facial meshes remains challenging. Direct regression of the entire sequence often l…
Speech-driven 3D facial animation aims to synthesize vivid facial animations that accurately synchronize with speech and match the unique speaking style. However, existing works primarily focus on achieving precise lip synchronization while neglecting to model the subject-specific speaking style, of…
Fine-grained object retrieval aims to learn discriminative representation to retrieve visually similar objects. However, existing top-performing works usually impose pairwise similarities on the semantic embedding spaces or design a localization sub-network to continually fine-tune the entire model…
Open-set fine-grained retrieval is an emerging challenging task that allows to retrieve unknown categories beyond the training set. The best solution for handling unknown categories is to represent them using a set of visual attributes learnt from known categories, as widely used in zero-shot learn…
Open-set fine-grained retrieval is an emerging challenge that requires an extra capability to retrieve unknown subcategories during evaluation. However, current works are rooted in the close-set scenarios, where all the subcategories are pre-defined, and make it hard to capture discriminative knowle…
In this work, we build a modular-designed codebase, formulate strong training recipes, design an error diagnosis toolbox, and discuss current methods for image-based 3D object detection. Specifically, different from other highly mature tasks, e.g., 2D object detection, the community of image-based 3…
Employing additional prior knowledge to model local features as a final fine-grained object representation has become a trend for fine-grained object retrieval (FGOR). A potential limitation of these methods is that they only focus on common parts across the dataset (e.g. head, body or even leg) by…
3D object detection is a fundamental and challenging task for 3D scene understanding, and the monocular-based methods can serve as an economical alternative to the stereo-based or LiDAR-based methods. However, accurately locating objects in the 3D space from a single image is extremely difficult due…
Existing stereo matching methods mainly tend to directly aggregate features output from Convolutional Neural Network to obtain more discriminative cost features, but ignore the affinity of each element in the cost feature which also plays a key role in enhancing the cost feature. In this work, we pr…
Estimating 3D bounding boxes from monocular images is an essential component in autonomous driving, while accurate 3D object detection from this kind of data is very challenging. In this work, by intensive diagnosis experiments, we quantify the impact introduced by each sub-task and found the `local…
Most weakly supervised fine-grained image recognition (WFGIR) approaches predominantly focus on learning the discriminative details which contain the visual variances and position clues. The position clues can be indirectly learnt by utilizing context information of discriminative visual content. Ho…
Existing color-guided depth super-resolution (DSR) approaches require paired RGB-D data as training examples where the RGB image is used as structural guidance to recover the degraded depth map due to their geometrical similarity. However, the paired data may be limited or expensive to be collected…
Spatial Description Resolution, as a language-guided localization task, is proposed for target location in a panoramic street view, given corresponding language descriptions. Explicitly characterizing an object-level relationship while distilling spatial relationships are currently absent but crucia…
Existing weakly supervised fine-grained image recognition (WFGIR) methods usually pick out the discriminative regions from the high-level feature maps directly. We discover that due to the operation of stacking local receptive filed, Convolutional Neural Network causes the discriminative region diff…
In this paper, we propose a monocular 3D object detection framework in the domain of autonomous driving. Unlike previous image-based methods which focus on RGB feature extracted from 2D images, our method solves this problem in the reconstructed 3D space in order to exploit 3D contexts explicitly. T…
Visible distortions caused by rain streaks have significant negative effects on the performance of many vision and learning algorithms. Most of the existing deraining approaches propose to build complex prior models to formulate the appearance of rain streaks. Unfortunately, these human-designed pri…
In this paper, we propose a novel depth super-resolution framework with deep edge-inference network and edge-guided depth filling. We first construct a convolutional neural network (CNN) architecture to learn a binary map of depth edge location from low resolution depth map and corresponding color i…
Haze is one of the most important factors which reduce the outdoor image quality. Existing approaches often aim to design their models based on principles of hazes. However, even with exactly modeled haze distribution, it is still a challenging task due to factors in real scenario, such as noises, h…