28 accepted papers
Self-supervised Continual Graph Learning (CGL) aims to successively learn from a graph sequence with different tasks without label supervision—a paradigm that has attracted widespread attention. Most existing self-supervised CGL methods rely on instance-level consistency objectives that enforce stab…
Contrastive Language-Image Pre-training (CLIP) exhibits strong zero-shot classification ability on image-level tasks, leading to the research to adapt CLIP for open-vocabulary semantic segmentation without training. The key is to improve spatial representation of image-level CLIP, such as replacing…
Heterogeneous Graph Neural Networks (HGNNs), have demonstrated excellent capabilities in processing heterogeneous information networks. Self-supervised learning on heterogeneous graphs, especially contrastive self-supervised strategy, shows great potential when there are no labels. However, this app…
"Few-Shot Class-Incremental Learning (FSCIL) aims to learn new concepts with few training samples while preserving previously acquired knowledge. Although promising performance has been achieved, there remains an underexplored aspect regarding the basic statistical principles underlying FSCIL. There…
Open-vocabulary semantic segmentation strives to distinguish pixels into different semantic groups from an open set of categories. Most existing methods explore utilizing pre-trained vision-language models in which the key is to adopt the image-level model for pixel-level segmentation task. In this…
We propose a novel one-step transformer-based person search framework, PSTR, that jointly performs person detection and re-identification (re-id) in a single architecture. PSTR comprises a person search-specialized (PSS) module that contains a detection encoder-decoder for person detection along wit…
Convolutional Neural Network (CNN) based methods generally take crowd counting as a regression task by outputting crowd densities. They learn the mapping between image contents and crowd density distributions. Though having achieved promising results, these data-driven counting networks are prone to…
Heavy haze results in severe image degradation and thus hampers the performance of visual perception, object detection, etc. On the assumption that dehazed binocular images are superior to the hazy ones for stereo vision tasks such as 3D object detection and according to the fact that image haze is…
Image-text matching plays a central role in bridging vision and language. Most existing approaches only rely on the image-text instance pair to learn their representations, thereby exploiting their matching relationships and making the corresponding alignments. Such approaches only exploit the super…
Recent pedestrian detection methods generally rely on additional supervision, such as visible bounding-box annotations, to handle heavy occlusions. We propose an approach that leverages pedestrian count and proposal similarity information within a two-stage pedestrian detection framework. Both pedes…
We propose a novel two-stage detection method, D2Det, that collectively addresses both precise localization and accurate classification. For precise localization, we introduce a dense local regression that predicts multiple dense box offsets for an object proposal. Different from traditional regress…
Human parsing is for pixel-wise human semantic understanding. As human bodies are underlying hierarchically structured, how to model human structures is the central theme in this task. Focusing on this, we seek to simultaneously exploit the representational capacity of deep graph networks and the hi…
Directly processing large point clouds is inefficient. State-of-the-art frameworks hierarchically employ Farthest Point Sampling (FPS) to down-sample the data for point cloud classification, semantic segmentation, etc. However, using only geometric information, FPS neglects the importance of semanti…
Due to the advantages of real-time detection and improved performance, single-shot detectors have gained great attention recently. To solve the complex scale variations, single-shot detectors make scale-aware predictions based on multiple pyramid layers. However, the features in the pyramid are not…
Single-stage instance segmentation approaches have recently gained popularity due to their speed and simplicity, but are still lagging behind in accuracy, compared to two-stage methods. We propose a fast single-stage instance segmentation method, called SipMask, that preserves instance-specific spat…
Human-object interaction detection is an important and relatively new class of visual relationship detection tasks, essential for deeper scene understanding. Most existing approaches decompose the problem into object localization and interaction recognition. Despite showing progress, these approache…
Single-stage object detectors have recently gained popularity due to their combined advantage of high detection accuracy and real-time speed. However, while promising results have been achieved by these detectors on standard-sized objects, their performance on small objects is far from satisfactory.…
We propose a single-stage detection framework that jointly tackles the problem of multi-scale object detection and class imbalance. Rather than designing deeper networks, we introduce a simple yet effective feature enrichment scheme to produce multi-scale contextual features. We further introduce a…
Single shot detector simultaneously predicts object categories and regression offsets of the default boxes. Despite of high efficiency, this structure has some inappropriate designs: (1) The classification result of the default box is improperly assigned to that of the regressed box during inference…
This work proposes to combine neural networks with the compositional hierarchy of human bodies for efficient and complete human parsing. We formulate the approach as a neural information fusion framework. Our model assembles the information from three inference processes over the hierarchy: direct i…
Single-stage object detection methods have received significant attention recently due to their characteristic realtime capabilities and high detection accuracies. Generally, most existing single-stage detectors follow two common practices: they employ a network backbone that is pretrained on ImageN…
Pedestrian detection relying on deep convolution neural networks has made significant progress. Though promising results have been achieved on standard pedestrians, the performance on heavily occluded pedestrians remains far from satisfactory. The main culprits are intra-class occlusions involving o…
This paper studies the task of matching image and sentence, where learning appropriate representations to bridge the semantic gap between image contents and language appears to be the main challenge. Unlike previous approaches that predominantly deploy symmetrical architecture to represent both moda…
Aggregating multi-level features is essential for capturing multi-scale context information for precise scene semantic segmentation. However, the improvement by directly fusing shallow features and deep features becomes limited as the semantic gap between them increases. To solve this problem, we ex…
Joint object detection and semantic segmentation is essential in many fields such as self-driving cars. An initial attempt towards this goal is to simply share a single network for multi-task learning. We argue that it does not make full use of the fact that detection and segmentation are mutually b…
Zero-Shot Learning (ZSL) is generally achieved via aligning the semantic relationships between the visual features and the corresponding class semantic descriptions. However, using the global features to represent fine-grained images may lead to sub-optimal results since they neglect the discriminat…
The discrimination and simplicity of features are very important for effective and efficient pedestrian detection. However, most state-of-the-art methods are unable to achieve good tradeoff between accuracy and efficiency. Inspired by some simple inherent attributes of pedestrians (i.e., appearance…
Restoring underwater image from a single image is know to be ill-posed, and some assumptions made in previous methods are not suitable for many situations. In this paper, we propose a method based on blue-green channels dehazing and red channel correction for underwater image restoration. Firstly, b…