30 accepted papers
Medical image quality assessment (IQA) serves as a critical safeguard for precise clinical diagnosis and treatment. However, existing methods still face challenges arising from data scarcity and heterogeneity across imaging domains, which confine solutions to domain-specific designs and limit their
Generative modeling has recently shown remarkable promise for visuomotor policy learning, enabling flexible and expressive control across diverse embodied AI tasks. However, existing generative policies often struggle with data inefficiency, requiring large-scale demonstrations, and sampling ineffic
Natural videos exhibit heterogeneous temporal dynamics, with certain segments undergoing high-dynamic scene transitions and others dominated by low-dynamic visual changes. However, treating all frames identically, a common practice in most MLLMs, leads to redundant visual encoding, which results in
Random Sample Consensus (RANSAC) is a fundamental approach for robustly estimating parametric models from noisy data. Existing learning-based RANSAC methods utilize deep learning to enhance the robustness of RANSAC against outliers. However, these approaches are trained and tested on the data genera
Representation learning with Vision Transformers (ViTs) has advanced rapidly, yet the utility of large-scale models in spatially sensitive tasks is hindered by spurious tokens. Prior efforts to mitigate this have been limited, often defining these artifacts narrowly, for example, as simple high-norm
Current AI-Generated Image (AIGI) detection approaches predominantly rely on binary classification to distinguish real from synthetic images, often lacking interpretable or convincing evidence to substantiate their decisions. This limitation stems from existing AIGI detection benchmarks, which, desp…
The remarkable evolution of generative models has enabled the generation of high-quality, visually attractive images, often perceptually indistinguishable from real photographs to human eyes. This has spurred significant attention on AI-generated image (AIGI) detection. Intuitively, higher image qua…
Open-set counting is garnering increasing attention due to its capability to enumerate objects of arbitrary category. It can be generally categorized into two methodologies: text-guided zero-shot counting methods and exemplar-guided few-shot counting methods. Previous text-guided zero-shot methods o…
Text-guided zero-shot object counting leverages vision-language models (VLMs) to count objects of an arbitrary class given by a text prompt. Existing approaches for this challenging task only utilize local patch-level features to fuse with text feature, ignoring the important influence of the global…
Generative models in Autonomous Driving (AD) enable diverse scenario creation, yet existing methods fall short by only capturing a limited range of modalities, restricting the capability of generating controllable scenes for comprehensive evaluation of AD systems. In this paper, we introduce a multi…
Contrastive Language-Image Pre-training (CLIP) excels in global alignment with language but exhibits limited sensitivity to spatial information, leading to strong performance in zero-shot classification tasks but underperformance in tasks requiring precise spatial understanding. Recent approaches ha…
Although current data augmentation methods are successful to alleviate the data insufficiency, conventional augmentation are primarily intra-domain while advanced generative adversarial networks (GANs) generate images remaining uncertain, particularly in small-scale datasets. In this paper, we propo…
A codebook designed for learning discrete distributions in latent space has demonstrated state-of-the-art results on generation tasks. This inspires us to explore what distribution of codebook is better. Following the spirit of Kepler's Conjecture, we cast the codebook training as solving the sphere…
Dense Self-Supervised Learning (SSL) creates positive pairs by building positive paired regions or points, thereby aiming to preserve local features, for example of individual objects. However, existing approaches tend to couple objects by leaking information from the neighboring contextual regions…
In the realm of point cloud scene understanding particularly in indoor scenes objects are arranged following human habits resulting in objects of certain semantics being closely positioned and displaying notable inter-object correlations. This can create a tendency for neural networks to exploit the…
Generative adversarial networks (GAN) have been regarded as promising for anomalous sound detection (ASD) by training an unsupervised one-class classifier to pick out the anomalous sample. Existing GAN-based anomaly detection models usually focus on the generator to reduce the reconstruction error.…
Self-supervised learning (SSL) has the potential to benefit many applications, particularly those where manually annotating data is cumbersome. One such situation is the semantic segmentation of point clouds. In this context, existing methods employ contrastive learning strategies and define positiv…
Referring image segmentation aims at localizing all pixels of the visual objects described by a natural language sentence. Previous works learn to straightforwardly align the sentence embedding and pixel-level embedding for highlighting the referred objects, but ignore the semantic consistency of pi…
Self-supervised learning (SSL) methods aim to learn view-invariant representations by maximizing the similarity between the features extracted from different crops of the same image regardless of cropping size and content. In essence, this strategy ignores the fact that two crops may truly contain d…
This paper focuses on the unsupervised domain adaptation problem for video-based crowd counting, in which we use labeled data as source domain and unlabelled video data as target domain. It is challenging as there is a huge gap between the source and the target domain and no annotations of samples a…
In this paper, we revisit the long-standing Traveling Salesman Problem (TSP) and focus on the challenging, yet practical route planning problem with limited computational resources. We make contributions to TSP, one of the most famous NP-hard problems by providing a new improved approximate solution…
Classification and localization are two pillars of visual object detectors. However, in CNN-based detectors, these two modules are usually optimized under a fixed set of candidate (or anchor) bounding boxes. This configuration significantly limits the possibility to jointly optimize classification a…
Weakly-supervised action localization requires training a model to localize the action segments in the video given only video level action label. It can be solved under the Multiple Instance Learning (MIL) framework, where a bag (video) contains multiple instances (action segments). Since only the b…
Weakly supervised object detection (WSOD) is a challenging task when provided with image category supervision but required to simultaneously learn object locations and object detectors. Many WSOD approaches adopt multiple instance learning (MIL) and have non-convex loss functions which are prone to…
The weight sharing scheme and spatial pooling operations in Convolutional Neural Networks (CNNs) introduce semantic correlation to neighboring pixels on feature maps and therefore deteriorate their pixel-wise classification performance. In this paper, we implement an Orthogonal Decomposition Unit (O…
Robust object skeleton detection requires to explore rich representative visual features and effective feature fusion strategies. In this paper, we first re-visit the implementation of HED, the essential principle of which can be ideally described with a linear reconstruction model. Hinted by this,…
In this paper, we establish a baseline for object symmetry detection in complex backgrounds by presenting a new benchmark and an end-to-end deep learning approach, opening up a promising direction for symmetry detection in the wild. The new benchmark, named Sym-PASCAL, spans challenges including obj…
In this paper, a self-learning approach is proposed towards solving scene-specific pedestrian detection problem without any human' annotation involved. The self-learning approach is deployed as progressive steps of object discovery, object enforcement, and label propagation. In the learning procedur…
Compressive sensing (CS) has recently been applied for device-free localization (DFL) by exploiting spatial sparsity to reduce the number of measurements required by DFL systems while maintaining the high localization accuracy. However, few works considered model errors in CS-based DFL. This paper p…
In this paper, we propose a kind of image representation, named PCA filters based convolutional channel features (PCA-CCF) for pedestrian detection. The motivation is to use the convolutional network architecture with orthogonal PCA filters to enhance the state-of-the-art aggregate channel features…