25 accepted papers
Compositional Zero-Shot Learning (CZSL) addresses the challenge of recognizing unseen attribute-object compositions in images, representing a fundamental challenge in artificial intelligence. Current approaches, which primarily focus on semantic alignment or distribution independence of primitives,
Precise environmental perception is critical for the reliability of autonomous driving systems. While collaborative perception mitigates the limitations of single-agent perception through information sharing, it encounters a fundamental communication-performance trade-off. Existing communication-eff
Accurate reconstruction of 3D vehicle pose and shape from monocular images is challenging, particularly for distant objects in autonomous driving. Existing methods often suffer from geometric ambiguity in depth estimation and structural hollowness in shape recovery, primarily due to inadequate multi
Modern optical flow estimation, though empowered by recent deep neural architectures, remains rooted in the discrete correspondence paradigm inherited from classical vision. Most networks infer frame-to-frame displacements, capturing where pixels move but not how motion evolves continuously through
Sentence representation learning (SRL) aims to learn sentence embeddings that conform to the semantic information of sentences. In recent years, fine-tuning methods based on pre-trained models and contrastive learning frameworks have significantly advanced the quality of sentence representations. Ho
Medical decision-making often involves integrating knowledge from multiple clinical specialties, typically achieved through multidisciplinary teams. Inspired by this collaborative process, recent work has leveraged large language models (LLMs) in multi-agent collaboration frameworks to emulate exper
Multi-agent collaborative perception is expected to significantly improve perception performance by overcoming the limitations of single-agent perception through exchanging complementary information. However, training a robust collaborative perception model requires collecting sufficient training da…
Iris motion tracking is critical for discriminating the iris stiffness and developmental stage of primary angle-closure disease (PACD). Anterior segment optical coherence tomography (AS-OCT) video is a highly efficient approach to observe the morphological determinant in iris motion. However, the ir…
Dense video captioning aims to detect and describe all events in untrimmed videos. This paper presents a dense video captioning network called Multi-Concept Cyclic Learning (MCCL), which aims to: (1) detect multiple concepts at the frame level and leverage these concepts to provide temporal event cu…
Achieving a balance between accuracy and efficiency is a critical challenge in facial landmark detection (FLD). This paper introduces Parallel Optimal Position Search (POPoS), a high-precision encoding-decoding framework designed to address the limitations of traditional FLD methods. POPoS employs t…
Multi-agent collaboration enhances the perception capabilities of individual agents through information sharing. However, in real-world applications, differences in sensors and models across heterogeneous agents inevitably lead to domain gaps during collaboration. Existing approaches based on adapta…
Currently machine learning-based methods for remote sensing pansharpening have progressed rapidly. However existing pansharpening methods often do not fully exploit differentiating regional information in non-local spaces thereby limiting the effectiveness of the methods and resulting in redundant l…
Deep neural networks for image super-resolution (ISR) have shown significant advantages over traditional approaches like the interpolation. However, they are often criticized as 'black boxes' compared to traditional approaches with solid mathematical foundations. In this paper, we attempt to interpr…
Human Mesh Recovery (HMR) aims to estimate the 3D human body from 2D images which is a challenging task due to inherent ambiguities in translating 2D observations to 3D space. A novel approach called PostureHMR is proposed to leverage a multi-step diffusion-style process which converts this task int…
Pansharpening is a significant image fusion technique that merges the spatial content and spectral characteristics of remote sensing images to generate high-resolution multispectral images. Recently, denoising diffusion probabilistic models have been gradually applied to visual tasks, enhancing cont…
Transformer-based methods have proven to be effective in achieving long-distance modeling, capturing the spatial and spectral information, and exhibiting strong inductive bias in various computer vision tasks. Generally, the Transformer model includes two common modes of multi-head self-attention (M…
Despite the success of vision Transformers for the image deraining task, they are limited by computation-heavy and slow runtime. In this work, we investigate Transformer decoder is not necessary and has huge computational costs. Therefore, we revisit the standard vision Transformer as well as its su…
Group activity recognition aims to understand the overall behavior performed by a group of people. Recently, some graph-based methods have made progress by learning the relation graphs among multiple persons. However, the differences between an individual and others play an important role in identif…
Previous work generally believes that improving the spatial invariance of convolutional networks is the key to object counting. However, after verifying several mainstream counting networks, we surprisingly found too strict pixel-level spatial invariance would cause overfit noise in the density map…
Standard convolution operations can effectively perform feature extraction and representation but result in high computational cost, largely due to the generation of the original convolution kernel corresponding to the channel dimension of the feature map, which will cause unnecessary redundancy. In…
Deep Convolution Neural Networks have been adopted for pansharpening and achieved state-of-the-art performance. However, most of the existing works mainly focus on single-scale feature fusion, which leads to failure in fully considering relationships of information between high-level semantics and l…
In this paper, a novel deep generative model-based approach for video error concealment is proposed. Our method is comprised of completion network and two critics. The frame completion network is trained to fool the both the local and global critics, which requires completion network to conceal fram…
The aim of crowd counting is to estimate the number of people in images by leveraging the annotation of center positions for pedestrians' heads. Promising progresses have been made with the prevalence of deep Convolutional Neural Networks. Existing methods widely employ the Euclidean distance (i.e.,…
We introduce a new fashion search protocol where attribute manipulation is allowed within the interaction between users and search engines, e.g. manipulating the color attribute of the clothing from red to blue. It is particularly useful for image-based search when the query image cannot perfectly m…
In recent years, both online retail and video hosting service have been exponentially grown. In this paper, a novel deep neural network, called AsymNet, is proposed to explore a new cross-domain task, Video2Shop, targeting for matching clothes appeared in videos to the exactly same items in online s…