29 accepted papers
Diffusion-based Real-World Image Super-Resolution (Real-ISR) achieves impressive perceptual quality but suffers from high computational costs due to iterative sampling. While recent distillation approaches leveraging large-scale Text-to-Image (T2I) priors have enabled one-step generation, they are t…
Text-prompted image segmentation enables fine-grained visual understanding and is critical for applications such as human-computer interaction and robotics. However, existing supervised fine-tuning methods typically ignore explicit chain-of-thought (CoT) reasoning at test time, which limits their ab
Recent advancements in weakly-supervised video anomaly detection have achieved remarkable performance by applying the multiple instance learning paradigm based on multimodal foundation models such as CLIP to highlight anomalous instances and classify categories. However, their objectives may tend to
Global localization, encompassing robust place recognition and precise transformation estimation, is crucial for mobile robot navigation when the global navigation satellite system (GNSS) is unavailable. While LiDAR-based approaches are favored for their accuracy in 3D perception and resilience to i
Co-salient Object Detection (CoSOD) aims to segment salient objects that consistently appear across a group of related images. Despite the notable progress achieved by recent training-based approaches, they still remain constrained by the closed-set datasets and exhibit limited generalization. Howev
Recent research on universal object detection aims to introduce language in a SoTA closed-set detector and then generalize the open-set concepts by constructing large-scale (text-region) datasets for training. However, these methods face two main challenges: (i) how to efficiently use the prior info…
The live streaming e-commerce paradigm is experiencing a meteoric surge, distinguished by hosts dynamically showcasing products via live videos and fostering interactive engagement with their viewers. Although a streamer may not cater to every individual’s tastes, it is imperative that it resonates…
How can we enable models to comprehend video anomalies occurring over varying temporal scales and contexts?Traditional Video Anomaly Understanding (VAU) methods focus on frame-level anomaly prediction, often missing the interpretability of complex and diverse real-world anomalies. Recent multimodal…
It is a new trend to fine-tune Large Multimodal Models (LMMs) to adapt to specific visual tasks through task-related conversation data. This approach provides a new paradigm for solving various vision-language tasks, however, it still faces two problems: (1) the global visual features input to the b…
In recent years, semantic segmentation has flourished in various applications. However, the high computational cost remains a significant challenge that hinders its further adoption. The filter pruning method for structured network slimming offers a direct and effective solution for the reduction o…
Despite the great potential in capturing long-range dependency, one rarely-explored underlying issue of transformer in medical image segmentation is attention collapse, making it often degenerate into a bypass module in CNN-Transformer hybrid architectures. This is due to the high computational comp…
Text prompts are crucial for generalizing pre-trained open-set object detection models to new categories. However, current methods for text prompts are limited as they require manual feedback when generalizing to new categories, which restricts their ability to model complex scenes, often leading to…
Federated learning (FL) has emerged as a promising paradigm for training segmentation models on decentralized medical data, owing to its privacy-preserving property. However, existing research overlooks the prevalent annotation noise encountered in real-world medical datasets, which limits the perfo…
Due to escalating privacy concerns, federated learning has been recognized as a vital approach for training deep neural networks with decentralized medical data. In practice, it is challenging to ensure consistent imaging quality across various institutions, often attributed to equipment malfunction…
Neural Radiance Fields (NeRF) have recently achieved impressive results in synthesizing novel views. However, with the few-shot setup, the NeRF is prone to overfitting to the supervision views, which causes artifacts when rendering novel views. To this end, we design a human-perception-guided meta-t…
Deep learning-based methods have demonstrated encouraging results in tackling the task of panoramic image inpainting. However, it is challenging for existing methods to distinguish valid pixels from invalid pixels and find suitable references for corrupted areas, thus leading to artifacts in the inp…
Despite considerable progress being achieved in point cloud geometry compression, there still remains a challenge in effectively compressing large-scale scenes with sparse surfaces. Another key challenge lies in reducing decoding latency, a crucial requirement in real-world application. In this pape…
Reconstruction-based methods have been commonly used for unsupervised anomaly detection, in which a normal image is reconstructed and compared with the given test image to detect and locate anomalies. Recently, diffusion models have shown promising applications for anomaly detection due to their pow…
Federated noisy label learning (FNLL) is emerging as a promising tool for privacy-preserving multi-source decentralized learning. Existing research, relying on the assumption of class-balanced global data, might be incapable to model complicated label noise, especially in medical scenarios. In this…
This letter presents an approach to develop a variable impedance controller with considerations of the optimality and stability. Firstly, an original optimal variable law is designed via demonstration learning, through which the Gaussian mixture model/Gaussian mixture regression (GMM/GMR) algorithm
This paper proposes a quadratic programming (QP)-based variable impedance control (VIC) algorithm to solve contact-rich trajectory tracking problems with impedance, position and velocity constraints. To the best of our knowledge, the impedance constraints which are significant to ensure the worst co…
Social recommendation (SR) aims to enhance the performance of recommendations by incorporating social information. However, such information is not always reliable, e.g., some of the friends may share similar preferences with the user on a specific item, while others may be irrelevant to this item d…
Next basket recommendation aims to provide users a basket of items on the next visit by considering the sequence of their historical baskets. However, since a user’s purchase interests vary over time, historical baskets often contain many irrelevant items to his/her next choices. Therefore, it is ne…
Uplift modeling aims to model the incremental impact of a treatment on an individual outcome, which has attracted great interests of researchers and practitioners from different communities. Existing uplift modeling methods rely on either the data collected from randomized controlled trials (RCTs) o…
Predicting the popularity of shopping guide micro-videos incorporating merchandise is crucial for online advertising. What are the significant factors affecting the popularity of the micro-video? How to extract and effectively fuse multiple modalities for the micro-video popularity prediction? This…
Cross-domain recommendation (CDR) technology is proved to be an effective way to tackle the difficulties encountered by traditional recommender technology (e.g. CF), such as data sparsity and cold-start. However, on account of the heterogeneity, it is difficult to enhance the representation of user…
Image-text matching is an important multi-modal task with massive applications. It tries to match the image and the text with similar semantic information. Existing approaches do not explicitly transform the different modalities into a common space. Meanwhile, the attention mechanism which is widely…
We present a visual localization framework based on novel deep attention aware features for autonomous driving that achieves centimeter level localization accuracy. Conventional approaches to the visual localization problem rely on handcrafted features or human-made objects on the road. They are kno…
In this paper, a new method is proposed to generate image-assisted geometry simplification for estimating minimum sampling rate of image-based rendering (IBR). If some geometry information (e.g., the shape of object surface and proxy geometry) on a scene is known, we can decompose the scene geometry…