15 accepted papers
The denoising diffusion probabilistic model (DDPM) for image inpainting transforms the input image to Gaussian noise by adding noise in the forward process, and reconstitution masked regions that are consistent with unmasked regions from pure noise in the reverse process. Recently, on inpainting met…
Low-rank adaptation (LoRA) has become a prevalent method for adapting pre-trained large language models to downstream tasks. However, the simple low-rank decomposition form may constrain the optimization flexibility. To address this limitation, we introduce Location-aware Cosine Adaptation (LoCA), a…
Multimodal emotion recognition aims to identify emotions by integrating multimodal features derived from spoken utterances. However, existing work often neglects the calibration of conversational entities, focusing mainly on extracting potential intra- or cross-modal information. This leads to the u
Although optical flow estimation has improved significantly in recent years, large displacements and occlusions remain challenging for current methods due to motion discontinuities that may hinder accurate feature correspondences in these regions, leading to degraded performance. To address this cha…
Efficiently utilizing rich knowledge in pretrained models has become a critical topic in the era of large models. This work focuses on adaptively utilize knowledge from multiple source-pretrained models to an unlabeled target domain without accessing the source data. Despite being a practically usef…
Conventional Unsupervised Domain Adaptation (UDA) strives to minimize distribution discrepancy between domains which neglects to harness rich semantics from data and struggles to handle complex domain shifts. A promising technique is to leverage the knowledge of large-scale pre-trained vision-langua…
Large vision-language models (VLMs) like CLIP have demonstrated good zero-shot learning performance in the unsupervised domain adaptation task. Yet most transfer approaches for VLMs focus on either the language or visual branches overlooking the nuanced interplay between both modalities. In this wor…
Accurate estimation of customer lifetime value (LTV), which reflects the potential consumption of a user over a period of time, is crucial for the revenue management of online advertising platforms. However, predicting LTV in real-world applications is not an easy task since the user consumption dat…
Generalized zero-shot learning (GZSL) aims to recognize samples whose categories may not have been seen at training. Recognizing unseen classes as seen ones or vice versa often leads to poor performance in GZSL. Therefore, distinguishing seen and unseen domains is naturally an effective yet challeng…
Person search has recently emerged as a challenging task that jointly addresses pedestrian detection and person re-identification. Existing approaches follow a fully supervised setting where both bounding box and identity annotations are available. However, annotating identities is labor-intensive,…
The development of detection methods for oriented object detection remains a challenging task. A considerable obstacle is the wide variation in the shape (e.g., aspect ratio) of objects. Sample selection in general object detection has been widely studied as it plays a crucial role in the performanc…
Zero-shot learning (ZSL) aims to recognize novel classes by transferring semantic knowledge from seen classes to unseen ones. Semantic knowledge is learned from attribute descriptions shared between different classes, which are strong prior for localization of object attribute for representing discr…
Open Set Domain Adaptation (OSDA) is a challenging domain adaptation setting which allows the existence of unknown classes on the target domain. Although existing OSDA methods are good at classifying samples of known classes, they ignore the classification ability for the unknown samples, making the…
Unsupervised Domain Adaptation (UDA) aims to generalize the knowledge learned from a well-labeled source domain to an unlabled target domain. Recently, adversarial domain adaptation with two distinct classifiers (bi-classifier) has been introduced into UDA which is effective to align distributions b…
Conventional zero-shot learning (ZSL) methods generally learn an embedding, e.g., visual-semantic mapping, to handle the unseen visual samples via an indirect manner. In this paper, we take the advantage of generative adversarial networks (GANs) and propose a novel method, named leveraging invariant…