39 accepted papers
Source-Free Object Detection (SFOD) aims to adapt a source-pretrained object detector to a target domain without access to source data. However, existing SFOD methods predominantly rely on internal knowledge from the source model, which limits their capacity to generalize across domains and often re
Recent advances in multi-instance learning (MIL) have demonstrated impressive performance in whole slide image (WSI) analysis. However, current methods search for cues and draw conclusions from all instances or regions, resulting in excessive redundant computation and suboptimal representation quali
Recently, Multimodal Large Language Models (MLLMs) have achieved exceptional performance across diverse tasks, continually surpassing previous expectations regarding their capabilities. Nevertheless, their proficiency in perceiving emotions from images remains debated, with studies yielding divergen…
The maturation of Large Audio Language Models (LALMs) has raised growing expectations for them to comprehend complex audio much like humans. Current efforts primarily replicate text-based reasoning by contextualizing audio content through a one-time encoding, which introduces a critical information…
Many scientific and geometric problems exhibit general linear symmetries, yet most equivariant neural networks are built for compact groups or simple vector features, limiting their reuse on matrix-valued data such as covariances, inertias, or shape tensors. We introduce \textbf{Reductive Lie Neuron…
Temporal Sentence Grounding in Videos (TSGV) aims to temporally localize segments of a video that correspond to a given natural language query. Despite recent progress, most existing TSGV approaches operate under closed-vocabulary settings, limiting their ability to generalize to real-world queries
Multimodal Emotion Recognition (MER) focuses on identifying and interpreting emotions from modality-compound inputs. Closely mirroring human cognitive processes in real-world environments, MER has drawn substantial attention from both academia and industry. Recently, a paradigm shift has been unveil
Bird's-Eye-View (BEV) detection has become a dominant paradigm for 3D object detection in autonomous driving, due to its strong perception capability. However, most existing methods mainly focus on constructing high-quality BEV feature representations, while neglecting the design of task-specific de
Video Large Language Models (Video LLMs) have achieved remarkable results in video understanding tasks. However, they often suffer from heavy computational overhead due to the large number of visual tokens generated from multiple video frames. Existing visual token compression methods often rely on
The advancements in Multimodal Large Language Models (MLLMs) have enabled various multimodal tasks to be addressed under a zero-shot paradigm. This paradigm sidesteps the cost of model fine-tuning, emerging as a dominant trend in practical application. Nevertheless, Multimodal Sentiment Analysis (MS…
Visual emotion recognition (VER), which aims at understanding humans' emotional reactions toward different visual stimuli, has attracted increasing attention. Given the subjective and ambiguous characteristics of emotion, annotating a reliable large-scale dataset is hard. For reducing reliance on da…
The parameter-efficient adaptation of the image-text pretraining model CLIP for video-text retrieval is a prominent area of research. While CLIP is focused on image-level vision-language matching, video-text retrieval demands comprehensive understanding at the video level. Three key discrepancies em…
Video Large Language Models have demonstrated strong video understanding capabilities, yet their practical deployment is hindered by substantial inference costs caused by redundant video tokens. Existing pruning techniques fail to effectively exploit the spatiotemporal redundancy present in video d…
Blind Image Quality Assessment (BIQA) aims to evaluate image quality in line with human perception, without reference benchmarks. Currently, deep learning BIQA methods typically depend on using features from high-level tasks for transfer learning. However, the inherent differences between BIQA and t…
Recently, single-frame infrared small target (SIRST) detection with single point supervision has drawn wide-spread attention. However, the latest label evolution with single point supervision (LESPS) framework suffers from instability, excessive label evolution, and difficulty in exerting embedded n…
Recent advances in selective state space models (Mamba) have shown great promise in whole slide image (WSI) classification. Despite this, WSIs contain explicit local redundancy (similar patches) and irrelevant regions (uninformative instances), posing significant challenges for Mamba-based multi-ins…
Panoramic semantic segmentation has garnered increasing attention due to its ability to provide comprehensive environmental perception. However, it requires a large number of annotated panoramic images to achieve satisfactory performance, which is costly. Recently, Domain Adaptation for Panoramic Se…
AIGC images are prevalent across various fields, yet they frequently suffer from quality issues like artifacts and unnatural textures. Specialized models aim to predict defect region heatmaps but face two primary challenges: (1) lack of explainability, failing to provide reasons and analyses for sub…
Visual emotion recognition (VER), which aims at understanding humans’ emotional reactions toward different visual stimuli, has attracted increasing attention. However, because of the subjectivity and complex nature of emotion, existing VER methods suffer from one or more of the following problems: 1…
Multi-instance learning (MIL) has demonstrated impressive performance in whole slide image (WSI) analysis. However, existing approaches struggle with undesirable results and unbearable computational overhead due to the quadratic complexity of Transformers. Recently, Mamba has offered a feasible solu…
Multi-modal large language models (MLLMs) integrate the inherent text generation capabilities of large language models with an understanding of other modalities, promising wide applications in open-ended tasks. Despite their success, they often generate plausible but incorrect content. This phenomen…
Visual Emotion Recognition (VER) aims to identify emotions from visual content and has garnered significant attention in recent years due to its wide-ranging applications. Although deep learning-based methods have shown success in VER, they require extensive labeled data, which is costly. Unsupervis…
Multimodal sentiment analysis has attracted extensive research attention as increasing users share images and texts to express their emotions and opinions on social media. Collecting large amounts of labeled sentiment data is an expensive and challenging task due to the high cost of labeling and una…
Most text-video retrieval methods utilize the text-image pre-trained models like CLIP as a backbone. These methods process each sampled frame independently by the image encoder, resulting in high computational overhead and limiting practical deployment. Addressing this, we focus on efficient text-vi…
Monocular 3D object detection (M3OD) is important for autonomous driving. However, existing deep learning-based methods easily suffer from performance degradation in real-world scenarios due to the substantial domain gap between training and testing. M3OD's domain gaps are complex, including camera…
In many practical applications, it is often difficult and expensive to obtain large-scale labeled data to train state-of-the-art deep neural networks. Therefore, transferring the learned knowledge from a separate, labeled source domain to an unlabeled or sparsely labeled target domain becomes an app…
Unsupervised domain adaptation aims to transfer knowledge from a fully-labeled source domain to an unlabeled target domain. However, in real-world scenarios, providing abundant labeled data even in the source domain can be infeasible due to the difficulty and high expense of annotation. To address t…
Existing methods on facial expression recognition (FER) are mainly trained in the setting when multi-class data is available. However, to detect the alien expressions that are absent during training, this type of methods cannot work. To address this problem, we develop a Hierarchical Spatial One Cla…
We address the problem of domain generalizable object detection, which aims to learn a domain-invariant detector from multiple "seen" domains so that it can generalize well to other "unseen" domains. The generalization ability is crucial in practical scenarios especially when it is difficult to coll…
To reduce annotation labor associated with object detection, an increasing number of studies focus on transferring the learned knowledge from a labeled source domain to another unlabeled target domain. However, existing methods assume that the labeled data are sampled from a single source domain, wh…
Unsupervised domain adaptation (UDA) for human action recognition is a practical and challenging problem. Compared with image-based UDA, video-based UDA is comprehensive to bridge the domain shift on both spatial representation and temporal dynamics. Most previous works focus on short-term modeling…
Due to its robust and precise distance measurements, LiDAR plays an important role in scene understanding for autonomous driving. Training deep neural networks (DNNs) on LiDAR data requires large-scale point-wise annotations, which are time-consuming and expensive to obtain. Instead, simulation-to-r…
Images play a crucial role for people to express their opinions online due to the increasing popularity of social networks. While an affective image retrieval system is useful for obtaining visual contents with desired emotions from a massive repository, the abstract and subjective characteristics m…
We propose to harness the potential of simulation for semantic segmentation of real-world self-driving scenes in a domain generalization fashion. The segmentation network is trained without any information about target domains and tested on the unseen target domains. To this end, we propose a new ap…
Simulation-to-real domain adaptation for semantic segmentation has been actively studied for various applications such as autonomous driving. Existing methods mainly focus on a single-source setting, which cannot easily handle a more practical scenario of multiple sources with different distribution…
Earlier work demonstrates the promise of deep-learning-based approaches for point cloud segmentation; however, these approaches need to be improved to be practically useful. To this end, we introduce a new model SqueezeSegV2. With an improved model structure, SqueezeSetV2 is more robust against drop…
Image emotion recognition attracts much attention in recent years due to its wide applications. It aims to classify the emotional response of humans, where candidate emotion categories are generally defined by specific psychological theories, such as Ekman's six basic emotions. However, with the dev…
Neural networks rely on convolutions to aggregate spatial information. However, spatial convolutions are expensive in terms of model size and computation, both of which grow quadratically with respect to kernel size. In this paper, we present a parameter-free, FLOP-free "shift" operation as an alter…
Previous works on actor identification mainly focused on static features based on face identification and costume detection, without considering the abundant dynamic information contained in videos. In this paper, we propose a novel method to mine representative actions of each actor, and show the r…