45 accepted papers
In-Context Learning (ICL) has shown great effectiveness in developing generalist image segmentation models. Its significant advantage over text-based descriptions is the ability to convey intricate visual appearance details through simple reference images. However, finding a perfectly matching singl
The astonishing proficiency and unprecedented level of realism of diffusion models in creating and manipulating images have undoubtedly drawn concerns.Many methods have been proposed to detect generated images. Typically, they usually take RGB images as input, and use backbones like ResNet, CLIP vis
Effectively handling long contexts is challenging for Large Language Models (LLMs) due to the rarity of long texts, high computational demands, and substantial forgetting of short-context abilities. Recent approaches have attempted to construct long contexts for instruction tuning, but these methods
Multimodal large reasoning models (MLRMs) are increasingly deployed for vision-language tasks that produce explicit intermediate rationales. However, reasoning traces can contain unsafe content even when the final answer is non-harmful, creating deployment risks. Existing multimodal safety guards pr
Effectively capturing co-occurrence signals, such as hand shapes, facial expressions, and body postures, is critical for semantic understanding in sign language recognition (SLR) and translation (SLT). Although skeleton data offer greater efficiency and robustness than RGB inputs, existing methods t
To enhance the perception and reasoning capabilities of multimodal large language models in complex visual scenes, recent research has introduced agent-based workflows. In these works, MLLMs autonomously utilize image cropping tool to analyze regions of interest for question answering. While existin
Visible-infrared person re-identification (VI-ReID) is challenging due to the large modality discrepancy between visible and infrared images. We contend that this discrepancy is largely related to differing lighting conditions, including differences in light wavelength and light source type. Recentl
Recent diffusion-based models have significantly improved inpainting quality. However, existing methods struggle with multi-task inpainting due to conflicting optimization objectives, and current datasets are typically limited to task-specific scenarios, hindering joint training. To address these ch
Joint Energy-based Models (JEMs) are well known for their ability to unify classification and generation within a single framework. Despite their promising generative and discriminative performance, their robustness remains far inferior to adversarial training (AT), which, conversely, achieves stron
Combination usage of LiDAR and RGB cameras for gait recognition can achieve cross space recognition and privacy protection. In addition, the widespread application of LiDAR cameras with 3D geometry information and the large amount of RGB gaits has led to the demand for cross-modality gait recognitio…
Zero-shot anomaly detection (ZSAD) requires detection models trained using auxiliary data to detect anomalies without any training sample in a target dataset. It is challenging since the models need to generalize to anomalies across different domains. Recently, CLIP-based anomaly detection methods,…
Large Language Models ( LLMs ), e.g. ChatGPT, have been widely adopted in real-world dialogue applications. However, LLMs’ robustness, especially in handling long complex dialogue sessions, including frequent motivation transfer, sophisticated cross-turn dependency, is criticized all along. Neverthe
In 3D human action recognition, limited supervised data makes it challenging to fully tap into the modeling potential of powerful networks such as transformers. As a result, researchers have been actively investigating effective self-supervised pre-training strategies. For example, MAMP shows that i…
In real applications, person re-identification (ReID) expects to retrieve the target person at any time, including both daytime and nighttime, ranging from short-term to long-term. However, existing ReID tasks and datasets cannot meet this requirement, as they are constrained by available time and o
Knowledge Distillation (KD) aims to enhance the performance of student models by transferring knowledge from teacher models. While reaping the benefits of KD, the intellectual property risks associated with it cannot be ignored. Even if models are released without training data or provided as a serv…
Open-vocabulary semantic segmentation (OVSS) aims to segment images of arbitrary categories specified by class labels. While previous approaches relied on extensive image-text pairs or dense semantic annotations, recent training-free methods attempted to overcome these limitations by constructing se…
Recent advancements in in-context segmentation generalists have demonstrated significant success in performing various image segmentation tasks using a limited number of labeled example images. However, real-world applications present challenges due to the variability of support examples, which ofte…
Large-scale Vision Transformers have achieved promising performance on downstream tasks through feature pre-training. However, the performance of vanilla lightweight Vision Transformers (ViTs) is still far from satisfactory compared to that of recent lightweight CNNs or hybrid networks. In this pape…
Recently, vulnerable samples have been shown to be crucial for improving adversarial training performance. Our analysis on existing vulnerable samples mining methods indicate that existing methods have two problems: 1) valuable connections among different pairs of natural samples and their adversari…
AI-synthesized text and images have gained significant attention, particularly due to the widespread dissemination of multi-modal manipulations on the internet, which has resulted in numerous negative impacts on society. Existing methods for multi-modal manipulation detection and grounding primarily…
As a manner to augment pretrained large language models (LLM), knowledge injection is critical to develop vertical domain large models and has been widely studied. While most current approaches, including parameter-efficient fine-tuning (PEFT) and block expansion methods, uniformly apply knowledge a…
Generating realistic human motion from given action descriptions has experienced significant advancements because of the emerging requirement of digital humans. While recent works have achieved impressive results in generating motion directly from textual action descriptions, they often support only…
Infrared small target detection (ISTD) is critical to national security and has been extensively applied in military areas. ISTD aims to segment small target pixels from background. Most ISTD networks focus on designing feature extraction blocks or feature fusion modules, but rarely describe the IST…
The rapid advancement of large language models (LLMs) has accelerated the emergence of in-context learning (ICL) as a cutting-edge approach in the natural language processing domain. Recently ICL has been employed in visual understanding tasks such as semantic segmentation and image captioning yield…
Text-to-Image person re-identification (TI-ReID) aims to retrieve the images of target identity according to the given textual description. The existing methods in TI-ReID focus on aligning the visual and textual modalities through contrastive feature alignment or reconstructive masked language mode…
Infrared small target detection (ISTD) is indispensable in remote sensing and military surveillance. Existing ISTD methods can discover regularly-shaped and clear objects well, but tend to overlook the tough-to-detect ones, such as targets with irregular shapes or blurry boundaries, causing inaccura…
Weakly-supervised Temporal Action Localization (WTAL) aims at localizing actions in untrimmed videos with only video-level labels. Most existing methods embrace a "localization by classification" paradigm and adopt a model that pre-trained with recognition task for feature extraction. The gap betwee…
Unsupervised Domain Adaptative person re-identification (UDA ReID) aims to transfer the knowledge of pre-trained model from labeled source domain to unlabeled target domain. Although the current clustering-based methods have achieved promising success, they neglect the tolerance of the model to cope…
Transformer is popular in recent 3D human pose estimation, which utilizes long-term modeling to lift 2D keypoints into the 3D space. However, current transformer-based methods do not fully exploit the prior knowledge of the human skeleton provided by the kinematic structure. In this paper, we propos…
Most infrared small target detection (ISTD) networks focus on building effective neural blocks or feature fusion modules but none describes the ISTD process from the image evolution perspective. The directional evolution of image pixels influenced by convolution, pooling and surrounding pixels is an…
Copy-Paste is a simple and effective data augmentation strategy for instance segmentation. By randomly pasting object instances onto new background images, it creates new training data for free and significantly boosts the segmentation performance, especially for rare object categories. Although div…
"Graph-based models have achieved great success in person re-identification tasks recently, which compute the graph topology structure (affinities) among different people first and then pass the information across them to achieve stronger features. But we find existing graph-based methods in the vis…
Transformers have achieved great success in pluralistic image inpainting recently. However, we find existing transformer based solutions regard each pixel as a token, thus suffer from information loss issue from two aspects: 1) They downsample the input image into much lower resolutions for efficien…
"Intra-frame inconsistency has been proved to be effective for the generalization of face forgery detection. However, learning to focus on these inconsistency requires extra pixel-level forged location annotations. Acquiring such annotations is non-trivial. Some existing methods generate large-scale…
Semantic image synthesis, translating semantic layouts to photo-realistic images, is a one-to-many mapping problem. Though impressive progress has been recently made, diverse semantic synthesis that can efficiently produce semantic-level multimodal results, still remains a challenge. In this paper,…
Monocular 3D object detection has received increasing attention due to the wide application in autonomous driving. Existing works mainly focus on introducing geometry projection to predict depth priors for each object. Despite their impressive progress, these methods neglect the geometry leverage ef…
Co-occurrent visual pattern makes aggregating contextual information a common paradigm to enhance the pixel representation for semantic image segmentation. The existing approaches focus on modeling the context from the perspective of the whole image, i.e., aggregating the image-level contextual info…
Unsupervised pretraining has achieved great success and many recently works have shown unsupervised pretraining can achieve comparable or even slightly better transfer performance than supervised pretraining on downstream target datasets. But in this paper, we find this conclusion may not hold when…
Visible-infrared cross modality person re-identification (VI-ReID) is a core but challenging technology in the 24-hours intelligent surveillance system. How to eliminate the large modality gap lies in the heart of VI-ReID. Conventional methods mainly focus on directly aligning the heterogeneous moda…
This paper studies the context aggregation problem in semantic image segmentation. The existing researches focus on improving the pixel representations by aggregating the contextual information within individual images. Though impressive, these methods neglect the significance of the representations…
Video object detection is challenging in the presence of appearance deterioration in certain video frames. Therefore, it is a natural choice to aggregate temporal information from other frames of the same video into the current frame. However, ROI Align, as one of the most core procedures of video d…
Cross-modality person re-identification (cm-ReID) is a challenging but key technology for intelligent video analysis. Existing works mainly focus on learning modality-shared representation by embedding different modalities into a same feature space, lowering the upper bound of feature distinctivenes…
Semi-supervised learning (SSL) has been extensively studied to improve the generalization ability of deep neural networks for visual recognition. To involve the unlabelled data, most existing SSL methods are based on common density-based cluster assumption: samples lying in the same high-density reg…
The popular tracking-by-detection paradigm for multi-object tracking (MOT) focuses on solving data association problem, of which a robust similarity model lies in the heart. Most previous works make effort to improve feature representation for individual object while leaving the relations among obje…
In this paper, we propose a CNN-based framework for online MOT. This framework utilizes the merits of single object trackers in adapting appearance models and searching for target in the next frame. Simply applying single object tracker for MOT will encounter the problem in computational efficiency…