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Zhen-Xiang Ma, Zhen-Duo Chen, Tai Zheng, Xin Luo, Zixia Jia, Xin-Shun Xu

Recently, a number of effective methods have been proposed to tackle the challenging task of Few-Shot Fine-Grained Image Classification (FS-FGIC). However, how to fully leverage the backbone network to discover and extract detailed features to generate more discriminative class prototypes, as well as how to accurately model the similarity relationship between query samples and the class prototypes, are still issues to be further considered. Therefore, we propose a novel progreSsively featUre refInement and conTinuous rElationship moDeling method, SUITED for short, to address these two issues existing in the State-of-the-Art FS-FGIC methods. Specifically, we design the Progressive Feature Refinement Module (PFRM) to fully exploit the backbone network's progressive feature extraction capabilities, forming multi-scale feature representations to further enhance discriminative features. Then, the Continuous Relationship Modeling Module (CRMM) is proposed to capture the dependencies between query samples and the corresponding class prototypes, achieving precise optimization of the distances among corresponding sample points in the feature space. We conducted extensive experiments on five fine-grained benchmark datasets, and the experimental results demonstrate that the proposed method is comprehensively ahead of the existing State-of-the-Art methods.

Shuai Lyu, Rongchen Zhang, Zeqi Ma, Fangjian Liao, Dongmei Mo, Waikeung Wong

Few-shot defect multi-classification (FSDMC) is an emerging trend in quality control within industrial manufacturing. However, current FSDMC research often lacks generalizability due to its focus on specific datasets. Additionally, defect classification heavily relies on contextual information within images, and existing methods fall short of effectively extracting this information. To address these challenges, we propose a general FSDMC framework called MVREC, which offers two primary advantages: (1) MVREC extracts general features for defect instances by incorporating the pre-trained AlphaCLIP model. (2) It utilizes a region-context framework to enhance defect features by leveraging mask region input and multi-view context augmentation. Furthermore, Few-shot Zip-Adapter(-F) classifiers within the model are introduced to cache the visual features of the support set and perform few-shot classification. We also introduce MVTec-FS, a new FSDMC benchmark based on MVTec AD, which includes 1228 defect images with instance-level mask annotations and 46 defect types. Extensive experiments conducted on MVTec-FS and four additional datasets demonstrate its effectiveness in general defect classification and its ability to incorporate contextual information to improve classification performance.

Yuhang Lu, Yichen Yao, Jiadong Tu, Jiangnan Shao, Yuexin Ma, Xinge Zhu

Large Vision-Language Models (LVLMs) have recently garnered significant attention, with many efforts aimed at harnessing their general knowledge to enhance the interpretability and robustness of autonomous driving models. However, LVLMs typically rely on large, general-purpose datasets and lack the specialized expertise required for professional and safe driving. Existing vision-language driving datasets focus primarily on scene understanding and decision-making, without providing explicit guidance on traffic rules and driving skills, which are critical aspects directly related to driving safety. To bridge this gap, we propose IDKB, a large-scale dataset containing over one million data items collected from various countries, including driving handbooks, theory test data, and simulated road test data. Much like the process of obtaining a driver's license, IDKB encompasses nearly all the explicit knowledge needed for driving from theory to practice. In particular, we conducted comprehensive tests on 15 LVLMs using IDKB to assess their reliability in the context of autonomous driving and provided extensive analysis. We also fine-tuned popular models, achieving notable performance improvements, which further validate the significance of our dataset.

Yuti Liu, Shice Liu, Junyuan Gao, Peng-tao Jiang, Hao Zhang, Jinwei Chen, Bo Li

Image Aesthetic Assessment (IAA) is a vital and intricate task that entails analyzing and assessing an image's aesthetic values, and identifying its highlights and areas for improvement. Traditional methods of IAA often concentrate on a single aesthetic task and suffer from inadequate labeled datasets, thus impairing in-depth aesthetic comprehension. Despite efforts to overcome this challenge through the application of Multi-modal Large Language Models (MLLMs), such models remain underdeveloped for IAA purposes. To address this, we propose a comprehensive aesthetic MLLM capable of nuanced aesthetic insight. Central to our approach is an innovative multi-scale text-guided self-supervised learning technique. This technique features a multi-scale feature alignment module and capitalizes on a wealth of unlabeled data in a self-supervised manner to structurally and functionally enhance aesthetic ability. The empirical evidence indicates that accompanied with extensive instruct-tuning, our model sets new state-of-the-art benchmarks across multiple tasks, including aesthetic scoring, aesthetic commenting, and personalized image aesthetic assessment. Remarkably, it also demonstrates zero-shot learning capabilities in the emerging task of aesthetic suggesting. Furthermore, for personalized image aesthetic assessment, we harness the potential of in-context learning and showcase its inherent advantages.

Shuhong Liu, Xiang Chen, Hongming Chen, Quanfeng Xu, Mingrui Li

Reconstruction under adverse rainy conditions poses significant challenges due to reduced visibility and the distortion of visual perception. These conditions can severely impair the quality of geometric maps, which is essential for applications ranging from autonomous planning to environmental monitoring. In response to these challenges, this study introduces the novel task of 3D Reconstruction in Rainy Environments (3DRRE), specifically designed to address the complexities of reconstructing 3D scenes under rainy conditions. To benchmark this task, we construct the HydroViews dataset that comprises a diverse collection of both synthesized and real-world scene images characterized by various intensities of rain streaks and raindrops. Furthermore, we propose DeRainGS, the first 3DGS method tailored for reconstruction in adverse rainy environments. Extensive experiments across a wide range of rain scenarios demonstrate that our method delivers state-of-the-art performance, remarkably outperforming existing occlusion-free methods by a large margin.

Gaofeng Liu, Zhiyuan Ma, Tao Fang

Recent years have witnessed the remarkable success of Text-to-3D generation, particularly with the rise of mainstream conditional diffusion models (DMs). Though achieving substantial progress, existing methods still face a knotty "human preference" dilemma, that is the 3D contents generated by the models often deviate greatly from the desired effects (e.g., perspective, aesthetics, shading, appearance, etc.) due to the lack of attention to human preferences. To mitigate the limitation of data deficiency and enable human preference learning, we first elaborately curate the HP3D, a text-to-3D dataset with expert preference annotations which is initally captioned by the multimodal large model LLava and then refined by human expert. Based on such a brand-new HP3D, we further propose DreamAlign, a reward-free method that does not require designing any complex reward models whereas only by introducing a light-weight lora adapter and then designing a novel direct 3D preference optimization (D-3DPO) algorithm for training. Moreover, in the stage of text-to-3D we design an additional Preference Contrastive Feedback training for score distillation sampling, which enables the generated 3D objects to align the human preferences (e.g., aesthetics, material, etc.). Extensive experiments demonstrate that DreamAlign consistently achieves state-of-the-art performance on generative effects and human preference alignment across various benchmark evaluations.

Decheng Liu, Zongqi Wang, Chunlei Peng, Nannan Wang, Ruimin Hu, Xinbo Gao

Due to the successful development of deep image generation technology, forgery detection plays a more important role in social and economic security. Racial bias has not been explored thoroughly in the deep forgery detection field. In the paper, we first contribute a dedicated dataset called the Fair Forgery Detection (FairFD) dataset, where we prove the racial bias of public state-of-the-art (SOTA) methods. Different from existing forgery detection datasets, the self-constructed FairFD dataset contains a balanced racial ratio and diverse forgery generation images with the largest-scale subjects. Additionally, we identify the problems with naive fairness metrics when benchmarking forgery detection models. To comprehensively evaluate fairness, we design novel metrics including Approach Averaged Metric and Utility Regularized Metric, which can avoid deceptive results. We also present an effective and robust post-processing technique, Bias Pruning with Fair Activations (BPFA), which improves fairness without requiring retraining or weight updates. Extensive experiments conducted with 12 representative forgery detection models demonstrate the value of the proposed dataset and the reasonability of the designed fairness metrics. By applying the BPFA to the existing fairest detector, we achieve a new SOTA. Furthermore, we conduct more in-depth analyses to offer more insights to inspire researchers in the community.

Baolong Liu, Ruiqing Yang, Roukai Huang, Wenhao Xu, Xin Pan, Chuanhuang Li, Bin Wang, Xun Wang, Jianfeng Dong

The paper targets the challenging task of Ship License Plate (SLP) recognition. Existing methods for SLP recognition are hampered by the scarcity of large and publicly available datasets, leading to evaluations on small and non-representative datasets. To alleviate it, we have built a large dataset, called SLP34K, which consists of 34,385 images collected by an intelligent traffic surveillance system. The dataset is carefully manually annotated with text labels and attributes, and presents high data diversity by multiple installation locations and long capturing period of the cameras. Additionally, we propose a simple yet effective SLP recognition baseline method. The baseline is equipped with a strong visual encoder that benefits from initial pre-training via self-supervised learning, followed by further refinement through our devised semantic enhancement module. Extensive experiments on SLP34K verify the effectiveness of our proposed baseline. Moreover, while our baseline is designed for SLP recognition, it can also be used for common scene text recognition and achieve state-of-the-art performance on seven mainstream scene text recognition datasets.

Yiheng Lin, Yihan Hu, Chenyi Zhang, Ting Liu, Xiaochao Qu, Luoqi Liu, Yao Zhao, Yunchao Wei

Transformer-based models have recently achieved outstanding performance in image matting. However, their application to high-resolution images remains challenging due to the quadratic complexity of global self-attention. To address this issue, we propose MEMatte, a memory-efficient matting framework for processing high-resolution images. MEMatte incorporates a router before each global attention block, directing informative tokens to the global attention while routing other tokens to a Lightweight Token Refinement Module (LTRM). Specifically, the router employs a local-global strategy to predict the routing probability of each token, and the LTRM utilizes efficient modules to simulate global attention. Additionally, we introduce a Batch-constrained Adaptive Token Routing (BATR) mechanism, which allows each router to dynamically route tokens based on image content and the stages of attention block in the network. Furthermore, we construct an ultra high-resolution image matting dataset, UHR-395, comprising 35,500 training images and 1,000 test images, with an average resolution of 4872 × 6017. This dataset is created by compositing 395 different alpha mattes across 11 categories onto various backgrounds, all with high-quality manual annotation. Extensive experiments demonstrate that MEMatte outperforms existing methods on both high-resolution and real-world datasets, significantly reducing memory usage by approximately 88% and latency by 50% on the Composition-1K benchmark.

Kaiqing Lin, Yuzhen Lin, Weixiang Li, Taiping Yao, Bin Li

The proliferation of deepfake faces poses huge potential negative impacts on our daily lives. Despite substantial advancements in deepfake detection over these years, the generalizability of existing methods against forgeries from unseen datasets or created by emerging generative models remains constrained. In this paper, inspired by the zero-shot advantages of Vision-Language Models (VLMs), we propose a novel approach that repurposes a well-trained VLM for general deepfake detection. Motivated by the model reprogramming paradigm that manipulates the model prediction via input perturbations, our method can reprogram a pre-trained VLM model (e.g., CLIP) solely based on manipulating its input without tuning the inner parameters. First, learnable visual perturbations are used to refine feature extraction for deepfake detection. Then, we exploit information of face embedding to create sample-level adaptative text prompts, improving the performance. Extensive experiments on several popular benchmark datasets demonstrate that (1) the cross dataset and cross-manipulation performances of deepfake detection can be significantly and consistently improved (e.g., over 88% AUC in cross-dataset setting from FF++ to Wild-Deepfake); (2) the superior performances are achieved with fewer trainable parameters, making it a promising approach for real-world applications.

Sangbeom Lim, Seongchan Kim, Seungjun An, Seokju Cho, Paul Hongsuck Seo, Seungryong Kim

Current benchmarks for video segmentation are limited to annotating only salient objects (i.e., foreground instances). Despite their impressive architectural designs, previous works trained on these benchmarks have struggled to adapt to realworld scenarios. Thus, developing a new video segmentation dataset aimed at tracking multi-granularity segmentation target in the video scene is necessary. In this work, we aim to generate multi-granularity video segmentation dataset that is annotated for both salient and non-salient masks. To achieve this, we propose a large-scale, densely annotated multi-granularity video object segmentation (MUG-VOS) dataset that includes various types and granularities of mask annotations. We automatically collected a training set that assists in tracking both salient and non-salient objects, and we also curated a human-annotated test set for reliable evaluation. In addition, we present memory-based mask propagation model (MMPM), trained and evaluated on MUG-VOS dataset, which leads to the best performance among the existing video object segmentation methods and Segment SAM-based video segmentation methods.

Zixi Liang, Guowei Xu, Haifeng Wu, Ye Huang, Wen Li, Lixin Duan

Learning-based methods have become increasingly popular in 3D indoor scene synthesis (ISS), showing superior performance over traditional optimization-based approaches. These learning-based methods typically model distributions on simple yet explicit scene representations using generative models. However, due to the oversimplified explicit representations that overlook detailed information and the lack of guidance from multimodal relationships within the scene, most learning-based methods struggle to generate indoor scenes with realistic object arrangements and styles. In this paper, we introduce a new method, Scene Implicit Neural Field (S-INF), for indoor scene synthesis, aiming to learn meaningful representations of multimodal relationships, to enhance the realism of indoor scene synthesis. S-INF assumes that the scene layout is often related to the object-detailed information. It disentangles the multimodal relationships into scene layout relationships and detailed object relationships, fusing them later through implicit neural fields (INFs). By learning specialized scene layout relationships and projecting them into S-INF, we achieve a realistic generation of scene layout. Additionally, S-INF captures dense and detailed object relationships through differentiable rendering, ensuring stylistic consistency across objects. Through extensive experiments on the benchmark 3D-FRONT dataset, we demonstrate that our method consistently achieves state-of-the-art performance under different types of ISS.

Baoyu Liang, Qile Su, Shoutai Zhu, Yuchen Liang, Chao Tong

Despite the significant impact of visual events on human cognition, understanding events in videos remains a challenging task for AI due to their complex structures, semantic hierarchies, and dynamic evolution. To address this, we propose the task of video event understanding that extracts event scripts and makes predictions with these scripts from videos. To support this task, we introduce VidEvent, a large-scale dataset containing over 23,000 well-labeled events, featuring detailed event structures, broad hierarchies, and logical relations extracted from movie recap videos. The dataset was created through a meticulous annotation process, ensuring high-quality and reliable event data. We also provide comprehensive baseline models offering detailed descriptions of their architecture and performance metrics. These models serve as benchmarks for future research, facilitating comparisons and improvements. Our analysis of VidEvent and the baseline models highlights the dataset's potential to advance video event understanding and encourages the exploration of innovative algorithms and models.

Zixu Li, Zhiwei Chen, Haokun Wen, Zhiheng Fu, Yupeng Hu, Weili Guan

The objective of Composed Image Retrieval (CIR) is to identify a target image that meets the requirement based on a multimodal query (including the reference image and the modification text) provided by the user. Despite the notable success of existing approaches, they fail to adequately address the modification relation between visual entities and modification actions. This limitation is non-trivial due to three challenges: 1) irrelevant factor perturbation, 2) vague semantic boundaries, and 3) implicit modification relations. To address the above challenges, we propose an Entity miNing and modifiCation relatiOn binDing nEtwoRk (ENCODER), which has been designed to mine visual entities and modification actions, and then bind modification relations. Among the various components of the proposed ENCODER, we have initially designed the Latent Factor Filter (LFF) module to filter visual and textual latent factors related to modification semantics based on a threshold gating mechanism. Secondly, we propose Entity-Action Binding (EAB), which comprises modality-shared Learnable Relation Queries (LRQ) that are capable of mining visual entities and modification actions, as well as learning implicit modification relations for entity-action binding. Finally, the Multi-scale Composition module is introduced to achieve multi-scale feature composition, with guidance provided by entity-action binding. Extensive experiments on four benchmark datasets demonstrate the superiority of our proposed method.

Xudong Li, Yan Zhang, Yunhang Shen, Ke Li, Runze Hu, Xiawu Zheng, Sicheng Zhao

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 these high-level tasks inevitably introduce noise into the quality-aware features. In this paper, we take an initial step toward exploring the diffusion model for feature denoising in BIQA, namely Perceptual Feature Diffusion for IQA (PFD-IQA), which aims to remove noise from quality-aware features. Specifically, 1) we propose a Perceptual Prior Discovery and Aggregation module to establish two auxiliary tasks to discover potential low-level features in images that are used to aggregate perceptual textual prompt conditions for the diffusion model. 2) we propose a Perceptual Conditional Feature Refinement strategy, which matches noisy features to predefined denoising trajectories and then performs exact feature denoising based on textual prompt conditions. By incorporating a lightweight denoiser and requiring only a few feature denoising steps (e.g., just five iterations), our PFD-IQA framework achieves superior performance across eight standard BIQA datasets, validating its effectiveness.

Kun Li, Dan Guo, Guoliang Chen, Chunxiao Fan, Jingyuan Xu, Zhiliang Wu, Hehe Fan, Meng Wang

Micro-Action Recognition (MAR) has gained increasing attention due to its crucial role as a form of non-verbal communication in social interactions, with promising potential for applications in human communication and emotion analysis. However, current approaches often overlook the inherent ambiguity in micro-actions, which arises from the wide category range and subtle visual differences between categories. This oversight hampers the accuracy of micro-action recognition. In this paper, we propose a novel Prototypical Calibrating Ambiguous Network (PCAN) to unleash and mitigate the ambiguity of MAR. Firstly, we employ a hierarchical action-tree to identify the ambiguous sample, categorizing them into distinct sets of ambiguous samples of false negatives and false positives, considering both body- and action-level categories. Secondly, we implement an ambiguous contrastive refinement module to calibrate these ambiguous samples by regulating the distance between ambiguous samples and their corresponding prototypes. This calibration process aims to pull false negative (FN) samples closer to their respective prototypes and push false positive (FP) samples apart from their affiliated prototypes. In addition, we propose a new prototypical diversity amplification loss to strengthen the model's capacity by amplifying the differences between different prototypes. Finally, we propose a prototype-guided rectification to rectify prediction by incorporating the representability of prototypes. Extensive experiments conducted on the benchmark dataset demonstrate the superior performance of our method compared to existing approaches.

Jiafeng Li, Ying Wen, Lianghua He

As digital media manipulation becomes increasingly sophisticated, accurately detecting and localizing image forgeries with minimal supervision has become a critical challenge. Existing weakly supervised image forgery detection (W-IFD) methods often rely on convolutional neural networks (CNNs) and limited exploration of internal relationships, leading to poor detection and localization performance with only image-level labels. To address these limitations, we introduce a novel Multi-View and Multi-Level Relation Learning Network (M²RL-Net) for W-IFD. M²RL-Net effectively identifies forged images using only image-level annotations by exploring relationships between different views and hierarchical levels within images. Specifically, M²RL-Net achieves patch-level self-consistency learning (PSL) and feature-level contrastive learning (FCL) across different views, facilitating more generalized self-supervised learning of forgery features. In detail, PSL employs self-supervised learning to distinguish consistent and inconsistent regions within images, enhancing its ability to accurately locate tampered areas. FCL utilizes feature-level self-view and multi-view contrastive learning to differentiate between genuine and tampered image features, thereby improving the recognition of authentic and manipulated content across different views. Extensive experiments on various datasets demonstrate that M²RL-Net outperforms existing weakly-supervised methods in both detection and localization accuracy. This research sets a new benchmark for weakly-supervised image forgery detection and lays a robust foundation for future studies in this field.

Sangho Lee, Il Yong Chun, Hogun Park

Multi-modal transformers are rapidly gaining attention in video captioning tasks. Existing multi-modal video captioning methods extract a fixed number of frames, but this has critical challenges. If a limited number of frames are extracted, important frames with essential information for caption generation may be missed. Conversely, extracting an excessive number of frames includes consecutive frames, potentially causing redundancy in visual tokens extracted from consecutive video frames. To extract an appropriate number of frames for each video, this paper proposes the first model-agnostic module selection framework in video captioning that has two main functions: (1) selecting a caption generation module with an appropriate size based on visual tokens extracted from video frames, and (2) constructing subsets of visual tokens for the selected caption generation module. Furthermore, we propose a new adaptive attention masking scheme that enhances attention on important visual tokens. Our numerical experiments with three different benchmark datasets demonstrate that the proposed framework significantly improves the performances of three recent video captioning models.

Suruchi Kumari, Pravendra Singh

Medical image segmentation often faces the dual challenges of limited annotations and domain shifts, further complicated by degraded images in practical scenarios. Traditional methods tend to underperform when these issues occur simultaneously, as they are typically designed for specific tasks. To address this, we propose a unified framework that effectively handles limited annotations and domain shifts while also managing both clean and degraded images during inference. Overcoming these challenges requires focusing on three critical aspects: First, the model must be robust to various noise conditions. Second, it should excel at capturing domain-invariant features. Third, it should effectively utilize unlabeled data. We propose three major components in our approach to tackle these challenges. First, the Wavelet-based Cross-Component Exchange (WCCE) swaps high-frequency wavelet components between labeled and unlabeled images to enhance robustness. Second, we employ a diffusion VNet architecture with a reweighting mechanism to capture domain-invariant features. Finally, we utilize Cross-Decoder Pseudo (CDP) training to effectively leverage unlabeled data. Evaluations on three publicly available medical datasets and across four types of degraded image scenarios demonstrate that our method outperforms state-of-the-art (SOTA) techniques, consistently delivering superior performance across varying image qualities. Our approach not only addresses annotation scarcity and domain shift but also effectively manages noisy and blurred conditions, setting a new benchmark in medical image segmentation.

Akash Kumar, Sirshapan Mitra, Yogesh Singh Rawat

In this work, we focus on semi-supervised learning for video action detection. Video action detection requires spatio-temporal localization in addition to classification, and a limited amount of labels makes the model prone to unreliable predictions. We present Stable Mean Teacher, a simple end-to-end student-teacher-based framework that benefits from improved and temporally consistent pseudo labels. It relies on a novel ErrOr Recovery (EoR) module, which learns from students' mistakes on labeled samples and transfers this to the teacher to improve pseudo labels for unlabeled samples. Moreover, existing spatio-temporal losses do not take temporal coherency into account and are prone to temporal inconsistencies. To overcome this, we present Difference of Pixels (DoP), a simple and novel constraint focused on temporal consistency, which leads to coherent temporal detections. We evaluate our approach on four different spatio-temporal detection benchmarks: UCF101-24, JHMDB21, AVA, and Youtube-VOS. Our approach outperforms the supervised baselines for action detection by an average margin of 23.5% on UCF101-24, 16% on JHMDB21, and 3.3% on AVA. Using merely 10% and 20% of data, it provides a competitive performance compared to the supervised baseline trained on 100% annotations on UCF101-24 and JHMDB21 respectively. We further evaluate its effectiveness on AVA for scaling to large-scale datasets and Youtube-VOS for video object segmentation, demonstrating its generalization capability to other tasks in the video domain.