GenAI4ELab/papercli-papers huggingface dataset:
Forensics Adapter: Adapting CLIP for Generalizable Face Forgery Detection
Authors: Xinjie Cui; Yuezun Li; Ao Luo; Jiaran Zhou; Junyu Dong
Venue: CVPR 2025 (main)

Abstract: We describe the Forensics Adapter, an adapter network designed to transform CLIP into an effective and generalizable face forgery detector. Although CLIP is highly versatile, adapting it for face forgery detection is non-trivial as forgery-related knowledge is entangled with a wide range of unrelated knowledge. Existing methods treat CLIP merely as a feature extractor, lacking task-specific adaptation, which limits their effectiveness. To address this, we introduce an adapter to learn face forgery traces -- the blending boundaries unique to forged faces, guided by task-specific objectives. Then we enhance the CLIP visual tokens with a dedicated interaction strategy that communicates knowledge across CLIP and the adapter. Since the adapter is alongside CLIP, its versatility is highly retained, naturally ensuring strong generalizability in face forgery detection. With only 5.7M trainable parameters, our method achieves a significant performance boost, improving by approximately 7% on average across five standard datasets. We believe the proposed method can serve as a baseline for future CLIP-based face forgery detection methods.
KAC: Kolmogorov-Arnold Classifier for Continual Learning
Authors: Yusong Hu; Zichen Liang; Fei Yang; Qibin Hou; Xialei Liu; Ming-Ming Cheng
Venue: CVPR 2025 (main)

Abstract: Continual learning requires models to train continuously across consecutive tasks without forgetting. Most existing methods utilize linear classifiers, which struggle to maintain a stable classification space while learning new tasks. Inspired by the success of Kolmogorov-Arnold Networks (KAN) in preserving learning stability during simple continual regression tasks, we set out to explore their potential in more complex continual learning scenarios. In this paper, we introduce the Kolmogorov-Arnold Classifier (KAC), a novel classifier developed for continual learning based on the KAN structure. We delve into the impact of KAN's spline functions and introduce Radial Basis Functions (RBF) for improved compatibility with continual learning. We replace linear classifiers with KAC in several recent approaches and conduct experiments across various continual learning benchmarks, all of which demonstrate performance improvements, highlighting the effectiveness and robustness of KAC in continual learning.
PI-HMR: Towards Robust In-bed Temporal Human Shape Reconstruction with Contact Pressure Sensing
Authors: Ziyu Wu; Yufan Xiong; Mengting Niu; Fangting Xie; Quan Wan; Qijun Ying; Boyan Liu; Xiaohui Cai
Venue: CVPR 2025 (main)

Abstract: Long-term in-bed monitoring benefits automatic and real-time health management within healthcare, and the advancement of human shape reconstruction technologies further enhances the representation and visualization of users' activity patterns. However, existing technologies are primarily based on visual cues, facing serious challenges in non-light-of-sight and privacy-sensitive in-bed scenes. Pressure-sensing bedsheets offer a promising solution for real-time motion reconstruction. Yet, limited exploration in model designs and data have hindered its further development. To tackle these issues, we propose a general framework that bridges gaps in data annotation and model design. Firstly, we introduce SMPLify-IB, an optimization method that overcomes the depth ambiguity issue in top-view scenarios through gravity constraints, enabling generating high-quality 3D human shape annotations for in-bed datasets. Then we present PI-HMR, a temporal-based human shape estimator to regress meshes from pressure sequences. By integrating multi-scale feature fusion with high-pressure distribution and spatial position priors, PI-HMR outperforms SOTA methods with 17.01mm Mean-Per-Joint-Error decrease. This work provides a whole tool-chain to support the development of in-bed monitoring with pressure contact sensing.
BOOTPLACE: Bootstrapped Object Placement with Detection Transformers
Authors: Hang Zhou; Xinxin Zuo; Rui Ma; Li Cheng
Venue: CVPR 2025 (main)

Abstract: In this paper, we tackle the copy-paste image-to-image composition problem with a focus on object placement learning. Prior methods have leveraged generative models to reduce the reliance for dense supervision. However, this often limits their capacity to model complex data distributions. Alternatively, transformer networks with a sparse contrastive loss have been explored, but their over-relaxed regularization often leads to imprecise object placement. We introduce BOOTPLACE, a novel paradigm that formulates object placement as a placement-by-detection problem. Our approach begins by identifying suitable regions of interest for object placement. This is achieved by training a specialized detection transformer on object-subtracted backgrounds, enhanced with multi-object supervisions. It then semantically associates each target compositing object with detected regions based on their complementary characteristics. Through a boostrapped training approach applied to randomly object-subtracted images, our model enforces meaningful placements through extensive paired data augmentation. Experimental results on established benchmarks demonstrate BOOTPLACE's superior performance in object repositioning, markedly surpassing state-of-the-art baselines on Cityscapes and OPA datasets with notable improvements in IOU scores. Additional ablation studies further showcase the compositionality and generalizability of our approach, supported by user study evaluations. Code is available at https://github.com/RyanHangZhou/BOOTPLACE
CheckManual: A New Challenge and Benchmark for Manual-based Appliance Manipulation
Authors: Yuxing Long; Jiyao Zhang; Mingjie Pan; Tianshu Wu; Taewhan Kim; Hao Dong
Venue: CVPR 2025 (main)

Abstract: Correct use of electrical appliances has significantly improved human life quality. Unlike simple tools that can be manipulated with common sense, different parts of electrical appliances have specific functions defined by manufacturers. If we want the robot to heat bread by microwave, we should enable them to review the microwave's manual first. From the manual, it can learn about component functions, interaction methods, and representative task steps about appliances. However, previous manual-related works remain limited to question-answering tasks while existing manipulation researchers ignore the manual's important role and fail to comprehend multi-page manuals. In this paper, we propose the first manual-based appliance manipulation benchmark CheckManual. Specifically, we design a large model-assisted human-revised data generation pipeline to create manuals based on CAD appliance models. With these manuals, we establish novel manual-based manipulation challenges, metrics, and simulator environments for model performance evaluation. Furthermore, we propose the first manual-based manipulation planning model ManualPlan to set up a group of baselines for the CheckManual benchmark.
CXPMRG-Bench: Pre-training and Benchmarking for X-ray Medical Report Generation on CheXpert Plus Dataset
Authors: Xiao Wang; Fuling Wang; Yuehang Li; Qingchuan Ma; Shiao Wang; Bo Jiang; Jin Tang
Venue: CVPR 2025 (main)

Abstract: X-ray image-based medical report generation (MRG) is a pivotal area in artificial intelligence that can significantly reduce diagnostic burdens and patient wait times. Despite significant progress, we believe that the task has reached a bottleneck due to the limited benchmark datasets and the existing large models' insufficient capability enhancements in this specialized domain. Specifically, the recently released CheXpert Plus dataset lacks comparative evaluation algorithms and their results, providing only the dataset itself. This situation makes the training, evaluation, and comparison of subsequent algorithms challenging. Thus, we conduct a comprehensive benchmarking of existing mainstream X-ray report generation models and large language models (LLMs), on the CheXpert Plus dataset. We believe that the proposed benchmark can provide a solid comparative basis for subsequent algorithms and serve as a guide for researchers to quickly grasp the state-of-the-art models in this field. More importantly, we propose a large model for the X-ray image report generation using a multi-stage pre-training strategy, including self-supervised autoregressive generation and Xray-report contrastive learning, and supervised fine-tuning. Extensive experimental results indicate that the autoregressive pre-training based on Mamba effectively encodes X-ray images, and the image-text contrastive pre-training further aligns the feature spaces, achieving better experimental results.
FASTer: Focal token Acquiring-and-Scaling Transformer for Long-term 3D Objection Detection
Authors: Chenxu Dang; ZaiPeng Duan; Pei An; Xinmin Zhang; Xuzhong Hu; Jie Ma
Venue: CVPR 2025 (main)

Abstract: Recent top-performing temporal 3D detectors based on Lidars have increasingly adopted region-based paradigms. They first generate coarse proposals, followed by encoding and fusing regional features. However, indiscriminate sampling and fusion often overlook the varying contributions of individual points and lead to exponentially increased complexity as the number of input frames grows. Moreover, arbitrary result-level concatenation limits the global information extraction. In this paper, we propose a Focal Token Acquring-and-Scaling Transformer (FASTer), which dynamically selects focal tokens and condenses token sequences in an adaptive and lightweight manner. Emphasizing the contribution of individual tokens, we propose a simple but effective Adaptive Scaling mechanism to capture geometric contexts while sifting out focal points. Adaptively storing and processing only focal points in historical frames dramatically reduces the overall complexity. Furthermore, a novel Grouped Hierarchical Fusion strategy is proposed, progressively performing sequence scaling and Intra-Group Fusion operations to facilitate the exchange of global spatial and temporal information. Experiments on the Waymo Open Dataset demonstrate that our FASTer significantly outperforms other state-of-the-art detectors in both performance and efficiency while also exhibiting improved flexibility and robustness. The code is available at https://github.com/MSunDYY/FASTer.git.
SEEN-DA: SEmantic ENtropy guided Domain-aware Attention for Domain Adaptive Object Detection
Authors: Haochen Li; Rui Zhang; Hantao Yao; Xin Zhang; Yifan Hao; Xinkai Song; Shaohui Peng; Yongwei Zhao; Chen Zhao; Yanjun Wu; Ling Li
Venue: CVPR 2025 (main)

Abstract: Domain adaptive object detection (DAOD) aims to generalize detectors trained on an annotated source domain to an unlabelled target domain. Traditional works focus on aligning visual features between domains to extract domain-invariant knowledge, and recent VLM-based DAOD methods leverage semantic information provided by the textual encoder to supplement domain-specific features for each domain.However, they overlook the role of semantic information in guiding the learning of visual features that are beneficial for adaptation.To solve the problem, we propose semantic entropy to quantify the semantic information contained in visual features, and design SEmantic ENtropy guided Domain-aware Attention (SEEN-DA) to adaptively refine visual features with the semantic information of two domains.Semantic entropy reflects the importance of features based on semantic information, which can serve as attention to select discriminative visual features and suppress semantically irrelevant redundant information.Guided by semantic entropy, we introduce domain-aware attention modules into the visual encoder in SEEN-DA.It utilizes an inter-domain attention branch to extract domain-invariant features and eliminate redundant information, and an intra-domain attention branch to supplement the domain-specific semantic information discriminative on each domain.Comprehensive experiments validate the effectiveness of SEEN-DA, demonstrating significant improvements in cross-domain object detection performance.
Event-Equalized Dense Video Captioning
Authors: Kangyi Wu; Pengna Li; Jingwen Fu; Yizhe Li; Yang Wu; Yuhan Liu; Jinjun Wang; Sanping Zhou
Venue: CVPR 2025 (main)

Abstract: Dense video captioning aims to localize and caption all events in arbitrary untrimmed videos. Although previous methods have achieved appealing results, they still face the issue of temporal bias, i.e, models tend to focus more on events with certain temporal characteristics. Specifically, 1) the temporal distribution of events in training datasets is uneven. Models trained on these datasets will pay less attention to out-of-distribution events. 2) long-duration events have more frame features than short ones and will attract more attention. To address this, we argue that events, with varying temporal characteristics, should be treated equally when it comes to dense video captioning. Intuitively, different events tend to have distinct visual differences due to varied camera views, backgrounds, or subjects. Inspired by that, we intend to utilize visual features to have an approximate perception of possible events and pay equal attention to them. In this paper, we introduce a simple but effective framework, called Event-Equalized Dense Video Captioning(E^2DVC) to overcome the temporal bias and treat all possible events equally. Specifically, an event perception module(EPM) is proposed to do uneven clustering on visual frame features to generate pseudo-events. We enforce the model's attention to these pseudo-events through the pseudo-event initialization module(PEI). A novel event-enhanced encoder(EEE) is also devised to enhance the model's ability to explore frame-frame and frame-event relationships. Experimental results validate the effectiveness of the proposed methods.
Geometry-guided Online 3D Video Synthesis with Multi-View Temporal Consistency
Authors: Hyunho Ha; Lei Xiao; Christian Richardt; Thu Nguyen-Phuoc; Changil Kim; Min H. Kim; Douglas Lanman; Numair Khan
Venue: CVPR 2025 (main)

Abstract: We introduce a novel geometry-guided online video view synthesis method with enhanced view and temporal consistency. Traditional approaches achieve high-quality synthesis from dense multi-view camera setups but require significant computational resources. In contrast, selective-input methods reduce this cost but often compromise quality, leading to multi-view and temporal inconsistencies such as flickering artifacts. Our method addresses this challenge to deliver efficient, high-quality novel-view synthesis with view and temporal consistency. The key innovation of our approach lies in using global geometry to guide an image-based rendering pipeline. To accomplish this, we progressively refine depth maps using color difference masks across time. These depth maps are then accumulated through truncated signed distance fields in the synthesized view's image space. This depth representation is view and temporally consistent, and is used to guide a pre-trained blending network that fuses multiple forward-rendered input-view images. Thus, the network is encouraged to output geometrically consistent synthesis results across multiple views and time. Our approach achieves consistent, high-quality video synthesis, while running efficiently in an online manner.
Point2RBox-v2: Rethinking Point-supervised Oriented Object Detection with Spatial Layout Among Instances
Authors: Yi Yu; Botao Ren; Peiyuan Zhang; Mingxin Liu; Junwei Luo; Shaofeng Zhang; Feipeng Da; Junchi Yan; Xue Yang
Venue: CVPR 2025 (main)

Abstract: With the rapidly increasing demand for oriented object detection (OOD), recent research involving weakly-supervised detectors for learning OOD from point annotations has gained great attention. In this paper, we rethink this challenging task setting with the layout among instances and present Point2RBox-v2. At the core are three principles: 1) Gaussian overlap loss. It learns an upper bound for each instance by treating objects as 2D Gaussian distributions and minimizing their overlap. 2) Voronoi watershed loss. It learns a lower bound for each instance through watershed on Voronoi tessellation. 3) Consistency loss. It learns the size/rotation variation between two output sets with respect to an input image and its augmented view. Supplemented by a few devised techniques, e.g. edge loss and copy-paste, the detector is further enhanced. To our best knowledge, Point2RBox-v2 is the first approach to explore the spatial layout among instances for learning point-supervised OOD. Our solution is elegant and lightweight, yet it is expected to give a competitive performance especially in densely packed scenes: 62.61%/86.15%/34.71% on DOTA/HRSC/FAIR1M.
LibraGrad: Balancing Gradient Flow for Universally Better Vision Transformer Attributions
Authors: Faridoun Mehri; Mahdieh Soleymani Baghshah; Mohammad Taher Pilehvar
Venue: CVPR 2025 (main)

Abstract: Why do gradient-based explanations struggle with Transformers, and how can we improve them? We identify gradient flow imbalances in Transformers that violate FullGrad-completeness, a critical property for attribution faithfulness that CNNs naturally possess. To address this issue, we introduce LibraGrad--a theoretically grounded post-hoc approach that corrects gradient imbalances through pruning and scaling of backward paths, without changing the forward pass or adding computational overhead. We evaluate LibraGrad using three metric families: Faithfulness, which quantifies prediction changes under perturbations of the most and least relevant features; Completeness Error, which measures attribution conservation relative to model outputs; and Segmentation AP, which assesses alignment with human perception. Extensive experiments across 8 architectures, 4 model sizes, and 5 datasets show that LibraGrad universally enhances gradient-based methods, outperforming existing white-box methods--including Transformer-specific approaches--across all metrics. We demonstrate superior qualitative results through two complementary evaluations: precise text-prompted region highlighting on CLIP models and accurate class discrimination between co-occurring animals on ImageNet-finetuned models--two settings on which existing methods often struggle. LibraGrad is effective even on the attention-free MLP-Mixer architecture, indicating potential for extension to other modern architectures. Our code is freely available at https://nightmachinery.github.io/LibraGrad/.
Blind Bitstream-corrupted Video Recovery via Metadata-guided Diffusion Model
Authors: Shuyun Wang; Hu Zhang; Xin Shen; Dadong Wang; Xin Yu
Venue: CVPR 2025 (main)

Abstract: Bitstream-corrupted video recovery aims to fill in realistic video content due to bitstream corruption during video storage or transmission. Most existing methods typically assume that the predefined masks of the corrupted regions are known in advance. However, manually annotating these masks is laborious and time-consuming, limiting the applicability of existing methods in real-world scenarios. Therefore, we expect to relax this assumption by defining a new blind video recovery setting where the recovery of corrupted regions does not rely on predefined masks. There are two significant challenges in this setting: (i) without predefined masks, how accurately can a model identify the regions requiring recovery? (ii) how to recover contents from extensive and irregular regions, especially when large portions of frames are severely degraded? To address these challenges, we introduce a Metadata-Guided Diffusion Model, dubbed M-GDM. To enable a diffusion model focusing on the corrupted regions, we leverage intrinsic video metadata as a corruption indicator and design a dual-stream metadata encoder. This encoder first embeds the motion vectors and frame types of a video separately and then merges them into a unified metadata representation. The metadata representation will interact with the corrupted latent feature through cross-attention mechanisms at each diffusion step. Meanwhile, to preserve the intact regions, we propose a prior-driven mask predictor that generates pseudo masks for the corrupted regions by leveraging the metadata prior and diffusion prior. These pseudo masks enable the separation and recombination of intact and recovered regions through hard masking. However, imperfections in pseudo mask predictions and hard masking processes often result in boundary artifacts. Thus, we introduce a post-refinement module that refines the hard-masked outputs, enhancing the consistency between intact and recovered regions. Extensive experiment results validate the effectiveness of our method and demonstrate its superiority in the blind video recovery task.
Mind the Trojan Horse: Image Prompt Adapter Enabling Scalable and Deceptive Jailbreaking
Authors: Junxi Chen; Junhao Dong; Xiaohua Xie
Venue: CVPR 2025 (main)

Abstract: Recently, the Image Prompt Adapter (IP-Adapter) has been increasingly integrated into text-to-image diffusion models (T2I-DMs) to improve controllability. However, in this paper, we reveal that T2I-DMs equipped with the IP-Adapter (T2I-IP-DMs) enable a new jailbreak attack named the hijacking attack. We demonstrate that, by uploading imperceptible image-space adversarial examples (AEs), the adversary can hijack massive benign users to jailbreak an Image Generation Service (IGS) driven by T2I-IP-DMs and mislead the public to discredit the service provider. Worse still, the IP-Adapter's dependency on open-source image encoders reduces the knowledge required to craft AEs. Extensive experiments verify the technical feasibility of the hijacking attack. In light of the revealed threat, we investigate several existing defenses and explore combining the IP-Adapter with adversarially trained models to overcome existing defenses' limitations. Our code is available at https://github.com/fhdnskfbeuv/attackIPA.
Lost in Translation, Found in Context: Sign Language Translation with Contextual Cues
Authors: Youngjoon Jang; Haran Raajesh; Liliane Momeni; Gül Varol; Andrew Zisserman
Venue: CVPR 2025 (main)

Abstract: Our objective is to translate continuous sign language into spoken language text. Inspired by the way human interpreters rely on context for accurate translation, we incorporate additional contextual cues together with the signing video, into a new translation framework. Specifically, besides visual sign recognition features that encode the input video, we integrate complementary textual information from (i) captions describing the background show, (ii) translation of previous sentences, as well as (iii) pseudo-glosses transcribing the signing. These are automatically extracted and inputted along with the visual features to a pre-trained large language model (LLM), which we fine-tune to generate spoken language translations in text form. Through extensive ablation studies, we show the positive contribution of each input cue to the translation performance. We train and evaluate our approach on BOBSL -- the largest British Sign Language dataset currently available. We show that our contextual approach significantly enhances the quality of the translations compared to previously reported results on BOBSL, and also to state-of-the-art methods that we implement as baselines. Furthermore, we demonstrate the generality of our approach by applying it also to How2Sign, an American Sign Language dataset, and achieve competitive results.