GenAI4ELab/papercli-papers huggingface dataset:
A Watermark-Conditioned Diffusion Model for IP Protection
Authors: Rui Min; Sen Li; Hongyang Chen; Minhao Cheng
Venue: ECCV 2024 (main)

Abstract: "The ethical need to protect AI-generated content has been a significant concern in recent years. While existing watermarking strategies have demonstrated success in detecting synthetic content (detection), there has been limited exploration in identifying the users responsible for generating these outputs from a single model (owner identification). In this paper, we focus on both practical scenarios and propose a unified watermarking framework for content copyright protection within the context of diffusion models. Specifically, we consider two parties: the model provider, who grants public access to a diffusion model via an API, and the users, who can solely query the model API and generate images in a black-box manner. Our task is to embed hidden information into the generated contents, which facilitates further detection and owner identification. To tackle this challenge, we propose a Watermark-conditioned Diff usion model called WaDiff, which manipulates the watermark as a conditioned input and incorporates fingerprinting into the generation process. All the generative outputs from our WaDiff carry user-specific information, which can be recovered by an image extractor and further facilitate forensic identification. Extensive experiments are conducted on two popular diffusion models, and we demonstrate that our method is effective and robust in both the detection and owner identification tasks. Meanwhile, our watermarking framework only exerts a negligible impact on the original generation and is more stealthy and efficient in comparison to existing watermarking strategies. Our code is publicly available at https: //github.com/rmin2000/WaDiff."
Finding NeMo: Negative-mined Mosaic Augmentation for Referring Image Segmentation
Authors: Seongsu Ha; Chaeyun Kim; Donghwa Kim; Junho Lee; Sangho Lee; Joonseok Lee
Venue: ECCV 2024 (main)

Abstract: "Referring Image Segmentation is a comprehensive task to segment an object referred by a textual query from an image. In nature, the level of difficulty in this task is affected by the existence of similar objects and the complexity of the referring expression. Recent RIS models still show a significant performance gap between easy and hard scenarios. We pose that the bottleneck exists in the data, and propose a simple but powerful data augmentation method, Negative-mined Mosaic Augmentation (NeMo). This method augments a training image into a mosaic with three other negative images carefully curated by a pretrained multimodal alignment model, e.g., CLIP, to make the sample more challenging. We discover that it is critical to properly adjust the difficulty level, neither too ambiguous nor too trivial. The augmented training data encourages the RIS model to recognize subtle differences and relationships between similar visual entities and to concretely understand the whole expression to locate the right target better. Our approach shows consistent improvements on various datasets and models, verified by extensive experiments."
SAFT: Towards Out-of-Distribution Generalization in Fine-Tuning
Authors: Bac Nguyen; Stefan Uhlich; Fabien Cardinaux; Lukas Mauch; Marzieh Edraki; Aaron Courville
Venue: ECCV 2024 (main)

Abstract: "Handling distribution shifts from training data, known as out-of-distribution (OOD) generalization, poses a significant challenge in the field of machine learning. While a pre-trained vision-language model like CLIP has demonstrated remarkable zero-shot performance, further adaptation of the model to downstream tasks leads to undesirable degradation for OOD data. In this work, we introduce Sparse Adaptation for Fine-Tuning (), a method that prevents fine-tuning from forgetting the general knowledge in the pre-trained model. only updates a small subset of important parameters whose gradient magnitude is large, while keeping the other parameters frozen. is straightforward to implement and conceptually simple. Extensive experiments show that with only 0.1% of the model parameters, can significantly improve the performance of CLIP. It consistently outperforms baseline methods across several benchmarks. On the few-shot learning benchmark of ImageNet and its variants, gives a gain of 5.15% on average over the conventional fine-tuning method in OOD settings."
FTBC: Forward Temporal Bias Correction for Optimizing ANN-SNN Conversion
Authors: Xiaofeng Wu; Velibor Bojkovic; Bin Gu; Kun Suo; Kai Zou
Venue: ECCV 2024 (main)

Abstract: "Spiking Neural Networks (SNNs) offer a promising avenue for energy-efficient computing compared with Artificial Neural Networks (ANNs), closely mirroring biological neural processes. However, this potential comes with inherent challenges in directly training SNNs through spatio-temporal backpropagation — stemming from the temporal dynamics of spiking neurons and their discrete signal processing — which necessitates alternative ways of training, most notably through ANN-SNN conversion. In this work, we introduce a lightweight Forward Temporal Bias Correction () technique, aimed at enhancing conversion accuracy without the computational overhead. We ground our method on provided theoretical findings that through proper temporal bias calibration the expected error of ANN-SNN conversion can be reduced to be zero after each time step. We further propose a heuristic algorithm for finding the temporal bias only in the forward pass, thus eliminating the computational burden of backpropagation and we evaluate our method on CIFAR-10/100 and ImageNet datasets, achieving a notable increase in accuracy on all datasets. Codes are released at a GitHub repository."
Improving Vision and Language Concepts Understanding with Multimodal Counterfactual Samples
Authors: Chengen Lai; Shengli Song; Sitong Yan; Guangneng Hu
Venue: ECCV 2024 (main)

Abstract: "Vision and Language (VL) models have achieved remarkable performance in a variety of multimodal learning tasks. The success of these models is attributed to learning a joint and aligned representation space of visual and text. However, recent popular VL models still struggle with concepts understanding beyond bag-of-objects in images & texts, suffering from compositional reasoning about relationship between objects & attributes and word order. To address the above issues, we create a synthetic multimodal counterfactual dataset (COCO-CF) and propose a novel contrastive learning framework (COMO). We contribute the COCO-CF dataset which is automatically generated from MS-COCO by injecting concepts from off-the-shelf language models and diffusion models to reduce the bias of bag-of-objects. We contribute the COMO framework for effectively leveraging COCO-CF to treat the counterfactual samples as hard negatives and reweight their importance during contrastive learning. Extensive experiments and ablations show COMO achieved a significant improvement of VL concept understanding on the two VL-Checklist and Winoground benchmarks over five strong VL baselines in their zero-shot setting evaluations. Dataset is available at https://github.com/laichengen/COMO."
Centering the Value of Every Modality: Towards Efficient and Resilient Modality-agnostic Semantic Segmentation
Authors: Xu Zheng; Yuanhuiyi Lyu; jiazhou zhou; Lin Wang
Venue: ECCV 2024 (main)

Abstract: "Fusing an arbitrary number of modalities is vital for achieving robust multi-modal fusion of semantic segmentation yet remains less explored to date. Recent endeavors regard RGB modality as the center and the others as the auxiliary, yielding an asymmetric architecture with two branches. However, the RGB modality may struggle in certain circumstances, , nighttime, while others, , event data, own their merits; thus, it is imperative for the fusion model to discern robust and fragile modalities, and incorporate the most robust and fragile ones to learn a resilient multi-modal framework. To this end, we propose a novel method, named MAGIC, that can be flexibly paired with various backbones, ranging from compact to high-performance models. Our method comprises two key plug-and-play modules. Firstly, we introduce a multi-modal aggregation module to efficiently process features from multi-modal batches and extract complementary scene information. On top, a unified arbitrary-modal selection module is proposed to utilize the aggregated features as the benchmark to rank the multi-modal features based on the similarity scores. This way, our method can eliminate the dependence on RGB modality and better overcome sensor failures while ensuring the segmentation performance. Under the commonly considered multi-modal setting, our method achieves state-of-the-art performance while reducing the model parameters by 60%. Moreover, our method is superior in the novel modality-agnostic setting, where it outperforms prior arts by a large margin of +19.41% mIoU."
GTPT: Group-based Token Pruning Transformer for Efficient Human Pose Estimation
Authors: Haonan Wang; Jie Liu; Jie Tang; Gangshan Wu; Bo Xu; Yanbing Chou; Yong Wang
Venue: ECCV 2024 (main)

Abstract: "In recent years, 2D human pose estimation has made significant progress on public benchmarks. However, many of these approaches face challenges of less applicability in the industrial community due to the large number of parametric quantities and computational overhead. Efficient human pose estimation remains a hurdle, especially for whole-body pose estimation with numerous keypoints. While most current methods for efficient human pose estimation primarily rely on CNNs, we propose the Group-based Token Pruning Transformer (GTPT) that fully harnesses the advantages of the Transformer. GTPT alleviates the computational burden by gradually introducing keypoints in a coarse-to-fine manner. It minimizes the computation overhead while ensuring high performance. Besides, GTPT groups keypoint tokens and prunes visual tokens to improve model performance while reducing redundancy. We propose the Multi-Head Group Attention (MHGA) between different groups to achieve global interaction with little computational overhead. We conducted experiments on COCO and COCO-WholeBody. Compared to other methods, the experimental results show that GTPT can achieve higher performance with less computation, especially in whole-body with numerous keypoints."
Lost in Translation: Modern Neural Networks Still Struggle With Small Realistic Image Transformations
Authors: Ofir Shifman; Yair Weiss
Venue: ECCV 2024 (main)

Abstract: "Deep neural networks that achieve remarkable performance in image classification have previously been shown to be easily fooled by tiny transformations such as a one pixel translation of the input image. In order to address this problem, two approaches have been proposed in recent years. The first approach suggests using huge datasets together with data augmentation in the hope that a highly varied training set will teach the network to learn to be invariant. The second approach suggests using architectural modifications based on sampling theory to deal explicitly with image translations. In this paper, we show that these approaches still fall short in robustly handling ’natural’ image translations that simulate a subtle change in camera orientation. Our findings reveal that a mere one-pixel translation can result in a significant change in the predicted image representation for approximately 40% of the test images in state-of-the-art models (e.g. open-CLIP trained on LAION-2B or DINO-v2) , while models that are explicitly constructed to be robust to cyclic translations can still be fooled with 1 pixel realistic (non-cyclic) translations 11% of the time. We present Robust Inference by Crop Selection: a simple method that can be proven to achieve any desired level of consistency, although with a modest tradeoff with the model’s accuracy. Importantly, we demonstrate how employing this method reduces the ability to fool state-of-the-art models with a 1 pixel translation to less than 5% while suffering from only a 1% drop in classification accuracy. Additionally, we show that our method can be easily adjusted to deal with circular shifts as well. In such a case we achieve 100% robustness to integer shifts with state-of-the-art accuracy, and with no need for any further training. Code is available at: https://github.com/ofirshifman/RICS."
DIAL: Dense Image-text ALignment for Weakly Supervised Semantic Segmentation
Authors: Soojin Jang; JungMin Yun; JuneHyoung Kwon; Eunju Lee; YoungBin Kim
Venue: ECCV 2024 (main)

Abstract: "Weakly supervised semantic segmentation (WSSS) approaches typically rely on class activation maps (CAMs) for initial seed generation, which often fail to capture global context due to limited supervision from image-level labels. To address this issue, we introduce DALNet, Dense Alignment Learning Network that leverages text embeddings to enhance the comprehensive understanding and precise localization of objects across different levels of granularity. Our key insight is to employ a dual-level alignment strategy: (1) Global Implicit Alignment (GIA) to capture global semantics by maximizing the similarity between the class token and the corresponding text embeddings while minimizing the similarity with background embeddings, and (2) Local Explicit Alignment (LEA) to improve object localization by utilizing spatial information from patch tokens. Moreover, we propose a cross-contrastive learning approach that aligns foreground features between image and text modalities while separating them from the background, encouraging activation in missing regions and suppressing distractions. Through extensive experiments on the PASCAL VOC and MS COCO datasets, we demonstrate that DALNet significantly outperforms state-of-the-art WSSS methods. Our approach, in particular, allows for more efficient end-to-end process as a single-stage method."
Rethinking Normalization Layers for Domain Generalizable Person Re-identification
Authors: Ren Nie; Jin Ding; Xue Zhou; Xi Li
Venue: ECCV 2024 (main)

Abstract: "Domain Generalizable Person Re-Identification (DG-ReID) strives to transfer learned feature representation from source domains to unseen target domains, despite significant distribution shifts. While most existing methods enhance model generalization and discriminative feature extraction capability by introducing Instance Normalization (IN) in combination with Batch Normalization (BN), these approaches still struggle with the overfitting of normalization layers to the source domains, posing challenges in domain generalization. To address this issue, we propose ReNorm, a purely normalization-based framework that integrates two complementary normalization layers through two forward propagations for the same weight matrix. In the first forward propagation, Remix Normalization (RN) combines IN and BN in a concise manner to ensure the feature extraction capability. As an effective complement to RN, Emulation Normalization (EN) simulates the testing process of RN, implicitly mitigating the domain shifts caused by the absence of target domain information and actively guiding the model in learning how to generalize the feature extraction capability to unseen target domains. Meanwhile, we propose Domain Frozen (DF), freezing updates to affine parameters to reduce the impact of these less robust parameters on overfitting to the source domains. Extensive experiments show that our framework achieves state-of-the-art performance on the popular benchmarks. The code is available at https://github.com/3699nr/ReNorm."
Generalizing to Unseen Domains via Text-guided Augmentation
Authors: Daiqing Qi; Handong Zhao; Aidong Zhang; Sheng Li
Venue: ECCV 2024 (main)

Abstract: "To avoid the high cost of collecting visual data from all test domains in the domain adaptation task, recent work takes advantage of the pre-trained large-scale vision language models and augment training data with only text descriptions (e.g.,“a photo/painting/sketch...”) of each test domain. However, in many real-world applications, such text information of test domains is not always available in advance. Moreover, even if we can verbalize all test domains, it is laborious for existing work [?] to train a different augmentation network for each possible unseen domain, which suffers from time-inefficiency. To overcome these challenges, we benefit from the multimodal embedding space of a pre-trained vision-language model and propose to acquire training-free and domain-invariant augmentations with text descriptions of arbitrary crafted unseen domains, which not necessarily match test domains. Beyond achieving state-of-the-art results, compared with existing works that require trainable augmentation networks, our approach is also notably more time-efficient, and exhibits a more solid theoretical support."
VCP-CLIP: A visual context prompting model for zero-shot anomaly segmentation
Authors: Zhen Qu; Xian Tao; Mukesh Prasad; Fei Shen; Zhengtao Zhang; Xinyi Gong; Guiguang Ding
Venue: ECCV 2024 (main)

Abstract: "Recently, large-scale vision-language models such as CLIP have demonstrated immense potential in zero-shot anomaly segmentation (ZSAS) task, utilizing a unified model to directly detect anomalies on any unseen product with painstakingly crafted text prompts. However, existing methods often assume that the product category to be inspected is known, thus setting product-specific text prompts, which is difficult to achieve in the data privacy scenarios. Moreover, even the same type of product exhibits significant differences due to specific components and variations in the production process, posing significant challenges to the design of text prompts. In this end, we propose a visual context prompting model (VCP-CLIP) for ZSAS task based on CLIP. The insight behind VCP-CLIP is to employ visual context prompting to activate CLIP’s anomalous semantic perception ability. In specific, we first design a Pre-VCP module to embed global visual information into the text prompt, thus eliminating the necessity for product-specific prompts. Then, we propose a novel Post-VCP module, that adjusts the text embeddings utilizing the fine-grained features of the images. In extensive experiments conducted on 10 real-world industrial anomaly segmentation datasets, VCP-CLIP achieved state-of-the-art performance in ZSAS task. The code is available at https://github.com/xiaozhen228/VCP-CLIP."
Lost in Translation: Latent Concept Misalignment in Text-to-Image Diffusion Models
Authors: Juntu Zhao; Junyu Deng; Yixin Ye; Chongxuan Li; Zhijie Deng; Dequan Wang
Venue: ECCV 2024 (main)

Abstract: "Advancements in text-to-image diffusion models have broadened extensive downstream practical applications, but such models often encounter misalignment issues between text and image. Taking the generation of a combination of two disentangled concepts as an example, say given the prompt a tea cup of iced coke, existing models usually generate a glass cup of iced coke because the iced coke usually co-occurs with the glass cup instead of the tea one during model training. The root of such misalignment is attributed to the confusion in the latent semantic space of text-to-image diffusion models, and hence we refer to the a tea cup of iced coke phenomenon as Latent Concept Misalignment (LC-Mis). We leverage large language models (LLMs) to thoroughly investigate the scope of LC-Mis, and develop an automated pipeline for aligning the latent semantics of diffusion models to text prompts. Empirical assessments confirm the effectiveness of our approach, substantially reducing LC-Mis errors and enhancing the robustness and versatility of text-to-image diffusion models. Our code and dataset have been available online for reference."
Crowd-SAM:SAM as a smart annotator for object detection in crowded scenes
Authors: Zhi Cai; Yingjie Gao; Yaoyan Zheng; Nan Zhou; Di Huang
Venue: ECCV 2024 (main)

Abstract: "Object detection is an important task that finds its application in a wide range of scenarios. Generally, it requires extensive labels for training, which is quite time-consuming, especially in crowded scenes. Recently, Segment Anything Model (SAM) has emerged as a powerful zero-shot segmenter, offering a novel approach to instance segmentation. However, the accuracy and efficiency of SAM and its variants are often compromised when handling objects in crowded scenes where occlusions often appear. In this paper, we propose Crowd-SAM, a SAM-based framework designed to enhance the performance of SAM in crowded scenes with the cost of few learnable parameters and minimal labeled images. We introduce an efficient prompt sampler (EPS) and a part-whole discrimination network (PWD-Net), facilitating mask selection and contributing to an improvement in accuracy in crowded scenes. Despite its simplicity, Crowd-SAM rivals state-of-the-art fully-supervised object detection methods on several benchmarks including CrowdHuman and CityPersons. Our code is available at https://github.com/FelixCaae/CrowdSAM."
Zero-shot Text-guided Infinite Image Synthesis with LLM guidance
Authors: Soyeong Kwon; Taegyeong Lee; Taehwan Kim
Venue: ECCV 2024 (main)

Abstract: "Text-guided image editing and generation methods have diverse real-world applications. However, text-guided infinite image synthesis faces several challenges. First, there is a lack of text-image paired datasets with high-resolution and contextual diversity. Second, expanding images based on text requires global coherence and rich local context understanding. Previous studies have mainly focused on limited categories, such as natural landscapes, and also required to train on high-resolution images with paired text. To address these challenges, we propose a novel approach utilizing Large Language Models (LLMs) for both global coherence and local context understanding, without any high-resolution text-image paired training dataset. We train the diffusion model to expand an image conditioned on global and local captions generated from the LLM and visual feature. At the inference stage, given an image and a global caption, we use the LLM to generate a next local caption to expand the input image. Then, we expand the image using the global caption, generated local caption and the visual feature to consider global consistency and spatial local context. In experiments, our model outperforms the baselines both quantitatively and qualitatively. Furthermore, our model demonstrates the capability of text-guided arbitrary-sized image generation in zero-shot manner with LLM guidance."