GenAI4ELab/papercli-papers huggingface dataset:
Improving Zero-Shot Generalization for CLIP with Variational Adapter
Authors: Ziqian Lu; Fengli Shen; Mushui Liu; Yunlong Yu; Xi Li
Venue: ECCV 2024 (main)

Abstract: "The excellent generalization capability of pre-trained Vision-Language Models (VLMs) makes fine-tuning VLMs for downstream zero-shot tasks a popular choice. Despite achieving promising performance in the professionality of base classes, most existing fine-tuned methods suffer from feature confusion of novel classes, resulting in unsatisfactory transferability. To address this problem, we propose a divide-and-conquer approach called Prompt-based Variational Adapter (PVA) that explicitly reduces the prediction bias by separating base and novel samples. Specifically, we design two variational adapters with learnable textual tokens to align latent representations for each modality in a shared latent space. Once trained, we can separate novel samples from entangled space using the similarity metric of latent features, i.e., converting confusion task into two independent ones (One for base classes and the other for novel classes). Moreover, to improve the transferability for novel classes, we further refine the output features of the learned adapters with the global features via a residual connection. We conduct extensive experiments on Generalized Zero-Shot Learning and Cross-Dataset Transfer Learning to demonstrate the superiority of our approach and establish a new state-of-the-art on four popular benchmarks."
Realistic Human Motion Generation with Cross-Diffusion Models
Authors: Zeping Ren; Shaoli Huang; Xiu Li
Venue: ECCV 2024 (main)

Abstract: "In this work, we introduce the Cross Human Motion Diffusion Model (CrossDiff1 ), a novel approach for generating high-quality human motion based on textual descriptions. Our method integrates 3D and 2D information using a shared transformer network within the training of the diffusion model, unifying motion noise into a single feature space. This enables cross-decoding of features into both 3D and 2D motion representations, regardless of their original dimension. The primary advantage of CrossDiff is its cross-diffusion mechanism, which allows the model to reverse either 2D or 3D noise into clean motion during training. This capability leverages the complementary information in both motion representations, capturing intricate human movement details often missed by models relying solely on 3D information. Consequently, CrossDiff effectively combines the strengths of both representations to generate more realistic motion sequences. In our experiments, our model demonstrates competitive state-of-the-art performance on text-to-motion benchmarks. Moreover, our method consistently provides enhanced motion generation quality, capturing complex full-body movement intricacies. Additionally, with a pre-trained model, our approach accommodates using in-the-wild 2D motion data without 3D motion ground truth during training to generate 3D motion, highlighting its potential for broader applications and efficient use of available data resources. 1 https://wonderno.github.io/CrossDiff-webpage/"
EgoExo-Fitness: Towards Egocentric and Exocentric Full-Body Action Understanding
Authors: Yuan-Ming Li; Wei-Jin Huang; An-Lan Wang; Ling-An Zeng; Jing-Ke Meng; Wei-Shi Zheng
Venue: ECCV 2024 (main)

Abstract: "We present EgoExo-Fitness, a new full-body action understanding dataset, featuring fitness sequence videos recorded from synchronized egocentric and fixed exocentric (third-person) cameras. Compared with existing full-body action understanding datasets, EgoExo-Fitness not only contains videos from first-person perspectives, but also provides rich annotations. Specifically, two-level temporal boundaries are provided to localize single action videos along with sub-steps of each action. More importantly, EgoExo-Fitness introduces innovative annotations for interpretable action judgement–including technical keypoint verification, natural language comments on action execution, and action quality scores. Combining all of these, EgoExo-Fitness provides new resources to study egocentric and exocentric full-body action understanding across dimensions of “what”, “when”, and “how well”. To facilitate research on egocentric and exocentric full-body action understanding, we construct benchmarks on a suite of tasks (, action classification, action localization, cross-view sequence verification, cross-view skill determination, and a newly proposed task of guidance-based execution verification), together with detailed analysis. Data and code are available at https://github.com/iSEE-Laboratory/EgoExo-Fitness/tree/main."
Any Target Can be Offense: Adversarial Example Generation via Generalized Latent Infection
Authors: Youheng Sun; Shengming Yuan; Xuanhan Wang; Lianli Gao; Jingkuan Song
Venue: ECCV 2024 (main)

Abstract: "Targeted adversarial attack, which aims to mislead a model to recognize any image as a target object by imperceptible perturbations, has become a mainstream tool for vulnerability assessment of deep neural networks (DNNs). Since existing targeted attackers only learn to attack known target classes, they cannot generalize well to unknown classes. To tackle this issue, we propose Generalized Adversarial attacKER (GAKer), which is able to construct adversarial examples to any target class. The core idea behind GAKer is to craft a latently infected representation during adversarial example generation. To this end, the extracted latent representations of the target object are first injected into intermediate features of an input image in an adversarial generator. Then, the generator is optimized to ensure visual consistency with the input image while being close to the target object in the feature space. Since the GAKer is class-agnostic yet model-agnostic, it can be regarded as a general tool that not only reveals the vulnerability of more DNNs but also identifies deficiencies of DNNs in a wider range of classes. Extensive experiments have demonstrated the effectiveness of our proposed method in generating adversarial examples for both known and unknown classes. Notably, compared with other generative methods, our method achieves an approximately 14.13% higher attack success rate for unknown classes and an approximately 4.23% higher success rate for known classes. Our code is available in https://github.com/VL-Group/GAKer."
Towards Reliable Advertising Image Generation Using Human Feedback
Authors: Zhenbang Du; Wei Feng; Haohan Wang; Yaoyu Li; Jingsen Wang; Jian Li; Zheng Zhang; Jingjing Lv; Xin Zhu; Junsheng Jin; Junjie Shen; Zhangang Lin; Jingping Shao
Venue: ECCV 2024 (main)

Abstract: "In the e-commerce realm, compelling advertising images are pivotal for attracting customer attention. While generative models automate image generation, they often produce substandard images that may mislead customers and require significant labor costs to inspect. This paper delves into increasing the rate of available generated images. We first introduce a multi-modal Reliable Feedback Network (RFNet) to automatically inspect the generated images. Combining the RFNet into a recurrent process, Recurrent Generation, results in a higher number of available advertising images. To further enhance production efficiency, we fine-tune diffusion models with an innovative Consistent Condition regularization utilizing the feedback from RFNet (RFFT). This results in a remarkable increase in the available rate of generated images, reducing the number of attempts in Recurrent Generation, and providing a highly efficient production process without sacrificing visual appeal. We also construct a Reliable Feedback 1 Million (RF1M) dataset which comprises over one million generated advertising images annotated by human, which helps to train RFNet to accurately assess the availability of generated images and faithfully reflect the human feedback. Generally speaking, our approach offers a reliable solution for advertising image generation. Our dataset and code are available at https://github.com/ZhenbangDu/ Reliable_AD."
Topology-Preserving Downsampling of Binary Images
Authors: Chia-Chia Chen; Chi-Han Peng
Venue: ECCV 2024 (main)

Abstract: "We present a novel discrete optimization-based approach to generate downsampled versions of binary images that are guaranteed to have the same topology as the original, measured by the zeroth and first Betti numbers of the black regions, while having good similarity to the original image as measured by IoU and Dice scores. To our best knowledge, all existing binary image downsampling methods don’t have such topology-preserving guarantees. We also implemented a baseline morphological operation (dilation)-based approach that always generates topologically correct results. However, we found the similarity scores to be much worse. We demonstrate several applications of our approach. First, generating smaller versions of medical image segmentation masks for easier human inspection. Second, improving the efficiency of binary image operations, including persistent homology computation and shortest path computation, by substituting the original images with smaller ones. In particular, the latter is a novel application that is made feasible only by the full topology-preservation guarantee of our method."
ColorMAE: Exploring data-independent masking strategies in Masked AutoEncoders
Authors: Carlos Hinojosa; Shuming Liu; Bernard Ghanem
Venue: ECCV 2024 (main)

Abstract: "Masked AutoEncoders (MAE) have emerged as a robust self-supervised framework, offering remarkable performance across a wide range of downstream tasks. To increase the difficulty of the pretext task and learn richer visual representations, existing works have focused on replacing standard random masking with more sophisticated strategies, such as adversarial-guided and teacher-guided masking. However, these strategies depend on the input data thus commonly increasing the model complexity and requiring additional calculations to generate the mask patterns. This raises the question: Can we enhance MAE performance beyond random masking without relying on input data or incurring additional computational costs? In this work, we introduce a simple yet effective data-independent method, termed , which generates different binary mask patterns by filtering random noise. Drawing inspiration from color noise in image processing, we explore four types of filters to yield mask patterns with different spatial and semantic priors. requires no additional learnable parameters or computational overhead in the network, yet it significantly enhances the learned representations. We provide a comprehensive empirical evaluation, demonstrating our strategy’s superiority in downstream tasks compared to random masking. Notably, we report an improvement of 2.72 in mIoU in semantic segmentation tasks relative to baseline MAE implementations."
Classification Matters: Improving Video Action Detection with Class-Specific Attention
Authors: Jinsung Lee; Taeoh Kim; Inwoong Lee; Minho Shim; Dongyoon Wee; Minsu Cho; Suha Kwak
Venue: ECCV 2024 (main)

Abstract: "Video action detection (VAD) aims to detect actors and classify their actions in a video. We figure that VAD suffers more from classification rather than localization of actors. Hence, we analyze how prevailing methods form features for classification and find that they prioritize actor regions, yet often overlooking the essential contextual information necessary for accurate classification. Accordingly, we propose to reduce the bias toward actor and encourage paying attention to the context that is relevant to each action class. By assigning a class-dedicated query to each action class, our model can dynamically determine where to focus for effective classification. The proposed model demonstrates superior performance on three challenging benchmarks with significantly fewer parameters and less computation."
Improving Medical Multi-modal Contrastive Learning with Expert Annotations
Authors: Yogesh Kumar; Pekka Marttinen
Venue: ECCV 2024 (main)

Abstract: "We introduce eCLIP, an enhanced version of the CLIP model that integrates expert annotations in the form of radiologist eye-gaze heatmaps. It tackles key challenges in contrastive multi-modal medical imaging analysis, notably data scarcity and the “modality gap” – a significant disparity between image and text embeddings that diminishes the quality of representations and hampers cross-modal interoperability. eCLIP integrates a heatmap processor and leverages mixup augmentation to efficiently utilize the scarce expert annotations, thus boosting the model’s learning effectiveness. eCLIP is designed to be generally applicable to any variant of CLIP without requiring any modifications of the core architecture. Through detailed evaluations across several tasks, including zero-shot inference, linear probing, cross-modal retrieval, and Retrieval Augmented Generation (RAG) of radiology reports using a frozen Large Language Model, eCLIP showcases consistent improvements in embedding quality. The outcomes reveal enhanced alignment and uniformity, affirming eCLIP’s capability to harness high-quality annotations for enriched multi-modal analysis in the medical imaging domain."
Rethinking Data Bias: Dataset Copyright Protection via Embedding Class-wise Hidden Bias
Authors: Jinhyeok Jang; ByungOk Han; Jaehong Kim; Chan-Hyun Youn
Venue: ECCV 2024 (main)

Abstract: "Public datasets play a crucial role in advancing data-centric AI, yet they remain vulnerable to illicit uses. This paper presents ‘undercover bias,’ a novel dataset watermarking method that can reliably identify and verify unauthorized data usage. Our approach is inspired by an observation that trained models often inadvertently learn biased knowledge and can function on bias-only data, even without any information directly related to a target task. Leveraging this, we deliberately embed class-wise hidden bias via unnoticeable watermarks, which are unrelated to the target dataset but share the same labels. Consequently, a model trained on this watermarked data covertly learns to classify these watermarks. The model’s performance in classifying the watermarks serves as irrefutable evidence of unauthorized usage, which cannot be achieved by chance. Our approach presents multiple benefits: 1) stealthy and model-agnostic watermarks; 2) minimal impact on the target task; 3) irrefutable evidence of misuse; and 4) improved applicability in practical scenarios. We validate these benefits through extensive experiments and extend our method to fine-grained classification and image segmentation tasks. Our implementation is available at here1 . 1 https://github.com/jjh6297/UndercoverBias"
Pose-Aware Self-Supervised Learning with Viewpoint Trajectory Regularization
Authors: Jiayun Wang; Yubei Chen; Stella X. Yu
Venue: ECCV 2024 (main)

Abstract: "Learning visual features from unlabeled images has proven successful for semantic categorization, often by mapping different views of the same object to the same feature to achieve recognition invariance. However, visual recognition involves not only identifying what an object is but also understanding how it is presented. For example, seeing a car from the side versus head-on is crucial for deciding whether to stay put or jump out of the way. While unsupervised feature learning for downstream viewpoint reasoning is important, it remains under-explored, partly due to the lack of a standardized evaluation method and benchmarks. We introduce a new dataset of adjacent image triplets obtained from a viewpoint trajectory, without any semantic or pose labels. We benchmark both semantic classification and pose estimation accuracies on the same visual feature. Additionally, we propose a viewpoint trajectory regularization loss for learning features from unlabeled image triplets. Our experiments demonstrate that this approach helps develop a visual representation that encodes object identity and organizes objects by their poses, retaining semantic classification accuracy while achieving emergent global pose awareness and better generalization to novel objects. Our dataset and code are available at http://pwang.pw/trajSSL/."
SILC: Improving Vision Language Pretraining with Self-Distillation
Authors: Muhammad Ferjad Naeem; Yongqin Xian; Xiaohua Zhai; Lukas Hoyer; Luc Van Gool; Federico Tombari
Venue: ECCV 2024 (main)

Abstract: "Image-Text pretraining on web-scale image caption datasets has become the default recipe for open vocabulary classification and retrieval models thanks to the success of CLIP and its variants. Several works have also used CLIP features for dense prediction tasks and have shown the emergence of open-set abilities. However, the contrastive objective used by these models only focuses on image-text alignment and does not incentivise image feature learning for dense prediction tasks. In this work, we introduce SILC, a novel framework for vision language pretraining. SILC improves image-text contrastive learning with the simple addition of local-to-global correspondence learning by self-distillation. We show that distilling local image features from an exponential moving average (EMA) teacher model significantly improves model performance on dense predictions tasks like detection and segmentation, while also providing improvements on image-level tasks such as classification and retrieval. models sets a new state of the art for zero-shot classification, few shot classification, image and text retrieval, zero-shot segmentation, and open vocabulary segmentation. We further show that SILC features greatly benefit open vocabulary detection, captioning and visual question answering."
Learning Semantic Latent Directions for Accurate and Controllable Human Motion Prediction
Authors: Guowei Xu; Jiale Tao; Wen Li; Lixin Duan
Venue: ECCV 2024 (main)

Abstract: "In the realm of stochastic human motion prediction (SHMP), researchers have often turned to generative models like GANS, VAEs and diffusion models. However, most previous approaches have struggled to accurately predict motions that are both realistic and coherent with past motion due to a lack of guidance on the latent distribution. In this paper, we introduce Semantic Latent Directions (SLD) as a solution to this challenge, aiming to constrain the latent space to learn meaningful motion semantics and enhance the accuracy of SHMP. SLD defines a series of orthogonal latent directions and represents the hypothesis of future motion as a linear combination of these directions. By creating such an information bottleneck, SLD excels in capturing meaningful motion semantics, thereby improving the precision of motion predictions. Moreover, SLD offers controllable prediction capabilities by adjusting the coefficients of the latent directions during the inference phase. Expanding on SLD, we introduce a set of motion queries to enhance the diversity of predictions. By aligning these motion queries with the SLD space, SLD is further promoted to more accurate and coherent motion predictions. Through extensive experiments conducted on widely used benchmarks, we showcase the superiority of our method in accurately predicting motions while maintaining a balance of realism and diversity. Our code and pretrained models are available at https://github.com/GuoweiXu368/SLD-HMP."
Leveraging temporal contextualization for video action recognition
Authors: Minji Kim; Dongyoon Han; Taekyung Kim; Bohyung Han
Venue: ECCV 2024 (main)

Abstract: "We propose a novel framework for video understanding, called (), which leverages essential temporal information through global interactions in a spatio-temporal domain within a video. To be specific, we introduce Temporal Contextualization (TC), a layer-wise temporal information infusion mechanism for videos, which 1) extracts core information from each frame, 2) connects relevant information across frames for the summarization into context tokens, and 3) leverages the context tokens for feature encoding. Furthermore, the Video-conditional Prompting (VP) module processes context tokens to generate informative prompts in the text modality. Extensive experiments in zero-shot, few-shot, base-to-novel, and fully-supervised action recognition validate the effectiveness of our model. Ablation studies for TC and VP support our design choices. Our project page with the source code is available at https://github.com/naver-ai/tc-clip."
ChEX: Interactive Localization and Region Description in Chest X-rays
Authors: Philip Müller; Georgios Kaissis; Daniel Rueckert
Venue: ECCV 2024 (main)

Abstract: "Report generation models offer fine-grained textual interpretations of medical images like chest X-rays, yet they often lack interactivity (the ability to steer the generation process through user queries) and localized interpretability (visually grounding their predictions), which we deem essential for future adoption in clinical practice. While there have been efforts to tackle these issues, they are either limited in their interactivity by not supporting textual queries or fail to also offer localized interpretability. Therefore, we propose a novel multitask architecture and training paradigm integrating textual prompts and bounding boxes for diverse aspects like anatomical regions and pathologies. We call this approach the Chest X-Ray Explainer (ChEX). Evaluations across a heterogeneous set of 9 chest X-ray tasks, including localized image interpretation and report generation, showcase its competitiveness with SOTA models while additional analysis demonstrates ChEX’s interactive capabilities. Code: https://github. com/philip-mueller/chex."
AdaGlimpse: Active Visual Exploration with Arbitrary Glimpse Position and Scale
Authors: Adam Pardyl; Michał Wronka; Maciej Wołczyk; Kamil Adamczewski; Tomasz Trzcinski; Bartosz Zieliński
Venue: ECCV 2024 (main)

Abstract: "Active Visual Exploration (AVE) is a task that involves dynamically selecting observations (glimpses), which is critical to facilitate comprehension and navigation within an environment. While modern AVE methods have demonstrated impressive performance, they are constrained to fixed-scale glimpses from rigid grids. In contrast, existing mobile platforms equipped with optical zoom capabilities can capture glimpses of arbitrary positions and scales. To address this gap between software and hardware capabilities, we introduce . It uses Soft Actor-Critic, a reinforcement learning algorithm tailored for exploration tasks, to select glimpses of arbitrary position and scale. This approach enables our model to rapidly establish a general awareness of the environment before zooming in for detailed analysis. Experimental results demonstrate that surpasses previous methods across various visual tasks while maintaining greater applicability in realistic AVE scenarios."