GenAI4ELab/papercli-papers huggingface dataset:
A Geometric Distortion Immunized Deep Watermarking Framework with Robustness Generalizability
Authors: Linfeng Ma; Han Fang; Tianyi Wei; Zijin Yang; Zehua Ma; Weiming Zhang; Nenghai Yu
Venue: ECCV 2024 (main)

Abstract: "Robustness is the most important property of watermarking schemes. In practice, the watermarking mechanism shall be robust to both geometric and non-geometric distortions. In deep learning-based watermarking frameworks, robustness can be ensured by end-to-end training with different noise layers. However, most of the current CNN-based watermarking frameworks, even trained with targeted distortions, cannot well adapt to geometric distortions due to the architectural design. Since the traditional convolutional layer’s position structure is relatively fixed, it lacks the flexibility to capture the influence of geometric distortion, making it difficult to train for corresponding robustness. To address such limitations, we propose a Swin Transformer and Deformable Convolutional Network (DCN)-based watermark model backbone. The attention mechanism and the deformable convolutional window effectively improve the feature processing flexibility, greatly enhancing the robustness, especially for geometric distortions. Besides, for non-geometric distortions, aiming at improving the generalizability for more distortions, we also provide a distortion-style-ensembled noise layer, including an image encoder, an image decoder, and distortion-style layers that can effectively simulate styles of different kinds of distortions. Then we can simply train our watermark model with the proposed noise layer for overall robustness. Experiments illustrate the superiority of our method compared to existing state-of-the-art (SOTA) works, such as the 100.00% watermark extraction accuracy under almost all tested geometric distortions."
Towards Robust Event-based Networks for Nighttime via Unpaired Day-to-Night Event Translation
Authors: Yuhwan Jeong; Hoonhee Cho; Kuk-Jin Yoon
Venue: ECCV 2024 (main)

Abstract: "Event cameras with high dynamic range ensure scene capture even in low-light conditions. However, night events exhibit patterns different from those captured during the day. This difference causes performance degradation when applying night events to a model trained solely on day events. This limitation persists due to a lack of annotated night events. To overcome the limitation, we aim to alleviate data imbalance by translating annotated day data into night events. However, generating events from different modalities challenges reproducing their unique properties. Accordingly, we propose an unpaired event-to-event day-to-night translation model that effectively learns to map from one domain to another using Diffusion GAN. The proposed translation model analyzes events in spatio-temporal dimension with wavelet decomposition and disentangled convolution layers. We also propose a new temporal contrastive learning with a novel shuffling and sampling strategy to regularize temporal continuity. To validate the efficacy of the proposed methodology, we redesign metrics for evaluating events translated in an unpaired setting, aligning them with the event modality for the first time. Our framework shows the successful day-to-night event translation while preserving the characteristics of events. In addition, through our translation method, we facilitate event-based modes to learn about night events by translating annotated day events into night events. Our approach effectively mitigates the performance degradation of applying real night events to downstream tasks. The code is available at https://github.com/jeongyh98/ UDNET."
CLAMP-ViT: Contrastive Data-Free Learning for Adaptive Post-Training Quantization of ViTs
Authors: Akshat Ramachandran; Souvik Kundu; Tushar Krishna
Venue: ECCV 2024 (main)

Abstract: "We present CLAMP-ViT, a data-free post-training quantization method for vision transformers (ViTs). We identify the limitations of recent techniques, notably their inability to leverage meaningful inter-patch relationships, leading to the generation of simplistic and semantically vague data, impacting quantization accuracy. CLAMP-ViT employs a two-stage approach, cyclically adapting between data generation and model quantization. Specifically, we incorporate a patch-level contrastive learning scheme to generate richer, semantically meaningful data. Furthermore, we leverage contrastive learning in layer-wise evolutionary search for fixed- and mixed-precision quantization to identify optimal quantization parameters while mitigating the effects of a non-smooth loss landscape. Extensive evaluations across various vision tasks demonstrate the superiority of CLAMP-ViT, with performance improvements of up to 3% in top-1 accuracy for classification, 0.6 mAP for object detection, and 1.5 mIoU for segmentation at similar or better compression ratio over existing alternatives. Code is available at https: //github.com/georgia-tech-synergy-lab/CLAMP-ViT.git"
A Riemannian Approach for Spatiotemporal Analysis and Generation of 4D Tree-shaped Structures
Authors: Tahmina Khanam; Mohammed Bennamoun; Guan Wang; Guanjin Wang; Ferdous Sohel; Farid Boussaid; Anuj Srivastava; Hamid Laga
Venue: ECCV 2024 (main)

Abstract: "We propose the first comprehensive approach for modeling and analyzing the spatiotemporal shape variability in tree-like 4D objects, 3D objects whose shapes bend, stretch and change in their branching structure over time as they deform, grow, and interact with their environment. Our key contribution is the representation of tree-like 3D shapes using Square Root Velocity Function Trees (SRVFT) [?]. By solving the spatial registration in the SRVFT space, which is equipped with an metric, 4D tree-shaped structures become time-parameterized trajectories in this space. This reduces the problem of modeling and analyzing 4D tree-like shapes to that of modeling and analyzing elastic trajectories in the SRVFT space, where elasticity refers to time warping. In this paper, we propose a novel mathematical representation of the shape space of such trajectories, a Riemannian metric on that space, and computational tools for fast and accurate spatiotemporal registration and geodesics computation between 4D tree-shaped structures. Leveraging these building blocks, we develop a full framework for modelling the spatiotemporal variability using statistical models and generating novel 4D tree-like structures from a set of exemplars. We demonstrate and validate the proposed framework using real 4D plant data."
Dual-Path Adversarial Lifting for Domain Shift Correction in Online Test-time Adaptation
Authors: Yushun Tang; Shuoshuo Chen; Zhihe Lu; Xinchao Wang; Zhihai He
Venue: ECCV 2024 (main)

Abstract: "Transformer-based methods have achieved remarkable success in various machine learning tasks. How to design efficient test-time adaptation methods for transformer models becomes an important research task. In this work, motivated by the dual-subband wavelet lifting scheme developed in multi-scale signal processing which is able to efficiently separate the input signals into principal components and noise components, we introduce a dual-path token lifting for domain shift correction in test time adaptation. Specifically, we introduce an extra token, referred to as domain shift token, at each layer of the transformer network. We then perform dual-path lifting with interleaved token prediction and update between the path of domain shift tokens and the path of class tokens at all network layers. The prediction and update networks are learned in an adversarial manner. Specifically, the task of the prediction network is to learn the residual noise of domain shift which should be largely invariant across all classes and all samples in the target domain. In other words, the predicted domain shift noise should be indistinguishable between all sample classes. On the other hand, the task of the update network is to update the class tokens by removing the domain shift from the input image samples so that input samples become more discriminative between different classes in the feature space. To effectively learn the prediction and update networks with two adversarial tasks, both theoretically and practically, we demonstrate that it is necessary to use smooth optimization for the update network but non-smooth optimization for the prediction network. Experimental results on the benchmark datasets demonstrate that our proposed method significantly improves the online fully test-time domain adaptation performance."
Data Overfitting for On-Device Super-Resolution with Dynamic Algorithm and Compiler Co-Design
Authors: Gen Li; zhihao shu; Jie Ji; Minghai Qin; Fatemeh Afghah; Wei Niu; Xiaolong Ma
Venue: ECCV 2024 (main)

Abstract: "Deep neural networks (DNNs) are frequently employed in a variety of computer vision applications. Nowadays, an emerging trend in the current video distribution system is to take advantage of DNN’s overfitting properties to perform video resolution upscaling. By splitting videos into chunks and applying a super-resolution (SR) model to overfit each chunk, this scheme of SR models plus video chunks is able to replace traditional video transmission to enhance video quality and transmission efficiency. However, many models and chunks are needed to guarantee high performance, which leads to tremendous overhead on model switching and memory footprints at the user end. To resolve such problems, we propose a Dynamic Deep neural network assisted by a Content-Aware data processing pipeline to reduce the model number down to one (Dy-DCA), which helps promote performance while conserving computational resources. Additionally, to achieve real acceleration on the user end, we designed a framework that optimizes dynamic features (e.g., dynamic shapes, sizes, and control flow) in Dy-DCA to enable a series of compilation optimizations, including fused code generation, static execution planning, etc. By employing such techniques, our method achieves better PSNR and real-time performance (33 FPS) on an off-the-shelf mobile phone. Meanwhile, assisted by our compilation optimization, we achieve a 1.7× speedup while saving up to 1.61× memory consumption. Code available in https://github.com/coulsonlee/Dy-DCA-ECCV2024."
The Role of Masking for Efficient Supervised Knowledge Distillation of Vision Transformers
Authors: Seungwoo Son; Jegwang Ryu; Namhoon Lee; Jaeho Lee
Venue: ECCV 2024 (main)

Abstract: "Knowledge distillation is an effective method for training lightweight vision models. However, acquiring teacher supervision for training samples is often costly, especially from large-scale models like vision transformers (ViTs). In this paper, we develop a simple framework to reduce the supervision cost of ViT distillation: masking out a fraction of input tokens given to the teacher. By masking input tokens, one can skip the computations associated with the masked tokens without requiring any change to teacher parameters or architecture. We find that masking patches with the lowest student attention scores is highly effective, saving up to 50% of teacher FLOPs without any drop in student accuracy, while other masking criterion leads to suboptimal efficiency gains. Through in-depth analyses, we reveal that the student-guided masking provides a good curriculum to the student, making teacher supervision easier to follow during the early stage and challenging in the later stage."
Training A Small Emotional Vision Language Model for Visual Art Comprehension
Authors: Jing Zhang; Liang Zheng; Meng Wang; Dan Guo
Venue: ECCV 2024 (main)

Abstract: "This paper develops small vision language models to understand visual art, which, given an art work, aims to identify its emotion category and explain this prediction with natural language. While small models are computationally efficient, their capacity is much limited compared with large models. To break this trade-off, this paper builds a small emotional vision language model (SEVLM) by emotion modeling and input-output feature alignment. On the one hand, based on valence-arousal-dominance (VAD) knowledge annotated by psychology experts, we introduce and fuse emotional features derived through VAD dictionary and a VAD head to align VAD vectors of predicted emotion explanation and the ground truth. This allows the vision language model to better understand and generate emotional texts, compared with using traditional text embeddings alone. On the other hand, we design a contrastive head to pull close embeddings of the image, its emotion class, and explanation, which aligns model outputs and inputs. On two public affective explanation datasets, we show that the proposed techniques consistently improve the visual art understanding performance of baseline SEVLMs. Importantly, the proposed model can be trained and evaluated on a single RTX 2080 Ti while exhibiting very strong performance: it not only outperforms the state-of-the-art small models but is also competitive compared with LLaVA 7B after fine-tuning and GPT4(V). The code is available at https://github.com/BetterZH/SEVLM-"
UGG: Unified Generative Grasping
Authors: Jiaxin Lu; Hao Kang; Haoxiang Li; Bo Liu; Yiding Yang; Qixing Huang; Gang Hua
Venue: ECCV 2024 (main)

Abstract: "Dexterous grasping aims to produce diverse grasping postures with a high grasping success rate. Regression-based methods that directly predict grasping parameters given the object may achieve a high success rate but often lack diversity. Generation-based methods that generate grasping postures conditioned on the object can often produce diverse grasping, but they are insufficient for high grasping success due to lack of discriminative information. To mitigate, we introduce a unified diffusion-based dexterous grasp generation model, dubbed the name , which operates within the object point cloud and hand parameter spaces. Our all-transformer architecture unifies the information from the object, the hand, and the contacts, introducing a novel representation of contact points for improved contact modeling. The flexibility and quality of our model enable the integration of a lightweight discriminator, benefiting from simulated discriminative data, which pushes for a high success rate while preserving high diversity. Beyond grasp generation, our model can also generate objects based on hand information, offering valuable insights into object design and studying how the generative model perceives objects. Our model achieves state-of-the-art dexterous grasping on the large-scale DexGraspNet dataset while facilitating human-centric object design, marking a significant advancement in dexterous grasping research. Our project page is https://jiaxin-lu.github.io/ugg/."
FrePolad: Frequency-Rectified Point Latent Diffusion for Point Cloud Generation
Authors: Chenliang Zhou; Fangcheng Zhong; Param Hanji; Zhilin Guo; Kyle Thomas Fogarty; Alejandro Sztrajman; Hongyun Gao; A. Cengiz Oztireli
Venue: ECCV 2024 (main)

Abstract: "We propose FrePolad: frequency-rectified point latent diffusion, a point cloud generation pipeline integrating a variational autoencoder (VAE) with a denoising diffusion probabilistic model (DDPM) for the latent distribution. FrePolad simultaneously achieves high quality, diversity, and flexibility in point cloud cardinality for generation tasks while maintaining high computational efficiency. The improvement in generation quality and diversity is achieved through (1) a novel frequency rectification via spherical harmonics designed to retain high-frequency content while learning the point cloud distribution; and (2) a latent DDPM to learn the regularized yet complex latent distribution. In addition, FrePolad supports variable point cloud cardinality by formulating the sampling of points as conditional distributions over a latent shape distribution. Finally, the low-dimensional latent space encoded by the VAE contributes to FrePolad’s fast and scalable sampling. Our quantitative and qualitative results demonstrate FrePolad’s state-of-the-art performance in terms of quality, diversity, and computational efficiency. Project page: https://chenliang-zhou.github. io/FrePolad/."
Learning to Detect Multi-class Anomalies with Just One Normal Image Prompt
Authors: Bin-Bin Gao
Venue: ECCV 2024 (main)

Abstract: "Unsupervised reconstruction networks using self-attention transformers have achieved state-of-the-art performance for multi-class (unified) anomaly detection with a single model. However, these self-attention reconstruction models primarily operate on target features, which may result in perfect reconstruction for both normal and anomaly features due to high consistency with context, leading to failure in detecting anomalies. Additionally, these models often produce inaccurate anomaly segmentation due to performing reconstruction in low spatial resolution latent space. To enable reconstruction models enjoying high efficiency while enhancing their generalization for unified anomaly detection, we propose a simple yet effective method that reconstructs normal features and restores anomaly features with just One Normal Image Prompt (OneNIP). In contrast to previous work, OneNIP allows for the first time to reconstruct or restore anomalies with just one normal image prompt, effectively boosting unified anomaly detection performance. Furthermore, we propose a supervised refiner that regresses reconstruction errors by using both real normal and synthesized anomalous images, which significantly improves pixel-level anomaly segmentation. OneNIP outperforms previous methods on three industry anomaly detection benchmarks: MVTec, BTAD, and ViSA."
GAMMA-FACE: GAussian Mixture Models Amend Diffusion Models for Bias Mitigation in Face Images
Authors: Basudha Pal; Arunkumar Kannan; Ram Prabhakar Kathirvel; Alice O'Toole; Rama Chellappa
Venue: ECCV 2024 (main)

Abstract: "Significant advancements have been achieved in the domain of face generation with the adoption of diffusion models. However, diffusion models tend to amplify biases during the generative process, resulting in an uneven distribution of sensitive facial attributes such as age, gender, and race. In this paper, we introduce a novel approach to address this issue by debiasing the attributes in the images generated by diffusion models. Our approach involves disentangling facial attributes by localizing the means within the latent space of the diffusion model using Gaussian mixture models (GMM). This method, leveraging the adaptable latent structure of diffusion models, allows us to localize the subspace responsible for generating specific attributes on-the-fly without the need for retraining. We demonstrate the effectiveness of our technique across various face datasets, resulting in fairer data generation while preserving sample quality. Furthermore, we empirically illustrate its effectiveness in reducing bias in downstream classification tasks without compromising performance by augmenting the original dataset with fairly generated data."
Reinforcement Learning Friendly Vision-Language Model for Minecraft
Authors: Haobin Jiang; Junpeng Yue; Hao Luo; Ziluo Ding; Zongqing Lu
Venue: ECCV 2024 (main)

Abstract: "One of the essential missions in the AI research community is to build an autonomous embodied agent that can achieve high-level performance across a wide spectrum of tasks. However, acquiring or manually designing rewards for all open-ended tasks is unrealistic. In this paper, we propose a novel cross-modal contrastive learning framework architecture, CLIP4MC, aiming to learn a reinforcement learning (RL) friendly vision-language model (VLM) that serves as an intrinsic reward function for open-ended tasks. Simply utilizing the similarity between the video snippet and the language prompt is not RL-friendly since standard VLMs may only capture the similarity at a coarse level. To achieve RL-friendliness, we incorporate the task completion degree into the VLM training objective, as this information can assist agents in distinguishing the importance between different states. Moreover, we provide neat YouTube datasets based on the large-scale YouTube database provided by MineDojo. Specifically, two rounds of filtering operations guarantee that the dataset covers enough essential information and that the video-text pair is highly correlated. Empirically, we demonstrate that the proposed method achieves better performance on RL tasks compared with baselines. The code and datasets are available at https://github.com/PKU-RL/CLIP4MC."
Pseudo-RIS: Distinctive Pseudo-supervision Generation for Referring Image Segmentation
Authors: Seonghoon Yu; Paul Hongsuck Seo; Jeany Son
Venue: ECCV 2024 (main)

Abstract: "We propose a new framework that automatically generates high-quality segmentation masks with their referring expressions as pseudo supervisions for referring image segmentation (RIS). These pseudo supervisions allow the training of any supervised RIS methods without the cost of manual labeling. To achieve this, we incorporate existing segmentation and image captioning foundation models, leveraging their broad generalization capabilities. However, the naı̈ve incorporation of these models may generate non-distinctive expressions that do not distinctively refer to the target masks. To address this challenge, we propose two-fold strategies that generate distinctive captions: 1) ‘distinctive caption sampling’, a new decoding method for the captioning model, to generate multiple expression candidates with detailed words focusing on the target. 2) ‘distinctiveness-based text filtering’ to further validate the candidates and filter out those with a low level of distinctiveness. These two strategies ensure that the generated text supervisions can distinguish the target from other objects, making them appropriate for the RIS annotations. Our method significantly outperforms both weakly and zero-shot SoTA methods on the RIS benchmark datasets. It also surpasses fully supervised methods in unseen domains, proving its capability to tackle the open-world challenge within RIS. Furthermore, integrating our method with human annotations yields further improvements, highlighting its potential in semi-supervised learning applications. † Corresponding authors."
Training-free Composite Scene Generation for Layout-to-Image Synthesis
Authors: Jiaqi Liu; Tao Huang; Chang Xu
Venue: ECCV 2024 (main)

Abstract: "Recent breakthroughs in text-to-image diffusion models have significantly advanced the generation of high-fidelity, photo-realistic images from textual descriptions. Yet, these models often struggle with interpreting spatial arrangements from text, hindering their ability to produce images with precise spatial configurations. To bridge this gap, layout-to-image generation has emerged as a promising direction. However, training-based approaches are limited by the need for extensively annotated datasets, leading to high data acquisition costs and a constrained conceptual scope. Conversely, training-free methods face challenges in accurately locating and generating semantically similar objects within complex compositions. This paper introduces a novel training-free approach designed to overcome adversarial semantic intersections during the diffusion conditioning phase. By refining intra-token loss with selective sampling and enhancing the diffusion process with attention redistribution, we propose two innovative constraints: 1) an inter-token constraint that resolves token conflicts to ensure accurate concept synthesis; and 2) a self-attention constraint that improves pixel-to-pixel relationships. Our evaluations confirm the effectiveness of leveraging layout information for guiding the diffusion process, generating content-rich images with enhanced fidelity and complexity. Code is available at https: //github.com/Papple-F/csg.git."