GenAI4ELab/papercli-papers huggingface dataset:
Denoising Vision Transformers
Authors: Jiawei Yang; Katie Z Luo; Jiefeng Li; Congyue Deng; Leonidas Guibas; Dilip Krishnan; Kilian Weinberger; Yonglong Tian; Yue Wang
Venue: ECCV 2024 (main)

Abstract: "We study a crucial yet often overlooked issue inherent to Vision Transformers (ViTs): feature maps of these models exhibit grid-like artifacts (“Original features” in fig:teaser), which hurt the performance of ViTs in downstream dense prediction tasks such as semantic segmentation, depth prediction, and object discovery. We trace this issue down to the positional embeddings at the input stage. To mitigate this, we propose a two-stage denoising approach, termed (). In the first stage, we separate the clean features from those contaminated by positional artifacts by enforcing cross-view feature consistency with neural fields on a per-image basis. This per-image optimization process extracts artifact-free features from raw ViT outputs, providing clean feature estimates for offline applications. In the second stage, we train a lightweight transformer block to predict clean features from raw ViT outputs, leveraging the derived estimates of the clean features as supervision. Our method, , does not require re-training the existing pre-trained ViTs, and is immediately applicable to any Vision Transformer architecture. We evaluate our method on a variety of representative ViTs (DINO, DeiT-III, EVA02, CLIP, DINOv2, DINOv2-reg) and demonstrate that consistently improves existing state-of-the-art general-purpose models in semantic and geometric tasks across multiple datasets (fig:teaser, right, tab:denser esults, tab : objd et, tab : objd iscovery).W ehopeourstudywillencourageare−evaluationof V iT design, especiallyregardingth"
Differentiable Product Quantization for Memory Efficient Camera Relocalization
Authors: Zakaria Laskar; Iaroslav Melekhov; Assia Benbihi; Shuzhe Wang; Juho Kannala
Venue: ECCV 2024 (main)

Abstract: "Camera relocalization relies on 3D models of the scene with large memory footprint that is incompatible with the memory budget of several applications. One solution to reduce the scene memory size is map compression by removing certain 3D points and descriptor quantization. This achieves high compression but leads to performance drop due to information loss. To address the memory performance trade-off, we train a light-weight scene-specific auto-encoder network that performs descriptor quantization-dequantization in an end-to-end differentiable manner updating both product quantization centroids and network parameters through back-propagation. In addition to optimizing the network for descriptor reconstruction, we encourage it to preserve the descriptor-matching performance with margin-based metric loss functions. Results show that for a local descriptor memory of only 1MB, the synergistic combination of the proposed network and map compression achieves the best performance on the Aachen Day-Night compared to existing compression methods."
Spline-based Transformers
Authors: Prashanth Chandran; Agon Serifi; Markus Gross; Moritz Bächer
Venue: ECCV 2024 (main)

Abstract: "We introduce Spline-based Transformers, a novel class of Transformer models that eliminate the need for positional encoding. Inspired by workflows using splines in computer animation, our Spline-based Transformers embed an input sequence of elements as a smooth trajectory in latent space. Overcoming drawbacks of positional encoding such as sequence length extrapolation, Spline-based Transformers also provide a novel way for users to interact with transformer latent spaces by directly manipulating the latent control points to create new latent trajectories and sequences. We demonstrate the superior performance of our approach in comparison to conventional positional encoding on a variety of datasets, ranging from synthetic 2D to large-scale real-world datasets of images, 3D shapes, and animations."
Learning Pseudo 3D Guidance for View-consistent Texturing with 2D Diffusion
Authors: Kehan Li; Yanbo Fan; Yang Wu; Zhongqian Sun; Wei Yang; Xiangyang Ji; Li Yuan; Jie Chen
Venue: ECCV 2024 (main)

Abstract: "Text-driven 3D texturing requires the generation of high-fidelity texture that conforms to given geometry and description. Recently, the high-quality text-to-image generation ability of 2D diffusion model has significantly promoted this task, by converting it into a texture optimization process guided by multi-view synthesized images, where the generation of high-quality and multi-view consistency images becomes the key issue. State-of-the-art methods achieve the consistency between different views by treating image generation on a novel view as image inpainting conditioned on the texture generated by previously views. However, due to the accumulated semantic divergence of local inpainting and the occlusion between object parts on sparse views, these inpainting-based methods often fail to deal with long-range texture consistency. To address these, we present P3G, a texturing approach based on learned Pseudo 3D Guidance. The key idea of P3G is to first learn a coarse but consistent texture, to serve as a global semantics guidance for encouraging the consistency between images generated on different views. To this end, we incorporate pre-trained text-to-image diffusion models and multi-view optimization to achieve propagating accurate semantics globally for leaning the guidance, and design an efficient framework for high-quality and multi-view consistent image generation that integrates the learned semantic guidance. Quantitative and qualitative evaluation on variant 3D shapes demonstrates the superiority of our P3G on both consistency and overall visual quality."
TreeSBA: Tree-Transformer for Self-Supervised Sequential Brick Assembly
Authors: Mengqi Guo; Chen Li; Yuyang Zhao; Gim Hee Lee
Venue: ECCV 2024 (main)

Abstract: "Inferring step-wise actions to assemble 3D objects with primitive bricks from images is a challenging task due to complex constraints and the vast number of possible combinations. Recent studies have demonstrated promising results on sequential LEGO brick assembly through the utilization of LEGO-Graph modeling to predict sequential actions. However, existing approaches are class-specific and require significant computational and 3D annotation resources. In this work, we first propose a computationally efficient breadth-first search (BFS) LEGO-Tree structure to model the sequential assembly actions by considering connections between consecutive layers. Based on the LEGO-Tree structure, we then design a class-agnostic tree-transformer framework to predict the sequential assembly actions from the input multi-view images. A major challenge of the sequential brick assembly task is that the step-wise action labels are costly and tedious to obtain in practice. We mitigate this problem by leveraging synthetic-to-real transfer learning. Specifically, our model is first pre-trained on synthetic data with full supervision from the available action labels. We then circumvent the requirement for action labels in the real data by proposing an action-to-silhouette projection that replaces action labels with input image silhouettes for self-supervision. Without any annotation on the real data, our model outperforms existing methods with 3D supervision by 7.8% and 11.3% in mIoU on the MNIST and ModelNet Construction datasets, respectively."
SparseRadNet: Sparse Perception Neural Network on Subsampled Radar Data
Authors: Jialong Wu; Mirko Meuter; Markus Schoeler; Matthias Rottmann
Venue: ECCV 2024 (main)

Abstract: "Radar-based perception has gained increasing attention in autonomous driving, yet the inherent sparsity of radars poses challenges. Radar raw data often contains excessive noise, whereas radar point clouds retain only limited information. In this work, we holistically treat the sparse nature of radar data by introducing an adaptive subsampling method together with a tailored network architecture that exploits the sparsity patterns to discover global and local dependencies in the radar signal. Our subsampling module selects a subset of pixels from range-doppler (RD) spectra that contribute most to the downstream perception tasks. To improve the feature extraction on sparse subsampled data, we propose a new way of applying graph neural networks on radar data and design a novel two-branch backbone to capture both global and local neighbor information. An attentive fusion module is applied to combine features from both branches. Experiments on the RADIal dataset show that our SparseRadNet exceeds state-of-the-art (SOTA) performance in object detection and achieves close to SOTA accuracy in freespace segmentation, meanwhile using sparse subsampled input data."
Enhancing Semantic Fidelity in Text-to-Image Synthesis: Attention Regulation in Diffusion Models
Authors: Yang Zhang; Tze Tzun Teoh; Wei Hern Lim; Kenji Kawaguchi
Venue: ECCV 2024 (main)

Abstract: "Recent advancements in diffusion models have notably improved the perceptual quality of generated images in text-to-image synthesis tasks. However, diffusion models often struggle to produce images that accurately reflect the intended semantics of the associated text prompts. We examine cross-attention layers in diffusion models and observe a propensity for these layers to disproportionately focus on certain tokens during the generation process, thereby undermining semantic fidelity. To address the issue of dominant attention, we introduce attention regulation, a computation-efficient on-the-fly optimization approach at inference time to align attention maps with the input text prompt. Notably, our method requires no additional training or fine-tuning and serves as a plug-in module on a model. Hence, the generation capacity of the original model is fully preserved. We compare our approach with alternative approaches across various datasets, evaluation metrics, and diffusion models. Experiment results show that our method consistently outperforms other baselines, yielding images that more faithfully reflect the desired concepts with reduced computation overhead. Code is available at https://github.com/YaNgZhAnG-V5/attention_regulation."
Adversarial Diffusion Distillation
Authors: Axel Sauer; Dominik Lorenz; Andreas Blattmann; Robin Rombach
Venue: ECCV 2024 (main)

Abstract: "We introduce Adversarial Diffusion Distillation (ADD), a novel training approach that efficiently samples large-scale foundational image diffusion models in just 1–4 steps while maintaining high image quality. We use score distillation to leverage large-scale off-the-shelf image diffusion models as a teacher signal in combination with an adversarial loss to ensure high image fidelity even in the low-step regime of one or two sampling steps. Our analyses show that our model clearly outperforms existing few-step methods (GANs, Latent Consistency Models) in a single step and reaches the performance of state-of-the-art diffusion models (SDXL) in only four steps. ADD is the first method to unlock single-step, real-time image synthesis with foundation models."
Fake It till You Make It: Curricular Dynamic Forgery Augmentations towards General Deepfake Detection
Authors: Yuzhen Lin; Wentang Song; Bin Li; Yuezun Li; Jiangqun Ni; Han Chen; Qiushi Li
Venue: ECCV 2024 (main)

Abstract: "Previous studies in deepfake detection have shown promising results when testing face forgeries from the same dataset as the training. However, the problem remains challenging when one tries to generalize the detector to forgeries from unseen datasets and created by unseen methods. In this work, we present a novel general deepfake detection method, called Curricular Dynamic Forgery Augmentation (CDFA), which jointly trains a deepfake detector with a forgery augmentation policy network. Unlike the previous works, we propose to progressively apply forgery augmentations following a monotonic curriculum during the training. We further propose a dynamic forgery searching strategy to select one suitable forgery augmentation operation for each image varying between training stages, producing a forgery augmentation policy optimized for better generalization. In addition, we propose a novel forgery augmentation named self-shifted blending image to simply imitate the temporal inconsistency of deepfake generation. Comprehensive experiments show that CDFA can significantly improve both cross-datasets and cross-manipulations performances of various naive deepfake detectors in a plug-and-play way, and make them attain superior performances over the existing methods in several benchmark datasets."
Explain via Any Concept: Concept Bottleneck Model with Open Vocabulary Concepts
Authors: Andong Tan; Fengtao Zhou; Hao Chen
Venue: ECCV 2024 (main)

Abstract: "The concept bottleneck model (CBM) is an interpretable-by-design framework that makes decisions by first predicting a set of interpretable concepts, and then predicting the class label based on the given concepts. Existing CBMs are trained with a fixed set of concepts (concepts are either annotated by the dataset or queried from language models). However, this closed-world assumption is unrealistic in practice, as users may wonder about the role of any desired concept in decision-making after the model is deployed. Inspired by the large success of recent vision-language pre-trained models such as CLIP in zero-shot classification, we propose “OpenCBM” to equip the CBM with open vocabulary concepts via: (1) Aligning the feature space of a trainable image feature extractor with that of a CLIP’s image encoder via a prototype based feature alignment; (2) Simultaneously training an image classifier on the downstream dataset; (3) Reconstructing the trained classification head via any set of user-desired textual concepts encoded by CLIP’s text encoder. To reveal potentially missing concepts from users, we further propose to iteratively find the closest concept embedding to the residual parameters during the reconstruction until the residual is small enough. To the best of our knowledge, our “OpenCBM” is the first CBM with concepts of open vocabularies, providing users the unique benefit such as removing, adding, or replacing any desired concept to explain the model’s prediction even after a model is trained. Moreover, our model significantly outperforms the previous state-of-the-art CBM by 9% in the classification accuracy on the benchmark dataset CUB-200-2011."
Explore the Potential of CLIP for Training-Free Open Vocabulary Semantic Segmentation
Authors: Tong Shao; Zhuotao Tian; Hang Zhao; Jingyong Su
Venue: ECCV 2024 (main)

Abstract: "CLIP, as a vision-language model, has significantly advanced Open-Vocabulary Semantic Segmentation (OVSS) with its zero-shot capabilities. Despite its success, its application to OVSS faces challenges due to its initial image-level alignment training, which affects its performance in tasks requiring detailed local context. Our study delves into the impact of CLIP’s [CLS] token on patch feature correlations, revealing a dominance of ”global” patches that hinders local feature discrimination. To overcome this, we propose CLIPtrase, a novel training-free semantic segmentation strategy that enhances local feature awareness through recalibrated self-correlation among patches. This approach demonstrates notable improvements in segmentation accuracy and the ability to maintain semantic coherence across objects. Experiments show that we are 22.3% ahead of CLIP on average on 9 segmentation benchmarks, outperforming existing state-of-the-art training-free methods. The code are made publicly available at https://github.com/leaves162/CLIPtrase"
A Multimodal Benchmark Dataset and Model for Crop Disease Diagnosis
Authors: Xiang Liu; Zhaoxiang Liu; Huan Hu; Zezhou Chen; Kohou Wang; Kai Wang; Shiguo Lian
Venue: ECCV 2024 (main)

Abstract: "While conversational generative AI has shown considerable potential in enhancing decision-making for agricultural professionals, its exploration has predominantly been anchored in text-based interactions. The evolution of multimodal conversational AI, leveraging vast amounts of image-text data from diverse sources, marks a significant stride forward. However, the application of such advanced vision-language models in the agricultural domain, particularly for crop disease diagnosis, remains underexplored. In this work, we present the crop disease domain multimodal (CDDM) dataset, a pioneering resource designed to advance the field of agricultural research through the application of multimodal learning techniques. The dataset comprises 137,000 images of various crop diseases, accompanied by 1 million question-answer pairs that span a broad spectrum of agricultural knowledge, from disease identification to management practices. By integrating visual and textual data, CDDM facilitates the development of sophisticated question-answering systems capable of providing precise, useful advice to farmers and agricultural professionals. We demonstrate the utility of the dataset by finetuning state-of-the-art multimodal models, showcasing significant improvements in crop disease diagnosis. Specifically, we employed a novel finetuning strategy that utilizes low-rank adaptation (LoRA) to finetune the visual encoder, adapter and language model simultaneously. Our contributions include not only the dataset but also a finetuning strategy and a benchmark to stimulate further research in agricultural technology, aiming to bridge the gap between advanced AI techniques and practical agricultural applications. The dataset is available at https://github. com/UnicomAI/UnicomBenchmark/tree/main/CDDMBench."
Missing Modality Prediction for Unpaired Multimodal Learning via Joint Embedding of Unimodal Models
Authors: Taesup Kim; Donggeun Kim
Venue: ECCV 2024 (main)

Abstract: "Multimodal learning typically relies on the assumption that all modalities are fully available during both the training and inference phases. However, in real-world scenarios, consistently acquiring complete multimodal data presents significant challenges due to various factors. This often leads to the issue of missing modalities, where data for certain modalities are absent, posing considerable obstacles not only for the availability of multimodal pretrained models but also for their fine-tuning and the preservation of robustness in downstream tasks. To address these challenges, we propose a novel framework integrating parameter-efficient fine-tuning of unimodal pretrained models with a self-supervised joint-embedding learning method. This framework enables the model to predict the embedding of a missing modality in the representation space during inference. Our method effectively predicts the missing embedding through prompt tuning, leveraging information from available modalities. We evaluate our approach on several multimodal benchmark datasets and demonstrate its effectiveness and robustness across various scenarios of missing modalities."
Learning Where to Look: Self-supervised Viewpoint Selection for Active Localization using Geometrical Information
Authors: Luca Di Giammarino; Boyang Sun; Giorgio Grisetti; Marc Pollefeys; Hermann Blum; Daniel Barath
Venue: ECCV 2024 (main)

Abstract: "Accurate localization in diverse environments is a fundamental challenge in computer vision and robotics. The task involves determining a sensor’s precise position and orientation, typically a camera, within a given space. Traditional localization methods often rely on passive sensing, which may struggle in scenarios with limited features or dynamic environments. In response, this paper explores the domain of active localization, emphasizing the importance of viewpoint selection to enhance localization accuracy. Our contributions involve using a data-driven approach with a simple architecture designed for real-time operation, a self-supervised data training method, and the capability to consistently integrate our map into a planning framework tailored for real-world robotics applications. Our results demonstrate that our method performs better than the existing one, targeting similar problems and generalizing on synthetic and real data. We also release an open-source implementation to benefit the community at www.github.com/rvp-group/learning-where-to-look."
Improving Diffusion Models for Authentic Virtual Try-on in the Wild
Authors: Yisol Choi; Sangkyung Kwak; Kyungmin Lee; Hyungwon Choi; Jinwoo Shin
Venue: ECCV 2024 (main)

Abstract: "This paper considers image-based virtual try-on, which renders an image of a person wearing a curated garment, given a pair of images depicting the person and the garment, respectively. Previous works adapt existing exemplar-based inpainting diffusion models for virtual try-on to improve the naturalness of the generated visuals compared to other methods (e.g., GAN-based), but they fail to preserve the identity of the garments. To overcome this limitation, we propose a novel diffusion model that improves garment fidelity and generates authentic virtual try-on images. Our method, coined , uses two different modules to encode the semantics of garment image; given the base UNet of the diffusion model, 1) the high-level semantics extracted from a visual encoder are fused to the cross-attention layer, and then 2) the low-level features extracted from parallel UNet are fused to the self-attention layer. In addition, we provide detailed textual prompts for both garment and person images to enhance the authenticity of the generated visuals. Finally, we present a customization method using a pair of person-garment images, which significantly improves fidelity and authenticity. Our experimental results show that our method outperforms previous approaches (both diffusion-based and GAN-based) in preserving garment details and generating authentic virtual try-on images, both qualitatively and quantitatively. Furthermore, the proposed customization method demonstrates its effectiveness in a real-world scenario. More visualizations are available in our project page."
Exploiting Semantic Reconstruction to Mitigate Hallucinations in Vision-Language Models
Authors: Minchan Kim; Minyeong Kim; Junik Bae; Suhwan Choi; Sungkyung Kim; Buru Chang
Venue: ECCV 2024 (main)

Abstract: "Hallucinations in vision-language models pose a significant challenge to their reliability, particularly in the generation of long captions. Current methods fall short of accurately identifying and mitigating these hallucinations. To address this issue, we introduce ESREAL, a novel unsupervised reinforcement learning framework designed to suppress the generation of hallucinations through accurate localization and penalization of hallucinated tokens. Initially, ESREAL creates a reconstructed image based on the generated caption and aligns its corresponding regions with those of the original image. This semantic reconstruction aids in identifying both the presence and type of token-level hallucinations within the generated caption. Subsequently, ESREAL computes token-level hallucination scores by assessing the semantic similarity of aligned regions based on the type of hallucination. Finally, ESREAL employs a proximal policy optimization algorithm, where it selectively penalizes hallucinated tokens according to their token-level hallucination scores. Our framework notably reduces hallucinations in LLaVA, InstructBLIP, and mPLUG-Owl2 by 32.81%, 27.08%, and 7.46% on the CHAIR metric. This improvement is achieved solely through signals derived from the image itself, without the need for any image-text pairs."