GenAI4ELab/papercli-papers huggingface dataset:
Resilience of Entropy Model in Distributed Neural Networks
Authors: Milin Zhang; Mohammad Abdi; Shahriar Rifat; Francesco Restuccia
Venue: ECCV 2024 (main)

Abstract: "Distributed have emerged as a key technique to reduce communication overhead without sacrificing performance in edge computing systems. Recently, entropy coding has been introduced to further reduce the communication overhead. The key idea is to train the distributed jointly with an entropy model, which is used as side information during inference time to adaptively encode latent representations into bit streams with variable length. To the best of our knowledge, the resilience of entropy models is yet to be investigated. As such, in this paper we formulate and investigate the resilience of entropy models to intentional interference (, adversarial attacks) and unintentional interference (, weather changes and motion blur). Through an extensive experimental campaign with 3 different architectures, 2 entropy models and 4 rate-distortion trade-off factors, we demonstrate that the entropy attacks can increase the communication overhead by up to 95%. By separating compression features in frequency and spatial domain, we propose a new defense mechanism that can reduce the transmission overhead of the attacked input by about 9% compared to unperturbed data, with only about 2% accuracy loss. Importantly, the proposed defense mechanism is a standalone approach which can be applied in conjunction with approaches such as adversarial training to further improve robustness. Code is available at https://github.com/Restuccia-Group/EntropyR."
Rejection Sampling IMLE: Designing Priors for Better Few-Shot Image Synthesis
Authors: Chirag Vashist; Shichong Peng; Ke Li
Venue: ECCV 2024 (main)

Abstract: "An emerging area of research aims to learn deep generative models with limited training data. Implicit Maximum Likelihood Estimation (IMLE), a recent technique, successfully addresses the mode collapse issue of GANs and has been adapted to the few-shot setting, achieving state-of-the-art performance. However, current IMLE-based approaches encounter challenges due to inadequate correspondence between the latent codes selected for training and those drawn during inference. This results in suboptimal test-time performance. We theoretically show a way to address this issue and propose RS-IMLE, a novel approach that changes the prior distribution used for training. This leads to substantially higher quality image generation compared to existing GAN and IMLE-based methods, as validated by comprehensive experiments conducted on nine few-shot image datasets."
Implicit Concept Removal of Diffusion Models
Authors: Zhili Liu; Kai Chen; Yifan Zhang; Jianhua Han; Lanqing Hong; Hang Xu; Zhenguo Li; Dit-Yan Yeung; James Kwok
Venue: ECCV 2024 (main)

Abstract: "Text-to-image (T2I) diffusion models often inadvertently generate unwanted concepts such as watermarks and unsafe images. These concepts, termed “implicit concepts”, can be unintentionally learned during training and then be generated uncontrollably during inference. Existing removal methods still struggle to eliminate implicit concepts primarily due to their dependency on the model’s ability to recognize concepts it actually can not discern. To address this, we utilize the intrinsic geometric characteristics of implicit concepts and present , a novel concept removal method based on geometric-driven control. Specifically, once an unwanted implicit concept is identified, we integrate the existence and geometric information of the concept into the text prompts with the help of an accessible classifier or detector model. Subsequently, the model is optimized to identify and disentangle this information, which is then adopted as negative prompts during generation. Moreover, we introduce the Implicit Concept Dataset (ICD), a novel image-text dataset imbued with three typical implicit concepts (QR codes, watermarks, and text), reflecting real-life situations where implicit concepts are easily injected. effectively mitigates the generation of implicit concepts, achieving state-of-the-art results on the Inappropriate Image Prompts (I2P) and our challenging Implicit Concept Dataset (ICD) benchmarks."
PLOT: Text-based Person Search with Part Slot Attention for Corresponding Part Discovery
Authors: Jicheol Park; Dongwon Kim; Boseung Jeong; Suha Kwak
Venue: ECCV 2024 (main)

Abstract: "Text-based person search, employing free-form text queries to identify individuals within a vast image collection, presents a unique challenge in aligning visual and textual representations, particularly at the human part level. Existing methods often struggle with part feature extraction and alignment due to the lack of direct part-level supervision and reliance on heuristic features. We propose a novel framework that leverages a part discovery module based on slot attention to autonomously identify and align distinctive parts across modalities, enhancing interpretability and retrieval accuracy without explicit part-level correspondence supervision. Additionally, text-based dynamic part attention adjusts the importance of each part, further improving retrieval outcomes. Our method is evaluated on three public benchmarks, significantly outperforming existing methods."
GS-LRM: Large Reconstruction Model for 3D Gaussian Splatting
Authors: Kai Zhang; Sai Bi; Hao Tan; Yuanbo Xiangli; Nanxuan Zhao; Kalyan Sunkavalli; Zexiang Xu
Venue: ECCV 2024 (main)

Abstract: "We propose , a scalable large reconstruction model that can predict high-quality 3D Gaussian primitives from 2-4 posed sparse images in ∼0.23 seconds on single A100 GPU. Our model features a very simple transformer-based architecture; we patchify input posed images, pass the concatenated multi-view image tokens through a sequence of transformer blocks, and decode final per-pixel Gaussian parameters directly from these tokens for differentiable rendering. In contrast to previous LRMs that can only reconstruct objects, by predicting per-pixel Gaussians, naturally handles scenes with large variations in scale and complexity. We show that our model can work on both object and scene captures by training it on Objaverse and RealEstate10K respectively. In both scenarios, the models outperform state-of-the-art baselines by a wide margin. We also demonstrate applications of our model in downstream 3D generation tasks. Our project webpage is available at: https://sai-bi.github.io/project/gs-lrm/."
Robust-Wide: Robust Watermarking against Instruction-driven Image Editing
Authors: Runyi Hu; Jie Zhang; Ting Xu; Jiwei Li; Tianwei Zhang
Venue: ECCV 2024 (main)

Abstract: "Instruction-driven image editing allows users to quickly edit an image according to text instructions in a forward pass. Nevertheless, malicious users can easily exploit this technique to create fake images, which could cause a crisis of trust and harm the rights of the original image owners. Watermarking is a common solution to trace such malicious behavior. Unfortunately, instruction-driven image editing can significantly change the watermarked image at the semantic level, making current state-of-the-art watermarking methods ineffective. To remedy it, we propose , the first robust watermarking methodology against instruction-driven image editing. Specifically, we follow the classic structure of deep robust watermarking, consisting of the encoder, noise layer, and decoder. To achieve robustness against semantic distortions, we introduce a novel Partial Instruction-driven Denoising Sampling Guidance () module, which consists of a large variety of instruction injections and substantial modifications of images at different semantic levels. With , the encoder tends to embed the watermark into more robust and semantic-aware areas, which remains in existence even after severe image editing. Experiments demonstrate that can effectively extract the watermark from the edited image with a low bit error rate of nearly 2.6% for 64-bit watermark messages. Meanwhile, it only induces a neglectable influence on the visual quality and editability of the original images. blackMoreover, holds general robustness against different sampling configurations and other popular image editing methods such as ControlNet-InstructPix2Pix, MagicBrush, Inpainting, and DDIM Inversion. Codes and models are available at https://github.com/hurunyi/Robust-Wide."
OAPT: Offset-Aware Partition Transformer for Double JPEG Artifacts Removal
Authors: Qiao Mo; Yukang Ding; Jinhua Hao; Qiang Zhu; Ming Sun; Chao Zhou; Feiyu Chen; Shuyuan Zhu
Venue: ECCV 2024 (main)

Abstract: "Deep learning-based methods have shown remarkable performance in single JPEG artifacts removal task. However, existing methods tend to degrade on double JPEG images, which are prevalent in real-world scenarios. To address this issue, we propose Offset-Aware Partition Transformer for double JPEG artifacts removal, termed as OAPT. We conduct an analysis of double JPEG compression that results in up to four patterns within each 8×8 block and design our model to cluster the similar patterns to remedy the difficulty of restoration. Our OAPT consists of two components: compression offset predictor and image reconstructor. Specifically, the predictor estimates pixel offsets between the first and second compression, which are then utilized to divide different patterns. The reconstructor is mainly based on several Hybrid Partition Attention Blocks (HPAB), combining vanilla window-based self-attention and sparse attention for clustered pattern features. Extensive experiments demonstrate that OAPT outperforms the state-of-the-art method by more than 0.16dB in double JPEG image restoration task. Moreover, without increasing any computation cost, the pattern clustering module in HPAB can serve as a plugin to enhance other transformer-based image restoration methods. The code will be available at https://github.com/QMoQ/OAPT.git."
Formula-Supervised Visual-Geometric Pre-training
Authors: Ryosuke Yamada; Kensho Hara; Hirokatsu Kataoka; Koshi Makihara; Nakamasa Inoue; Rio Yokota; Yutaka Satoh
Venue: ECCV 2024 (main)

Abstract: "Throughout the history of computer vision, while research has explored the integration of images (visual) and point clouds (geometric), many advancements in image and 3D object recognition have tended to process these modalities separately. We aim to bridge this divide by integrating images and point clouds on a unified transformer model. This approach integrates the modality-specific properties of images and point clouds and achieves fundamental downstream tasks in image and 3D object recognition on a unified transformer model by learning visual-geometric representations. In this work, we introduce Formula-Supervised Visual-Geometric Pre-training (FSVGP), a novel synthetic pre-training method that automatically generates aligned synthetic images and point clouds from mathematical formulas. Through cross-modality supervision, we enable supervised pre-training between visual and geometric modalities. FSVGP also reduces reliance on real data collection, cross-modality alignment, and human annotation. Our experimental results show that FSVGP pre-trains more effectively than VisualAtom and PC-FractalDB across six tasks: image and 3D object classification, detection, and segmentation. These achievements demonstrate FSVGP’s superior generalization in image and 3D object recognition and underscore the potential of synthetic pre-training in visual-geometric representation learning. Our project website is available at https://ryosuke-yamada.github.io/fdsl-fsvgp/."
VideoAgent: A Memory-augmented Multimodal Agent for Video Understanding
Authors: Yue Fan; Xiaojian Ma; Rujie Wu; yuntao du; Jiaqi Li; Zhi Gao; Qing Li
Venue: ECCV 2024 (main)

Abstract: "We explore how reconciling several foundation models (large language models and vision-language models) with a novel unified memory mechanism could tackle the challenging video understanding problem, especially capturing the long-term temporal relations in lengthy videos. In particular, the proposed multimodal agent : 1) constructs a structured memory to store both the generic temporal event descriptions and object-centric tracking states of the video; 2) given an input task query, it employs tools including video segment localization and object memory querying along with other visual foundation models to interactively solve the task, utilizing the zero-shot tool-use ability of LLMs. demonstrates impressive performances on several long-horizon video understanding benchmarks, an average increase of 6.6% on NExT-QA and 26.0% on EgoSchema over baselines, closing the gap between open-sourced models and private counterparts including Gemini 1.5 Pro. The code and demo can be found at https:// videoagent.github.io."
Towards Unified Representation of Invariant-Specific Features in Missing Modality Face Anti-Spoofing
Authors: Guanghao Zheng; Yuchen Liu; Wenrui Dai; Chenglin Li; Junni Zou; Hongkai Xiong
Venue: ECCV 2024 (main)

Abstract: "The effectiveness of Vision Transformers (ViTs) diminishes considerably in multi-modal face anti-spoofing (FAS) under missing modality scenarios. Existing approaches rely on modality-invariant features to alleviate this issue but ignore modality-specific features. To solve this issue, we propose a Missing Modality Adapter framework for Face Anti-Spoofing (MMA-FAS), which leverages modality-disentangle adapters and LBP-guided contrastive loss for explicit combination of modality-invariant and modality-specific features. Modality-disentangle adapters disentangle features into modality-invariant and -specific features from the view of frequency decomposition. LBP-guided contrastive loss, together with batch-level and sample-level modality masking strategies, forces the model to cluster samples according to attack types and modal combinations, which further enhances modality-specific and -specific features. Moreover, we propose an adaptively modal combination sampling strategy, which dynamically adjusts the sample probability in masking strategies to balance the training process of different modal combinations. Extensive experiments demonstrate that our proposed method achieves state-of-the-art intra-dataset and cross-dataset performance in all the missing modality scenarios."
Restoring Images in Adverse Weather Conditions via Histogram Transformer
Authors: Shangquan Sun; Wenqi Ren; Xinwei Gao; Rui Wang; Xiaochun Cao
Venue: ECCV 2024 (main)

Abstract: "Transformer-based image restoration methods in adverse wea-ther have achieved significant progress. Most of them use self-attention along the channel dimension or within spatially fixed-range blocks to reduce computational load. However, such a compromise results in limitations in capturing long-range spatial features. Inspired by the observation that the weather-induced degradation factors mainly cause similar occlusion and brightness, in this work, we propose an efficient Histogram Transformer (Histoformer) for restoring images affected by adverse weather. It is powered by a mechanism dubbed histogram self-attention, which sorts and segments spatial features into intensity-based bins. Self-attention is then applied across bins or within each bin to selectively focus on spatial features of dynamic range and process similar degraded pixels of the long range together. To boost histogram self-attention, we present a dynamic-range convolution enabling conventional convolution to conduct operation over similar pixels rather than neighbor pixels. We also observe that the common pixel-wise losses neglect linear association and correlation between output and ground-truth. Thus, we propose to leverage the Pearson correlation coefficient as a loss function to enforce the recovered pixels following the identical order as ground-truth. Extensive experiments demonstrate the efficacy and superiority of our proposed method. We have released the codes in Github."
PosFormer: Recognizing Complex Handwritten Mathematical Expression with Position Forest Transformer
Authors: Tongkun Guan; Chengyu Lin; Wei Shen; Xiaokang Yang
Venue: ECCV 2024 (main)

Abstract: "Handwritten Mathematical Expression Recognition (HMER) has wide applications in human-machine interaction scenarios, such as digitized education and automated offices. Recently, sequence-based models with encoder-decoder architectures have been commonly adopted to address this task by directly predicting LaTeX sequences of expression images. However, these methods only implicitly learn the syntax rules provided by LaTeX, which may fail to describe the position and hierarchical relationship between symbols due to complex structural relations and diverse handwriting styles. To overcome this challenge, we propose a position forest transformer (PosFormer) for HMER, which jointly optimizes two tasks: expression recognition and position recognition, to explicitly enable position-aware symbol feature representation learning. Specifically, we first design a position forest that models the mathematical expression as a forest structure and parses the relative position relationships between symbols. Without requiring extra annotations, each symbol is assigned a position identifier in the forest to denote its relative spatial position. Second, we propose an implicit attention correction module to accurately capture attention for HMER in the sequence-based decoder architecture. Extensive experiments validate the superiority of PosFormer, which consistently outperforms the state-of-the-art methods 2.03%/1.22%/2.00%, 1.83%, and 4.62% gains on the single-line CROHME 2014/2016/2019, multi-line M2 E, and complex MNE datasets, respectively, with no additional latency or computational cost."
NGP-RT: Fusing Multi-Level Hash Features with Lightweight Attention for Real-Time Novel View Synthesis
Authors: Yubin Hu; Xiaoyang Guo; Yang Xiao; Jingwei Huang; Yong-Jin Liu
Venue: ECCV 2024 (main)

Abstract: "This paper presents NGP-RT, a novel approach for enhancing the rendering speed of Instant-NGP to achieve real-time novel view synthesis. As a classic NeRF-based method, Instant-NGP stores implicit features in multi-level grids or hash tables and applies a shallow MLP to convert the implicit features into explicit colors and densities. Although it achieves fast training speed, there is still a lot of room for improvement in its rendering speed due to the per-point MLP executions for implicit multi-level feature aggregation, especially for real-time applications. To address this challenge, our proposed NGP-RT explicitly stores colors and densities as hash features, and leverages a lightweight attention mechanism to disambiguate the hash collisions instead of using computationally intensive MLP. At the rendering stage, NGP-RT incorporates a pre-computed occupancy distance grid into the ray marching strategy to inform the distance to the nearest occupied voxel, thereby reducing the number of marching points and global memory access. Experimental results show that on the challenging Mip-NeRF 360 dataset, NGP-RT achieves better rendering quality than previous NeRF-based methods, achieving 108 fps at 1080p resolution on a single Nvidia RTX 3090 GPU. Our approach is promising for NeRF-based real-time applications that require efficient and high-quality rendering."
Elysium: Exploring Object-level Perception in Videos through Semantic Integration Using MLLMs
Authors: Han Wang; Yanjie Wang; Ye Yongjie; Yuxiang Nie; Can Huang
Venue: ECCV 2024 (main)

Abstract: "Multi-modal Large Language Models (MLLMs) have demonstrated their ability to perceive objects in still images, but their application in video-related tasks, such as object tracking, remains understudied. This lack of exploration is primarily due to two key challenges. Firstly, extensive pretraining on large-scale video datasets is required to equip MLLMs with the capability to perceive objects across multiple frames and understand inter-frame relationships. Secondly, processing a large number of frames within the context window of Large Language Models (LLMs) can impose a significant computational burden. To address the first challenge, we introduce , a large-scale video dataset supported for three tasks: Single Object Tracking (SOT), Referring Single Object Tracking (RSOT), and Video Referring Expression Generation (Video-REG). contains 1.27 million annotated video frames with corresponding object boxes and descriptions. Leveraging this dataset, we conduct training of MLLMs and propose a token-compression model T-Selector to tackle the second challenge. Our proposed approach, Elysium: Exploring Object-level Perception in Videos via MLLM, is an end-to-end trainable MLLM that attempts to conduct object-level tasks in videos without requiring any additional plug-in or expert models. All codes and datasets are released at https://github.com/Hon-Wong/Elysium."
G2fR: Frequency Regularization in Grid-based Feature Encoding Neural Radiance Fields
Authors: Shuxiang Xie; Shuyi Zhou; Ken Sakurada; Ryoichi Ishikawa; Masaki Onishi; Takeshi Oishi
Venue: ECCV 2024 (main)

Abstract: "Neural Radiance Field (NeRF) methodologies have garnered considerable interest, particularly with the introduction of grid-based feature encoding (GFE) approaches such as Instant-NGP and TensoRF. Conventional NeRF employs positional encoding (PE) and represents a scene with a Multi-Layer Perceptron (MLP). Frequency regularization has been identified as an effective strategy to overcome primary challenges in PE-based NeRFs, including dependency on known camera poses and the requirement for extensive image datasets. While several studies have endeavored to extend frequency regularization to GFE approaches, there is still a lack of basic theoretical foundations for these methods. Therefore, we first clarify the underlying mechanisms of frequency regularization. Subsequently, we conduct a comprehensive investigation into the expressive capability of GFE-based NeRFs and attempt to connect frequency regularization with GFE methods. Moreover, we propose a generalized strategy, : Generalized Grid-based Frequency Regularization, to address issues of camera pose optimization and few-shot reconstruction with GFE methods. We validate the efficacy of our methods through an extensive series of experiments employing various representations across diverse scenarios."
Getting it Right: Improving Spatial Consistency in Text-to-Image Models
Authors: Agneet Chatterjee; Gabriela Ben Melech Stan; Estelle Guez Aflalo; Sayak Paul; Dhruba Ghosh; Tejas Gokhale; Ludwig Schmidt; Hanna Hajishirzi; Vasudev Lal; Chitta R Baral; Yezhou Yang
Venue: ECCV 2024 (main)

Abstract: "One of the key shortcomings in current text-to-image (T2I) models is their inability to consistently generate images which faithfully follow the spatial relationships specified in the text prompt. In this paper, we offer a comprehensive investigation of this limitation, while also developing datasets and methods that support algorithmic solutions to improve spatial reasoning in T2I models. We find that spatial relationships are under-represented in the image descriptions found in current vision-language datasets. To alleviate this data bottleneck, we create SPRIGHT, the first spatially focused, large-scale dataset, by re-captioning 6 million images from 4 widely used vision datasets and through a 3-fold evaluation and analysis pipeline, show that SPRIGHT improves the proportion of spatial relationships in existing datasets. We show the efficacy of SPRIGHT data by showing that using only ∼0.25% of SPRIGHT results in a 22% improvement in generating spatially accurate images while also improving FID and CMMD scores. We also find that training on images containing a larger number of objects leads to substantial improvements in spatial consistency, including state-of-the-art results on T2I-CompBench with a spatial score of 0.2133, by fine-tuning on ¡500 images. Through a set of controlled experiments and ablations, we document additional findings that could support future work that seeks to understand factors that affect spatial consistency in text-to-image models. Project page : https://spright-t2i.github.io/"