GenAI4ELab/papercli-papers huggingface dataset:
Take A Step Back: Rethinking the Two Stages in Visual Reasoning
Authors: Mingyu Zhang; Jiting Cai; Mingyu Liu; Yue Xu; Cewu Lu; Yong-Lu Li
Venue: ECCV 2024 (main)

Abstract: "As a prominent research area, visual reasoning plays a crucial role in AI by facilitating concept formation and interaction with the world. However, current works are usually carried out separately on small datasets thus lacking generalization ability. Through rigorous evaluation of diverse benchmarks, we demonstrate the shortcomings of existing ad-hoc methods in achieving cross-domain reasoning and their tendency to data bias fitting. In this paper, we revisit visual reasoning with a two-stage perspective: (1) symbolization and (2) logical reasoning given symbols or their representations. We find that the reasoning stage is better at generalization than symbolization. Thus, it is more efficient to implement symbolization via separated encoders for different data domains while using a shared reasoner. Given our findings, we establish design principles for visual reasoning frameworks following the separated symbolization and shared reasoning. The proposed two-stage framework achieves impressive generalization ability on various visual reasoning tasks, including puzzles, physical prediction, and visual question answering (VQA), encompassing 2D and 3D modalities. We believe our insights will pave the way for generalizable visual reasoning. Our code is publicly available at https://mybearyzhang.github.io/projects/ TwoStageReason."
AlignZeg: Mitigating Objective Misalignment for Zero-shot Semantic Segmentation
Authors: Jiannan Ge; Lingxi Xie; Hongtao Xie; Pandeng Li; Xiaopeng Zhang; Yongdong Zhang; Qi Tian
Venue: ECCV 2024 (main)

Abstract: "A serious issue that harms the performance of zero-shot visual recognition is named objective misalignment, i.e., the learning objective prioritizes improving the recognition accuracy of seen classes rather than unseen classes, while the latter is the true target to pursue. This issue becomes more significant in zero-shot image segmentation because the stronger (i.e., pixel-level) supervision brings a larger gap between seen and unseen classes. To mitigate it, we propose a novel architecture named AlignZeg, which embodies a comprehensive improvement of the segmentation pipeline, including proposal extraction, classification, and correction, to better fit the goal of zero-shot segmentation. (1) Mutually-Refined Proposal Extraction. AlignZeg harnesses a mutual interaction between mask queries and visual features, facilitating detailed class-agnostic mask proposal extraction. (2) Generalization-Enhanced Proposal Classification. AlignZeg introduces synthetic data and incorporates multiple background prototypes to allocate a more generalizable feature space. (3) Predictive Bias Correction. During the inference stage, AlignZeg uses a class indicator to find potential unseen class proposals followed by a prediction postprocess to correct the prediction bias. Experiments demonstrate that AlignZeg markedly enhances zero-shot semantic segmentation, as shown by an average 3.8% increase in hIoU, primarily attributed to a 7.1% improvement in identifying unseen classes, and we further validate that the improvement comes from alleviating the objective misalignment issue."
Learning with Counterfactual Explanations for Radiology Report Generation
Authors: Mingjie Li; Haokun Lin; Liang Qiu; Xiaodan Liang; Ling Chen; Abdulmotaleb Elsaddik; Xiaojun Chang
Venue: ECCV 2024 (main)

Abstract: "Due to the common content of anatomy, radiology images with their corresponding reports exhibit high similarity. Such inherent data bias can predispose automatic report generation models to learn entangled and spurious representations resulting in misdiagnostic reports. To tackle these, we propose a novel CounterFactual Explanations-based framework (CoFE) for radiology report generation. Counterfactual explanations serve as a potent tool for understanding how decisions made by algorithms can be changed by asking “what if” scenarios. By leveraging this concept, CoFE can learn non-spurious visual representations by contrasting the representations between factual and counterfactual images. Specifically, we derive counterfactual images by swapping a patch between positive and negative samples until a predicted diagnosis shift occurs. Here, positive and negative samples are the most semantically similar but have different diagnosis labels. Additionally, CoFE employs a learnable prompt to efficiently fine-tune the pre-trained large language model, encapsulating both factual and counterfactual content to provide a more generalizable prompt representation. Extensive experiments on two benchmarks demonstrate that leveraging the counterfactual explanations enables CoFE to generate semantically coherent and factually complete reports and outperform in terms of language generation and clinical efficacy metrics."
SpeedUpNet: A Plug-and-Play Adapter Network for Accelerating Text-to-Image Diffusion Models
Authors: Weilong Chai; Dandan Zheng; Jiajiong Cao; Zhiquan Chen; Changbao Wang; Chenguang Ma
Venue: ECCV 2024 (main)

Abstract: "Text-to-image diffusion models (SD) exhibit significant advancements while requiring extensive computational resources. Existing acceleration methods usually require extensive training and are not universally applicable. LCM-LoRA, trainable once for diverse models, offers universality but rarely considers ensuring the consistency of generated content before and after acceleration. This paper proposes SpeedUpNet (SUN), an innovative acceleration module, to address the challenges of universality and consistency. Exploiting the role of cross-attention layers in U-Net for SD models, we introduce an adapter specifically designed for these layers, quantifying the offset in image generation caused by negative prompts relative to positive prompts. This learned offset demonstrates stability across a range of models, enhancing SUN’s universality. To improve output consistency, we propose a Multi-Step Consistency (MSC) loss, which stabilizes the offset and ensures fidelity in accelerated content. Experiments on SD v1.5 show that SUN leads to an overall speedup of more than 10 times compared to the baseline 25-step DPM-solver++, and offers two extra advantages: (1) training-free integration into various fine-tuned Stable-Diffusion models and (2) state-of-the-art FIDs of the generated data set before and after acceleration guided by random combinations of positive and negative prompts. Code is available1 . 1 Project: https://williechai.github.io/speedup-plugin-for-stable-diffusions.github.io"
Better Regression Makes Better Test-time Adaptive 3D Object Detection
Authors: Jiakang Yuan; Bo Zhang; Kaixiong Gong; Xiangyu Yue; Botian Shi; Yu Qiao; Tao Chen
Venue: ECCV 2024 (main)

Abstract: "Domain Adaptation (DA) has been widely explored and made significant progress on cross-domain 3D tasks recently. Despite being effective, existing works fail to deal with rapidly changing domains due to the unpredictable test time scenarios and meanwhile fast response time requirement. Thus, we explore a new task named test-time domain adaptive 3D object detection and propose Reg-TTA3D, a pseudo-label-based test-time adaptative 3D object detection method. By investigating the factor that limits the detection accuracy, we find that regression is essential in this task. To make better regression, we first design a noise-consistency pseudo-label generation process to filter pseudo-labels with instability under noise interference and obtain reliable pseudo-labels. Then, confidence-guided regression refinement is introduced, which uses the box regression results of high-confidence boxes to supervise boxes with relatively low confidence, further making the predicted box size gradually approach the distribution of the target domain. Finally, to better update the regression layer and alleviate the class-imbalance issue, a class-balance EMA updating strategy is proposed. Experimental results on multiple cross-domain scenarios including cross-beam, cross-location, and cross-weather demonstrate that Reg-TTA3D can achieve comparable or even better performance compared to unsupervised domain adaptation works by only updating less than 0.1% parameters within less than 1% time."
ShapeLLM: Universal 3D Object Understanding for Embodied Interaction
Authors: Zekun Qi; Runpei Dong; Shaochen Zhang; Haoran Geng; Chunrui Han; Zheng Ge; Li Yi; Kaisheng Ma
Venue: ECCV 2024 (main)

Abstract: "This paper presents , the first 3D Multimodal Large Language Model (LLM) designed for embodied interaction, exploring a universal 3D object understanding with 3D point clouds and languages. is built upon an improved 3D encoder by extending [?] to that benefits from multi-view image distillation for enhanced geometry understanding. By utilizing as the 3D point cloud input encoder for LLMs, is trained on constructed instruction-following data and tested on our newly human-curated benchmark, 3D MM-Vet. and achieve state-of-the-art performance in 3D geometry understanding and language-unified 3D interaction tasks, such as embodied visual grounding."
Content-Aware Radiance Fields: Aligning Model Complexity with Scene Intricacy Through Learned Bitwidth Quantization
Authors: Weihang Liu; Xue Xian Zheng; Jingyi Yu; Xin Lou
Venue: ECCV 2024 (main)

Abstract: "The recent popular radiance field models, exemplified by Neural Radiance Fields (NeRF), Instant-NGP and 3D Gaussian Splatting, are designed to represent 3D content by that training models for each individual scene. This unique characteristic of scene representation and per-scene training distinguishes radiance field models from other neural models, because complex scenes necessitate models with higher representational capacity and vice versa. In this paper, we propose content-aware radiance fields, aligning the model complexity with the scene intricacies through Adversarial Content-Aware Quantization (A-CAQ). Specifically, we make the bitwidth of parameters differentiable and trainable, tailored to the unique characteristics of specific scenes and requirements. The proposed framework has been assessed on Instant-NGP, a well-known NeRF variant and evaluated using various datasets. Experimental results demonstrate a notable reduction in computational complexity, while preserving the requisite reconstruction and rendering quality, making it beneficial for practical deployment of radiance fields models. Codes are available at https://github.com/WeihangLiu2024/ Content_Aware_NeRF."
Finding Visual Task Vectors
Authors: Alberto Hojel; Yutong Bai; Trevor Darrell; Amir Globerson; Amir Bar
Venue: ECCV 2024 (main)

Abstract: "Visual Prompting is a technique for teaching models to perform a visual task via in-context examples, without any additional training. In this work, we analyze the activations of MAE-VQGAN, a recent Visual Prompting model [?], and find Task Vectors, activations that encode task-specific information. Equipped with this insight, we demonstrate that it is possible to identify the Task Vectors and use them to guide the network towards performing different tasks without having to provide any in-context input-output examples. To find Task Vectors, we compute the mean activations of the attention heads in the model per task and use the REINFORCE [?] algorithm to patch into a subset of them with a new query image. The resulting Task Vectors guide the model towards performing the task better than the original model.1 1 For code and models see www.github.com/alhojel/visual_task_vectors"
Connecting Consistency Distillation to Score Distillation for Text-to-3D Generation
Authors: Zongrui Li; Minghui Hu; Qian Zheng; Xudong Jiang
Venue: ECCV 2024 (main)

Abstract: "Although recent advancements in text-to-3D generation have significantly improved generation quality, issues like limited level of detail and low fidelity still persist, which requires further improvement. To understand the essence of those issues, we thoroughly analyze current score distillation methods by connecting theories of consistency distillation to score distillation. Based on the insights acquired through analysis, we propose an optimization framework, Guided Consistency Sampling (GCS), integrated with 3D Gaussian Splatting (3DGS) to alleviate those issues. Additionally, we have observed the persistent oversaturation in the rendered views of generated 3D assets. From experiments, we find that it is caused by unwanted accumulated brightness in 3DGS during optimization. To mitigate this issue, we introduce a Brightness-Equalized Generation (BEG) scheme in 3DGS rendering. Experimental results demonstrate that our approach generates 3D assets with more details and higher fidelity than state-of-the-art methods. The codes are released at https://github.com/ LMozart/ECCV2024-GCS-BEG."
Event Camera Data Dense Pre-training
Authors: Yan Yang; Liyuan Pan; Liu liu
Venue: ECCV 2024 (main)

Abstract: "This paper introduces a self-supervised learning framework designed for pre-training neural networks tailored to dense prediction tasks using event camera data. Our approach utilizes solely event data for training. Transferring achievements from dense RGB pre-training directly to event camera data yields subpar performance. This is attributed to the spatial sparsity inherent in an event image (converted from event data), where many pixels do not contain information. To mitigate this sparsity issue, we encode an event image into event patch features, automatically mine contextual similarity relationships among patches, group the patch features into distinctive contexts, and enforce context-to-context similarities to learn discriminative event features. For training our framework, we curate a synthetic event camera dataset featuring diverse scene and motion patterns. Transfer learning performance on downstream dense prediction tasks illustrates the superiority of our method over state-of-the-art approaches."
Distractors-Immune Representation Learning with Cross-modal Contrastive Regularization for Change Captioning
Authors: Yunbin Tu; Liang Li; Li Su; Chenggang Yan; Qingming Huang
Venue: ECCV 2024 (main)

Abstract: "Change captioning aims to succinctly describe the semantic change between a pair of similar images, while being immune to distractors (illumination and viewpoint changes). Under these distractors, unchanged objects often appear pseudo changes about location and scale, and certain objects might overlap others, resulting in perturbational and discrimination-degraded features between two images. However, most existing methods directly capture the difference between them, which risk obtaining error-prone difference features. In this paper, we propose a distractors-immune representation learning network that correlates the corresponding channels of two image representations and decorrelates different ones in a self-supervised manner, thus attaining a pair of stable image representations under distractors. Then, the model can better interact them to capture the reliable difference features for caption generation. To yield words based on the most related difference features, we further design a cross-modal contrastive regularization, which regularizes the cross-modal alignment by maximizing the contrastive alignment between the attended difference features and generated words. Extensive experiments show that our method outperforms the state-of-the-art methods on four public datasets. The code is available at https://github.com/ tuyunbin/DIRL."
Rethinking Image-to-Video Adaptation: An Object-centric Perspective
Authors: Rui Qian; Shuangrui Ding; Dahua Lin
Venue: ECCV 2024 (main)

Abstract: "Image-to-video adaptation seeks to efficiently adapt image models for use in the video domain. Instead of finetuning the entire image backbone, many image-to-video adaptation paradigms use lightweight adapters for temporal modeling on top of the spatial module. However, these attempts are subject to limitations in efficiency and interpretability. In this paper, we propose a novel and efficient image-to-video adaptation strategy from the object-centric perspective. Inspired by human perception, which identifies objects as key components for video understanding, we integrate a proxy task of object discovery into image-to-video transfer learning. Specifically, we adopt slot attention with learnable queries to distill each frame into a compact set of object tokens. These object-centric tokens are then processed through object-time interaction layers to model object state changes across time. Integrated with two novel object-level losses, we demonstrate the feasibility of performing efficient temporal reasoning solely on the compressed object-centric representations for video downstream tasks. Our method achieves state-of-the-art performance with fewer tunable parameters, only 5% of fully finetuned models and 50% of efficient tuning methods, on action recognition benchmarks. In addition, our model performs favorably in zero-shot video object segmentation without further retraining or object annotations, proving the effectiveness of object-centric video understanding."
Layer-Wise Relevance Propagation with Conservation Property for ResNet
Authors: Seitaro Otsuki; Tsumugi Iida; Félix Doublet; Tsubasa Hirakawa; Takayoshi Yamashita; Hironobu Fujiyoshi; Komei Sugiura
Venue: ECCV 2024 (main)

Abstract: "The transparent formulation of explanation methods is essential for elucidating the predictions of neural networks, which are typically black-box models. Layer-wise Relevance Propagation (LRP) is a well-established method that transparently traces the flow of a model’s prediction backward through its architecture by backpropagating relevance scores. However, the conventional LRP does not fully consider the existence of skip connections, and thus its application to the widely used ResNet architecture has not been thoroughly explored. In this study, we extend LRP to ResNet models by introducing Relevance Splitting at points where the output from a skip connection converges with that from a residual block. Our formulation guarantees the conservation property throughout the process, thereby preserving the integrity of the generated explanations. To evaluate the effectiveness of our approach, we conduct experiments on ImageNet and the Caltech-UCSD Birds-200-2011 dataset. Our method achieves superior performance to that of baseline methods on standard evaluation metrics such as the Insertion-Deletion score while maintaining its conservation property. We will release our code for further research at https://5ei74r0.github.io/ lrp-for-resnet.page/"
DECap: Towards Generalized Explicit Caption Editing via Diffusion Mechanism
Authors: Zhen Wang; Xinyun Jiang; Jun Xiao; Tao Chen; Long Chen
Venue: ECCV 2024 (main)

Abstract: "Explicit Caption Editing (ECE) — refining reference image captions through a sequence of explicit edit operations (, KEEP, DETELE) — has raised significant attention due to its explainable and human-like nature. After training with carefully designed reference and ground-truth caption pairs, state-of-the-art ECE models exhibit limited generalization ability beyond the original training data distribution, , they are tailored to refine content details only in in-domain samples but fail to correct errors in out-of-domain samples. To this end, we propose a new Diffusion-based Explicit Caption editing method: DECap. Specifically, we reformulate the ECE task as a denoising process under the diffusion mechanism, and introduce innovative edit-based noising and denoising processes. Thanks to this design, the noising process can help to eliminate the need for meticulous paired data selection by directly introducing word-level noises for training, learning diverse distribution over input reference caption. The denoising process involves the explicit predictions of edit operations and corresponding content words, refining reference captions through iterative step-wise editing. To further efficiently implement our diffusion process and improve the inference speed, DECap discards the prevalent multi-stage design and directly generates edit operations and content words simultaneously. Extensive ablations have demonstrated the strong generalization ability of DECap in various scenarios. More interestingly, it even shows great potential in improving the quality and controllability of caption generation."
EgoLifter: Open-world 3D Segmentation for Egocentric Perception
Authors: Qiao Gu; Zhaoyang Lv; Duncan Frost; Simon Green; Julian Straub; Chris Sweeney
Venue: ECCV 2024 (main)

Abstract: "In this paper we present , a novel system that can automatically segment scenes captured from egocentric sensors into a complete decomposition of individual 3D objects. The system is specifically designed for egocentric data where scenes contain hundreds of objects captured from natural (non-scanning) motion. adopts 3D Gaussians as the underlying representation of 3D scenes and objects and uses segmentation masks from the Segment Anything Model (SAM) as weak supervision to learn flexible and promptable definitions of object instances free of any specific object taxonomy. To handle the challenge of dynamic objects in ego-centric videos, we design a transient prediction module that learns to filter out dynamic objects in the 3D reconstruction. The result is a fully automatic pipeline that is able to reconstruct 3D object instances as collections of 3D Gaussians that collectively compose the entire scene. We created a new benchmark on the Aria Digital Twin dataset that quantitatively demonstrates its state-of-the-art performance in open-world 3D segmentation from natural egocentric input. We run on various egocentric activity datasets which shows the promise of the method for 3D egocentric perception at scale. Please visit project page at https://egolifter.github.io/."
MEVG : Multi-event Video Generation with Text-to-Video Models
Authors: Gyeongrok Oh; Jaehwan Jeong; Sieun Kim; Wonmin Byeon; Jinkyu Kim; Sungwoong Kim; Sangpil Kim
Venue: ECCV 2024 (main)

Abstract: "We introduce a novel diffusion-based video generation method, generating a video showing multiple events given multiple individual sentences from the user. Our method does not require a large-scale video dataset since our method uses a pre-trained diffusion-based text-to-video generative model without a fine-tuning process. Specifically, we propose a last frame-aware diffusion process to preserve visual coherence between consecutive videos where each video consists of different events by initializing the latent and simultaneously adjusting noise in the latent to enhance the motion dynamic in a generated video. Furthermore, we find that the iterative update of latent vectors by referring to all the preceding frames maintains the global appearance across the frames in a video clip. To handle dynamic text input for video generation, we utilize a novel prompt generator that transfers course text messages from the user into the multiple optimal prompts for the text-to-video diffusion model. Extensive experiments and user studies show that our proposed method is superior to other video-generative models in terms of temporal coherency of content and semantics. Video examples are available on our project page: https://kuai-lab.github.io/eccv2024mevg."