GenAI4ELab/papercli-papers huggingface dataset:
ProtoComp: Diverse Point Cloud Completion with Controllable Prototype
Authors: Xumin Yu; Yanbo Wang; Jie Zhou; Jiwen Lu
Venue: ECCV 2024 (main)

Abstract: "Point cloud completion aims to reconstruct the geometry of partial point clouds captured by various sensors. Traditionally, training a point cloud model is carried out on synthetic datasets, which have limited categories and deviate significantly from real-world scenarios. This disparity often leads existing methods to struggle with unfamiliar categories and severe incompleteness in real-world situations. In this paper, we propose PrototypeCompletion, a novel prototype-based approach for point cloud completion. It begins by generating rough prototypes and subsequently augments them with additional geometry details for the final prediction. With just a few hundred pairs of partial-complete point cloud data, our approach effectively handles the point clouds from diverse scenarios in real-world situations, including indoor ScanNet and outdoor KITTI. Additionally, we propose a new metric and test benchmark based on ScanNet200 and KITTI to evaluate the model’s performance in real-world scenarios, aiming to promote future research. Experimental results demonstrate that our method outperforms state-of-the-art methods on existing PCN benchmark and excels in various real-world situations with different object categories and sensors. Code and dataset are available at https://github.com/Yanbo-23/Proto-Comp."
CONDA: Condensed Deep Association Learning for Co-Salient Object Detection.
Authors: Long Li; Nian Liu; Dingwen Zhang; Zhongyu Li; Salman Khan; Rao Anwer; Hisham Cholakkal; Junwei Han; Fahad Shahbaz Khan
Venue: ECCV 2024 (main)

Abstract: "Inter-image association modeling is crucial for co-salient object detection. Despite satisfactory performance, previous methods still have limitations on sufficient inter-image association modeling. Because most of them focus on image feature optimization under the guidance of heuristically calculated raw inter-image associations. They directly rely on raw associations which are not reliable in complex scenarios, and their image feature optimization approach is not explicit for inter-image association modeling. To alleviate these limitations, this paper proposes a deep association learning strategy that deploys deep networks on raw associations to explicitly transform them into deep association features. Specifically, we first create hyperassociations to collect dense pixel-pair-wise raw associations and then deploys deep aggregation networks on them. We design a progressive association generation module for this purpose with additional enhancement of the hyperassociation calculation. More importantly, we propose a correspondence-induced association condensation module that introduces a pretext task, semantic correspondence estimation, to condense the hyperassociations for computational burden reduction and noise elimination. We also design an object-aware cycle consistency loss for high-quality correspondence estimations. Experimental results in three benchmark datasets demonstrate the remarkable effectiveness of our proposed method with various training settings. The code is available at: https://github.com/dragonlee258079/CONDA."
Cascade Prompt Learning for Visual-Language Model Adaptation
Authors: Ge Wu; Xin Zhang; Zheng Li; Zhaowei Chen; Jiajun Liang; Jian Yang; Xiang Li
Venue: ECCV 2024 (main)

Abstract: "Prompt learning has surfaced as an effective approach to enhance the performance of Vision-Language Models (VLMs) like CLIP when applied to downstream tasks. However, current learnable prompt tokens are primarily used for the single phase of adapting to tasks (i.e., adapting prompt), easily leading to overfitting risks. In this work, we propose a novel Cascade Prompt Learning (CasPL) framework to enable prompt learning to serve both generic and specific expertise (i.e., boosting and adapting prompt) simultaneously. Specifically, CasPL is a new learning paradigm comprising two distinct phases of learnable prompts: the first boosting prompt is crafted to extract domain-general knowledge from a senior larger CLIP teacher model by aligning their predicted logits using extensive unlabeled domain images. The second adapting prompt is then cascaded with the frozen first set to fine-tune the downstream tasks, following the approaches employed in prior research. In this manner, CasPL can effectively capture both domain-general and task-specific representations into explicitly different gradual groups of prompts, thus potentially alleviating overfitting issues in the target domain. It’s worth noting that CasPL serves as a plug-and-play module that can seamlessly integrate into any existing prompt learning approach. CasPL achieves a significantly better balance between performance and inference speed, which is especially beneficial for deploying smaller VLM models in resource-constrained environments. Compared to the previous state-of-the-art method PromptSRC, CasPL shows an average improvement of 1.85% for base classes, 3.44% for novel classes, and 2.72% for the harmonic mean over 11 image classification datasets. Code is publicly available at: https://github.com/ megvii-research/CasPL."
PolyRoom: Room-aware Transformer for Floorplan Reconstruction
Authors: Yuzhou Liu; Lingjie Zhu; Xiaodong Ma; Hanqiao Ye; Xiang Gao; Xianwei Zheng; Shuhan Shen
Venue: ECCV 2024 (main)

Abstract: "Reconstructing geometry and topology structures from raw unstructured data has always been an important research topic in indoor mapping research. In this paper, we aim to reconstruct the floorplan with a vectorized representation from point clouds. Despite significant advancements achieved in recent years, current methods still encounter several challenges, such as missing corners or edges, inaccuracies in corner positions or angles, self-intersecting or overlapping polygons, and potentially implausible topology. To tackle these challenges, we present PolyRoom, a room-aware Transformer that leverages uniform sampling representation, room-aware query initialization, and room-aware self-attention for floorplan reconstruction. Specifically, we adopt a uniform sampling floorplan representation to enable dense supervision during training and effective utilization of angle information. Additionally, we propose a room-aware query initialization scheme to prevent non-polygonal sequences and introduce room-aware self-attention to enhance memory efficiency and model performance. Experimental results on two widely used datasets demonstrate that PolyRoom surpasses current state-of-the-art methods both quantitatively and qualitatively. Our code is available at: https://github.com/3dv-casia/PolyRoom/."
BenchLMM: Benchmarking Cross-style Visual Capability of Large Multimodal Models
Authors: Rizhao Cai; Zirui Song; Dayan Guan; Zhenhao Chen; Yaohang Li; Xing Luo; Chenyu Yi; Alex Kot
Venue: ECCV 2024 (main)

Abstract: "Large Multimodal Models (LMMs) such as GPT-4V and LLaVA have shown remarkable capabilities in visual reasoning on data in common image styles. However, their robustness against diverse style shifts, crucial for practical applications, remains largely unexplored. In this paper, we propose a new benchmark, BenchLMM, to assess the robustness of LMMs toward three different styles: artistic image style, imaging sensor style, and application style. Utilizing BenchLMM, we comprehensively evaluate state-of-the-art LMMs and reveal: 1) LMMs generally suffer performance degradation when working with other styles; 2) An LMM performs better than another model in common style does not guarantee its superior performance in other styles; 3) LMMs’ reasoning capability can be enhanced by prompting LMMs to predict the style first, based on which we propose a versatile and training-free method for improving LMMs; 4) An intelligent LMM is expected to interpret the causes of its errors when facing stylistic variations. We hope that our benchmark and analysis can shed new light on developing more intelligent and versatile LMMs. The benchmark and evaluation have been released on https://github.com/AIFEG/BenchLMM."
SMFANet: A Lightweight Self-Modulation Feature Aggregation Network for Efficient Image Super-Resolution
Authors: mingjun zheng; Long Sun; Jiangxin Dong; Jinshan Pan
Venue: ECCV 2024 (main)

Abstract: "Transformer-based restoration methods achieve significant performance as the self-attention (SA) of the Transformer can explore non-local information for better high-resolution image reconstruction. However, the key dot-product SA requires substantial computational resources, which limits its application in low-power devices. Moreover, the low-pass nature of the SA mechanism limits its capacity for capturing local details, consequently leading to smooth reconstruction results. To address these issues, we propose a self-modulation feature aggregation (SMFA) module to collaboratively exploit both local and non-local feature interactions for a more accurate reconstruction. Specifically, the SMFA module employs an efficient approximation of self-attention (EASA) branch to model non-local information and uses a local detail estimation (LDE) branch to capture local details. Additionally, we further introduce a partial convolution-based feed-forward network (PCFN) to refine the representative features derived from the SMFA. Extensive experiments show that the proposed SMFANet family achieve a better trade-off between reconstruction performance and computational efficiency on public benchmark datasets. In particular, compared to the ×4 SwinIR-light, SMFANet+ achieves 0.14dB higher performance over five public testsets on average, and ×10 times faster runtime, with only about 43% of the model complexity (e.g., FLOPs). Our source codes and pre-trained models are available at: https://github.com/Zheng-MJ/SMFANet."
HENet: Hybrid Encoding for End-to-end Multi-task 3D Perception from Multi-view Cameras
Authors: Zhongyu Xia; ZhiWei Lin; Xinhao Wang; Yongtao Wang; Yun Xing; Shengxiang Qi; Nan Dong; Ming-Hsuan Yang
Venue: ECCV 2024 (main)

Abstract: "Three-dimensional perception from multi-view cameras is a crucial component in autonomous driving systems, which involves multiple tasks like 3D object detection and bird’s-eye-view (BEV) semantic segmentation. To improve perception precision, large image encoders, high-resolution images, and long-term temporal inputs have been adopted in recent 3D perception models, bringing remarkable performance gains. However, these techniques are often incompatible in training and inference scenarios due to computational resource constraints. Besides, modern autonomous driving systems prefer to adopt an end-to-end framework for multi-task 3D perception, which can simplify the overall system architecture and reduce the implementation complexity. However, conflict between tasks often arises when optimizing multiple tasks jointly within an end-to-end 3D perception model. To alleviate these issues, we present an end-to-end framework named HENet for multi-task 3D perception in this paper. Specifically, we propose a hybrid image encoding network, using a large image encoder for short-term frames and a small image encoder for long-term temporal frames. Then, we introduce a temporal feature integration module based on the attention mechanism to fuse the features of different frames extracted by the two aforementioned hybrid image encoders. Finally, according to the characteristics of each perception task, we utilize BEV features of different grid sizes, independent BEV encoders, and task decoders for different tasks. Experimental results show that HENet achieves state-of-the-art end-to-end multi-task 3D perception results on the nuScenes benchmark, including 3D object detection and BEV semantic segmentation."
Hierarchical Unsupervised Relation Distillation for Source Free Domain Adaptation
Authors: Bowei Xing; Xianghua Ying; Ruibin Wang; Ruohao Guo; Ji Shi; Wenzhen Yue
Venue: ECCV 2024 (main)

Abstract: "Source free domain adaptation (SFDA) aims to transfer the model trained on labeled source domain to unlabeled target domain without accessing source data. Recent SFDA methods predominantly rely on self-training, which supervise the model with pseudo labels generated from individual data samples. However, they often ignore the crucial data structure information and sample relationships that are beneficial for adaptive training. In this paper, we propose a novel hierarchical relation distillation framework, establishing multi-level relations across samples in an unsupervised manner, which fully exploits inherent data structure to guide sample training instead of using isolated pseudo labels. We first distinguish source-like samples based on prediction reliability during training, followed by an effort on distilling knowledge to those target-specific ones by transferring both local clustering relation and global semantic relation. Specifically, we leverage the affinity with nearest neighborhood samples for local relation and consider the similarity to category-wise Gaussian Mixtures for global relation, offering complementary supervision to facilitate student learning. To validate our approach’s effectiveness, we conduct extensive experiments on diverse benchmarks, achieving better performance compared to previous methods."
Customized Generation Reimagined: Fidelity and Editability Harmonized
Authors: Jian Jin; Yang Shen; Zhenyong Fu; Jian Yang
Venue: ECCV 2024 (main)

Abstract: "Customized generation aims to incorporate a novel concept into a pre-trained text-to-image model, enabling new generations of the concept in novel contexts guided by textual prompts. However, customized generation suffers from an inherent trade-off between concept fidelity and editability, i.e., between precisely modeling the concept and faithfully adhering to the prompts. Previous methods reluctantly seek a compromise and struggle to achieve both high concept fidelity and ideal prompt alignment simultaneously. In this paper, we propose a “Divide, Conquer, then Integrate” (DCI) framework, which performs a surgical adjustment in the early stage of denoising to liberate the fine-tuned model from the fidelity-editability trade-off at inference. The two conflicting components in the trade-off are decoupled and individually conquered by two collaborative branches, which are then selectively integrated to preserve high concept fidelity while achieving faithful prompt adherence. To obtain a better fine-tuned model, we introduce an Image-specific Context Optimization (ICO) strategy for model customization. ICO replaces manual prompt templates with learnable image-specific contexts, providing an adaptive and precise fine-tuning direction to promote the overall performance. Extensive experiments demonstrate the effectiveness of our method in reconciling the fidelity-editability trade-off. Code is available at https://github.com/jinjianRick/DCI ICO."
AUFormer: Vision Transformers are Parameter-Efficient Facial Action Unit Detectors
Authors: Kaishen Yuan; Zitong Yu; Xin Liu; Weicheng Xie; Huanjing Yue; Jingyu Yang
Venue: ECCV 2024 (main)

Abstract: "Facial Action Units (AU) is a vital concept in the realm of affective computing, and AU detection has always been a hot research topic. Existing methods suffer from overfitting issues due to the utilization of a large number of learnable parameters on scarce AU-annotated datasets or heavy reliance on substantial additional relevant data. Parameter-Efficient Transfer Learning (PETL) provides a promising paradigm to address these challenges, whereas its existing methods lack design for AU characteristics. Therefore, we innovatively investigate PETL paradigm to AU detection, introducing AUFormer and proposing a novel Mixture-of-Knowledge Expert (MoKE) collaboration mechanism. An individual MoKE specific to a certain AU with minimal learnable parameters first integrates personalized multi-scale and correlation knowledge. Then the MoKE collaborates with other MoKEs in the expert group to obtain aggregated information and inject it into the frozen Vision Transformer (ViT) to achieve parameter-efficient AU detection. Additionally, we design a Margin-truncated Difficulty-aware Weighted Asymmetric Loss (MDWA-Loss), which can encourage the model to focus more on activated AUs, differentiate the difficulty of unactivated AUs, and discard potential mislabeled samples. Extensive experiments from various perspectives, including within-domain, cross-domain, data efficiency, and micro-expression domain, demonstrate AUFormer’s state-of-the-art performance and robust generalization abilities without relying on additional relevant data. The code for AUFormer is available at https://github.com/yuankaishen2001/AUFormer."
Improving Video Segmentation via Dynamic Anchor Queries
Authors: Yikang Zhou; Tao Zhang; Xiangtai Li; Shunping Ji; Shuicheng Yan
Venue: ECCV 2024 (main)

Abstract: "Modern video segmentation methods adopt feature transitions between anchor and target queries to perform cross-frame object association. The smooth feature transitions between anchor and target queries enable these methods to achieve satisfactory performance when tracking continuously appearing objects. However, the emergence and disappearance of objects interrupt the smooth feature transition, and even widen this feature transition gap between anchor and target queries, which causes these methods to all underperform on newly emerging and disappearing objects that are common in the real world. We introduce Dynamic Anchor Queries (DAQ) to shorten the transition gap by dynamically generating anchor queries based on the features of potential newly emerging and disappearing candidates. Furthermore, we introduce a query-level object Emergence and Disappearance Simulation (EDS) strategy, which unleashes DAQ’s potential without any additional cost. Finally, we combine our proposed DAQ and EDS with the previous method, DVIS, to obtain DVIS-DAQ. Extensive experiments demonstrate that DVIS-DAQ achieves a new state-of-the-art (SOTA) performance on five mainstream video segmentation benchmarks."
Controllable Contextualized Image Captioning: Directing the Visual Narrative through User-Defined Highlights
Authors: Shunqi Mao; Chaoyi Zhang; Hang Su; Hwanjun Song; Igor Shalyminov; Weidong Cai
Venue: ECCV 2024 (main)

Abstract: "(CIC) evolves traditional image captioning into a more complex domain, necessitating the ability for multimodal reasoning. It aims to generate image captions given specific contextual information. This paper further introduces a novel domain of (). Unlike CIC, which solely relies on broad context, accentuates a user-defined highlight, compelling the model to tailor captions that resonate with the highlighted aspects of the context. We present two approaches, Prompting-based Controller () and Recalibration-based Controller (), to generate focused captions. conditions the model generation on highlight by prepending captions with highlight-driven prefixes, whereas tunes the model to selectively recalibrate the encoder embeddings for highlighted tokens. Additionally, we design a GPT-4V empowered evaluator to assess the quality of the controlled captions alongside standard assessment methods. Extensive experimental results demonstrate the efficient and effective controllability of our method, charting a new direction in achieving user-adaptive image captioning. Code is avaliable at https://github.com/ShunqiM/Ctrl-CIC."
Diffusion Models as Optimizers for Efficient Planning in Offline RL
Authors: Renming Huang; Yunqiang Pei; Guoqing Wang; Yangming Zhang; Yang Yang; Peng Wang; Heng Tao Shen
Venue: ECCV 2024 (main)

Abstract: "Diffusion models have shown strong competitiveness in offline reinforcement learning tasks by formulating decision-making as sequential generation. However, the practicality of these methods is limited due to the lengthy inference processes they require. In this paper, we address this problem by decomposing the sampling process of diffusion models into two decoupled subprocesses: 1) generating a feasible trajectory, which is a time-consuming process, and 2) optimizing the trajectory. With this decomposition approach, we are able to partially separate efficiency and quality factors, enabling us to simultaneously gain efficiency advantages and ensure quality assurance. We propose the Trajectory Diffuser, which utilizes a faster autoregressive model to handle the generation of feasible trajectories while retaining the trajectory optimization process of diffusion models. This allows us to achieve more efficient planning without sacrificing capability. To evaluate the effectiveness and efficiency of the Trajectory Diffuser, we conduct experiments on the D4RL benchmarks. The results demonstrate that our method achieves 3 -10 × faster inference speed compared to previous sequence modeling methods, while also outperforming them in terms of overall performance. https: //github.com/RenMing-Huang/TrajectoryDiffuser"
Enhanced Sparsification via Stimulative Training
Authors: Shengji Tang; Weihao Lin; Hancheng Ye; Peng Ye; Chong Yu; Baopu Li; Tao Chen
Venue: ECCV 2024 (main)

Abstract: "Sparsification-based pruning has been an important category in model compression. Existing methods commonly set sparsity-inducing penalty terms to suppress the importance of dropped weights, which is regarded as the suppressed sparsification paradigm. However, this paradigm inactivates the dropped parts of networks causing capacity damage before pruning, thereby leading to performance degradation. To alleviate this issue, we first study and reveal the relative sparsity effect in emerging stimulative training. Based on the sparsity effect, we propose a structured pruning framework, named STP. It is based on an enhanced sparsification paradigm which maintains the magnitude of dropped weights and enhances the expressivity of kept weights by self-distillation. Besides, to find an optimal architecture for the pruned network, we propose a multi-dimension architecture space and a knowledge distillation-guided exploration strategy. To reduce the huge capacity gap of distillation, we propose a subnet mutating expansion technique. Extensive experiments on various benchmarks indicate the effectiveness of STP. Specifically, without fine-tuning, our method consistently achieves superior performance at different budgets, especially under extremely aggressive pruning scenarios, e.g., remaining 95.11% Top-1 accuracy (72.43% in 76.15%) while reducing 85% FLOPs for ResNet-50 on ImageNet. Codes are at https:// github.com/tsj-001/STP."
How Many Unicorns Are in This Image? A Safety Evaluation Benchmark for Vision LLMs
Authors: Haoqin Tu; Chenhang Cui; Zijun Wang; Yiyang Zhou; Bingchen Zhao; Junlin Han; Wangchunshu Zhou; Huaxiu Yao; Cihang Xie
Venue: ECCV 2024 (main)

Abstract: "This work focuses on benchmarking the capabilities of vision large language models (VLLMs) in visual reasoning. Different from prior studies, we shift our focus from evaluating standard performance to introducing a comprehensive safety evaluation suite Unicorn, covering out-of-distribution (OOD) generalization and adversarial robustness. For the OOD evaluation, we present two novel visual question-answering (VQA) datasets, each with one variant, designed to test model performance under challenging conditions. In exploring adversarial robustness, we propose a straightforward attack strategy for misleading VLLMs to produce visual-unrelated responses. Moreover, we assess the efficacy of two jailbreaking strategies, targeting either the vision or language input of VLLMs. Our evaluation of 22 diverse models, ranging from open-source VLLMs to GPT-4V and Gemini Pro, yields interesting observations: 1) Current VLLMs struggle with OOD texts but not images, unless the visual information is limited; and 2) These VLLMs can be easily misled by deceiving vision encoders only, and their vision-language training often compromise safety protocols. We release this safety evaluation suite at https://github.com/UCSC-VLAA/vllm-safety-benchmark."