mtybilly/Instruct2DS huggingface dataset:
Revisit Event Generation Model: Self-Supervised Learning of Event-to-Video Reconstruction with Implicit Neural Representations
Authors: Zipeng Wang*, yunfan lu, Lin Wang*
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "Reconstructing intensity frames from event data while maintaining high temporal resolution and dynamic range is crucial for bridging the gap between event-based and frame-based computer vision. Previous approaches have depended on supervised learning on synthetic data, which lacks interpretability and risk over-fitting to the setting of the event simulator. Recently, self-supervised learning (SSL) based methods, which primarily utilize per-frame optical flow to estimate intensity via photometric constancy, has been actively investigated. However, they are vulnerable to errors in the case of inaccurate optical flow. This paper proposes a novel SSL event-to-video reconstruction approach, dubbed EvINR, which eliminates the need for labeled data or optical flow estimation. Our core idea is to reconstruct intensity frames by directly addressing the event generation model, essentially a partial differential equation (PDE) that describes how events are generated based on the time-varying brightness signals. Specifically, we utilize an implicit neural representation (INR), which takes in spatiotemporal coordinate (x, y, t) and predicts intensity values, to represent the solution of the event generation equation. The INR, parameterized as a fully-connected Multi-layer Perceptron (MLP), can be optimized with its temporal derivatives supervised by events. To make EvINR feasible for online requisites, we propose several acceleration techniques that substantially expedite the training process. Comprehensive experiments demonstrate that our EvINR surpasses previous SSL methods by 38% w.r.t. Mean Squared Error (MSE) and is comparable or superior to SoTA supervised methods. Project page: https://vlislab22.github.io/EvINR/."
SDPT: Synchronous Dual Prompt Tuning for Fusion-based Visual-Language Pre-trained Models
Authors: Yang Zhou*, Yongjian Wu, Jiya Saiyin, Bingzheng Wei, Maode Lai, Eric I Chang, Yan Xu*
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "Prompt tuning methods have achieved remarkable success in parameter-efficient fine-tuning on large pre-trained models. However, their application to dual-modal fusion-based visual-language pre-trained models (VLPMs), such as GLIP, has encountered issues. Existing prompt tuning methods have not effectively addressed the modal mapping and aligning problem for tokens in different modalities, leading to poor transfer generalization. To address this issue, we propose Synchronous Dual Prompt Tuning (SDPT). SDPT initializes a single set of learnable unified prototype tokens in the established modal aligning space to represent the aligned semantics of text and image modalities for downstream tasks. Furthermore, SDPT establishes inverse linear projections that require no training to embed the information of unified prototype tokens into the input space of different modalities. The inverse linear projections allow the unified prototype token to synchronously represent the two modalities and enable SDPT to share the unified semantics of text and image for downstream tasks across different modal prompts. Experimental results demonstrate that SDPT assists fusion-based VLPMs to achieve superior outcomes with only 0.04% of model parameters for training across various scenarios, outperforming other single- or dual-modal methods. The code will be released at https://github.com/wuyongjianCODE/SDPT."
Open-World Dynamic Prompt and Continual Visual Representation Learning
Authors: Youngeun Kim, Jun Fang*, Qin Zhang, Zhaowei Cai, Yantao Shen, Rahul Duggal, Dripta S. Raychaudhuri, Zhuowen Tu, Yifan Xing, Onkar Dabeer
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "The open world is inherently dynamic, characterized by ever-evolving concepts and distributions. Continual learning (CL) in this dynamic open-world environment presents a significant challenge in effectively generalizing to unseen test-time classes. To address this challenge, we introduce a new practical CL setting tailored for open-world visual representation learning. In this setting, subsequent data streams systematically introduce novel classes that are disjoint from those seen in previous training phases, while also remaining distinct from the unseen test classes. In response, we present Dynamic Prompt and Representation Learner (DPaRL), a simple yet effective Prompt-based CL (PCL) method. Our DPaRL learns to generate dynamic prompts for inference, as opposed to relying on a static prompt pool in previous PCL methods. In addition, DPaRL jointly learns dynamic prompt generation and discriminative representation at each training stage whereas prior PCL methods only refine the prompt learning throughout the process. Our experimental results demonstrate the superiority of our approach, surpassing state-of-the-art methods on well-established open-world image retrieval benchmarks by an average of 4.7% improvement in Recall@1 performance."
Learning Video Context as Interleaved Multimodal Sequences
Authors: Kevin Qinghong Lin, Pengchuan Zhang, Difei Gao, Xide Xia, Joya Chen, Ziteng Gao, Jinheng Xie, Xuhong Xiao, Mike Zheng Shou*
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "Narrative videos, such as movies, pose significant challenges in video understanding due to their rich contexts (characters, dialogues, storylines) and diverse demands (identify who [?], relationship [?], and reason [?]). In this paper, we introduce , a multimodal language model developed to address the wide range of challenges in understanding video contexts. Our core idea is to represent videos as interleaved multimodal sequences (including images, plots, videos, and subtitles), either by linking external knowledge databases or using offline models (such as whisper for subtitles). Through instruction-tuning, this approach empowers the language model to interact with videos using interleaved multimodal instructions. For example, instead of solely relying on video as input, we jointly provide character photos alongside their names and dialogues, allowing the model to associate these elements and generate more comprehensive responses. To demonstrate its effectiveness, we validate ’s performance on six datasets (LVU, MAD, Movienet, CMD, TVC, MovieQA) across five settings (video classifcation, audio description, video-text retrieval, video captioning, and video question-answering). The code will be public at https://github.com/showlab/MovieSeq."
Learning Unsigned Distance Functions from Multi-view Images with Volume Rendering Priors
Authors: Wenyuan Zhang, Kanle Shi, Yu-Shen Liu*, Zhizhong Han
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "Unsigned distance functions (UDFs) have been a vital representation for open surfaces. With different differentiable renderers, current methods are able to train neural networks to infer a UDF by minimizing the rendering errors on the UDF to the multi-view ground truth. However, these differentiable renderers are mainly handcrafted, which makes them either biased on ray-surface intersections, or sensitive to unsigned distance outliers, or not scalable to large scale scenes. To resolve these issues, we present a novel differentiable renderer to infer UDFs more accurately. Instead of using handcrafted equations, our differentiable renderer is a neural network which is pre-trained in a data-driven manner. It learns how to render unsigned distances into depth images, leading to a prior knowledge, dubbed volume rendering priors. To infer a UDF for an unseen scene from multiple RGB images, we generalize the learned volume rendering priors to map inferred unsigned distances in alpha blending for RGB image rendering. Our results show that the learned volume rendering priors are unbiased, robust, scalable, 3D aware, and more importantly, easy to learn. We evaluate our method on both widely used benchmarks and real scenes, and report superior performance over the state-of-the-art methods. Project page: https://wen-yuan-zhang.github.io/VolumeRenderingPriors/."
Dense Multimodal Alignment for Open-Vocabulary 3D Scene Understanding
Authors: Ruihuang Li*, Zhengqiang ZHANG, Chenhang He, Zhiyuan Ma, Vishal Patel, Lei Zhang
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "Recent vision-language pre-training models have exhibited remarkable generalization ability in zero-shot recognition tasks. Previous open-vocabulary 3D scene understanding methods mostly focus on training 3D models using either image or text supervision while neglecting the collective strength of all modalities. In this work, we propose a Dense Multimodal Alignment (DMA) framework to densely co-embed different modalities into a common space for maximizing their synergistic benefits. Instead of extracting coarse view- or region-level text prompts, we leverage large vision-language models to extract complete category information and scalable scene descriptions to build the text modality, and take image modality as the bridge to build dense point-pixel-text associations. Besides, in order to enhance the generalization ability of the 2D model for downstream 3D tasks without compromising the open-vocabulary capability, we employ a dual-path integration approach to combine frozen CLIP visual features and learnable mask features. Extensive experiments show that our DMA method produces highly competitive open-vocabulary segmentation performance on various indoor and outdoor tasks."
Deep Feature Surgery: Towards Accurate and Efficient Multi-Exit Networks
Authors: Cheng Gong, Yao Chen*, Qiuyang Luo, Ye Lu, Tao Li, Yuzhi Zhang, Yufei Sun*, Le Zhang
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "Multi-exit network is a promising architecture for efficient model inference by sharing backbone networks and weights among multiple exits. However, the gradient conflict of the shared weights results in sub-optimal accuracy. This paper introduces Deep Feature Surgery (), which consists of feature partitioning and feature referencing approaches to resolve gradient conflict issues during the training of multi-exit networks. The feature partitioning separates shared features along the depth axis among all exits to alleviate gradient conflict while simultaneously promoting joint optimization for each exit. Subsequently, feature referencing enhances multi-scale features for distinct exits across varying depths to improve the model accuracy. Furthermore, reduces the training operations with the reduced complexity of backpropagation. Experimental results on Cifar100 and ImageNet datasets exhibit that provides up to a 50.00% reduction in training time and attains up to a 6.94% enhancement in accuracy when contrasted with baseline methods across diverse models and tasks. Budgeted batch classification evaluation on MSDNet demonstrates that DFS uses about 2× fewer average FLOPs per image to achieve the same classification accuracy as baseline methods on Cifar100. The code is available at https: //github.com/GongCheng1919/dfs."
Multi-scale Cross Distillation for Object Detection in Aerial Images
Authors: Kun Wang, Zi Wang, Zhang Li*, Xichao Teng, Yang Li
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "Object detection in aerial images is a longstanding yet challenging task. Despite the significant advancements in recent years, most works still show unsatisfactory performance due to the scale variation of objects. A standard strategy to address this problem is multi-scale training, aiming to learn scale-invariant feature representations. Albeit achieving inspiring improvements, such a multi-scale strategy is impractical for real application as inference time increases considerably. Besides, the original images are resized to different scales and subsequently trained separately, lacking information interaction across different scales. This paper presents a novel method called multi-scale cross distillation (MSCD) to address the issues mentioned above. MSCD combines the merits of multi-scale training and knowledge distillation, enabling single-scale inference to achieve comparable or superior performance than multi-scale inference. Specifically, we first construct a parallel multi-branch architecture, in which each branch shares the same parameters yet takes images with different scales as input. Furthermore, we design an adaptive cross-scale distillation module that adaptively integrates the knowledge of different branches into one. Thus, the detectors trained with MSCD only require single-scale inference. Extensive experiments demonstrate the effectiveness of MSCD. Without bells and whistles, MSCD can facilitate prevalent two-stage detectors to outperform corresponding single-scale models by ∼5 and ∼7 mAP improvement on DOTA and DIOR-R datasets, respectively."
Progressive Proxy Anchor Propagation for Unsupervised Semantic Segmentation
Authors: Hyun Seok Seong, WonJun Moon, SuBeen Lee, Jae-Pil Heo*
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "The labor-intensive labeling for semantic segmentation has spurred the emergence of Unsupervised Semantic Segmentation. Recent studies utilize patch-wise contrastive learning based on features from image-level self-supervised pretrained models. However, relying solely on similarity-based supervision from image-level pretrained models often leads to unreliable guidance due to insufficient patch-level semantic representations. To address this, we propose a Progressive Proxy Anchor Propagation (PPAP) strategy. This method gradually identifies more trustworthy positives for each anchor by relocating its proxy to regions densely populated with semantically similar samples. Specifically, we initially establish a tight boundary to gather a few reliable positive samples around each anchor. Then, considering the distribution of positive samples, we relocate the proxy anchor towards areas with a higher concentration of positives and adjust the positiveness boundary based on the propagation degree of the proxy anchor. Moreover, to account for ambiguous regions where positive and negative samples may coexist near the positiveness boundary, we introduce an instance-wise ambiguous zone. Samples within these zones are excluded from the negative set, further enhancing the reliability of the negative set. Our state-of-the-art performances on various datasets validate the effectiveness of the proposed method for Unsupervised Semantic Segmentation. Our code is available at https://github.com/hynnsk/PPAP."
Within the Dynamic Context: Inertia-aware 3D Human Modeling with Pose Sequence
Authors: Yutong Chen, Yifan Zhan, Zhihang Zhong*, Wei Wang, Xiao Sun*, Yu Qiao, Yinqiang Zheng
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "Neural rendering techniques have significantly advanced 3D human body modeling. However, previous approaches overlook dynamics induced by factors such as motion inertia, leading to challenges in scenarios where the pose remains static while the appearance changes, such as abrupt stops after spinning. This limitation arises from conditioning on a single pose, which leads to ambiguity in mapping one pose to multiple appearances. In this study, we elucidate that variations in human appearance depend not only on the current frame’s pose condition but also on past pose states. We introduce Dyco, a novel method that utilizes the delta pose sequence to effectively model temporal appearance variations. To mitigate overfitting to the delta pose sequence, we further propose a localized dynamic context encoder to reduce unnecessary inter-body part dependencies. To validate the effectiveness of our approach, we collect a novel dataset named I3D-Human, focused on capturing temporal changes in clothing appearance under similar poses. Dyco significantly outperforms baselines on I3D-Human and achieves comparable results on ZJU-MoCap. Furthermore, our inertia-aware 3D human method can unprecedentedly simulate appearance changes caused by inertia at different velocities. The code, data and model are available at our project website at https://ai4sports.opengvlab.com/Dyco."
Revisit Human-Scene Interaction via Space Occupancy
Authors: Xinpeng Liu, Haowen Hou, Yanchao Yang, Yong-Lu Li*, Cewu Lu
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "Human-scene Interaction (HSI) generation is a challenging task and crucial for various downstream tasks. However, one of the major obstacles is its limited data scale. High-quality data with simultaneously captured human and 3D environments is hard to acquire, resulting in limited data diversity and complexity. In this work, we argue that interaction with a scene is essentially interacting with the space occupancy of the scene from an abstract physical perspective, leading us to a unified novel view of Human-Occupancy Interaction. By treating pure motion sequences as records of humans interacting with invisible scene occupancy, we can aggregate motion-only data into a large-scale paired human-occupancy interaction database: Motion Occupancy Base (MOB). Thus, the need for costly paired motion-scene datasets with high-quality scene scans can be substantially alleviated. With this new unified view of Human-Occupancy interaction, a single motion controller is proposed to reach the target state given the surrounding occupancy. Once trained on MOB with complex occupancy layout, which is stringent to human movements, the controller could handle cramped scenes and generalize well to general scenes with limited complexity like regular living rooms. With no GT 3D scenes for training, our method can generate realistic and stable HSI motions in diverse scenarios, including both static and dynamic scenes. The project is available at https://foruck.github.io/occu-page/."
Face Adapter for Pre-Trained Diffusion Models with Fine-Grained ID and Attribute Control
Authors: Yue Han*, Junwei Zhu, Keke He, Xu Chen, Yanhao Ge, Wei Li, Xiangtai Li, Jiangning Zhang, Chengjie Wang, Yong Liu
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "Current face reenactment and swapping methods mainly rely on GAN frameworks, but recent focus has shifted to pre-trained diffusion models for their superior generation capabilities. However, training these models is resource-intensive, and the results have not yet achieved satisfactory performance levels. To address this issue, we introduce Face-Adapter, an efficient and effective adapter designed for high-precision and high-fidelity face editing for pre-trained diffusion models. We observe that both face reenactment/swapping tasks essentially involve combinations of target structure, ID and attribute. We aim to sufficiently decouple the control of these factors to achieve both tasks in one model. Specifically, our method contains: 1) A Spatial Condition Generator that provides precise landmarks and background; 2) A Plug-and-play Identity Encoder that transfers face embeddings to the text space by a transformer decoder. 3) An Attribute Controller that integrates spatial conditions and detailed attributes. Face-Adapter achieves comparable or even superior performance in terms of motion control precision, ID retention capability, and generation quality compared to fully fine-tuned face reenactment/swapping models. Additionally, Face-Adapter seamlessly integrates with various StableDiffusion models."
WeConvene: Learned Image Compression with Wavelet-Domain Convolution and Entropy Model
Authors: Haisheng Fu*, Jie Liang, Zhenman Fang, Jingning Han, Feng Liang, Guohe Zhang
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "Recently learned image compression (LIC) has achieved great progress and even outperformed the traditional approach using DCT or discrete wavelet transform (DWT). However, LIC mainly reduces spatial redundancy in the autoencoder networks and entropy coding, but has not fully removed the frequency-domain correlation explicitly as in DCT or DWT. To leverage the best of both worlds, we propose a surprisingly simple but efficient framework, which introduces the DWT to both the convolution layers and entropy coding of CNN-based LIC. First, in both the core and hyperprior autoencoder networks, we propose a Wavelet-domain Convolution (WeConv) module, which performs convolution after DWT, and then converts the data back to spatial domain via inverse DWT. This module is used at selected layers in a CNN network to reduce the frequency-domain correlation explicitly and make the signal sparser in DWT domain. We also propose a Wavelet-domain Channel-wise Auto-Regressive entropy Model (WeChARM), where the output latent representations from the encoder network are first transformed by the DWT, before applying quantization and entropy coding, as in the traditional paradigm. Moreover, the entropy coding is split into two steps. We first code all low-frequency DWT coefficients, and then use them as prior to code high-frequency coefficients. The channel-wise entropy coding is further used in each step. By combining WeConv and WeChARM, the proposed WeConvene scheme achieves superior R-D performance compared to other state-of-the-art LIC methods as well as the latest H.266/VVC. For the Kodak dataset and the baseline network with −0.4% BD-Rate saving over H.266/VVC, introducing WeConv with the simplest Haar transform improves the saving to −4.7%. This is quite impressive given the simplicity of the Haar transform. Enabling Haar-based WeChARM entropy coding further boosts the saving to −8.2%. When the Haar transform is replaced by the 5/3 or 9/7 wavelet, the overall saving becomes −9.4% and −9.8% respectively. The standalone WeConv layer can also be used in many other computer vision tasks beyond image/video compression. The source code is available at https: //github.com/fengyurenpingsheng/WeConvene."
Grid-Attention: Enhancing Computational Efficiency of Large Vision Models without Fine-Tuning
Authors: Pengyu Li*, biao wang, Tianchu Guo, Xian-Sheng Hua
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "Recently, transformer-based large vision models, , the Segment Anything Model (SAM) and Stable Diffusion (SD), have achieved remarkable success in the computer vision field. However, the quartic complexity within the transformer’s Multi-Head Attention (MHA) leads to substantial computational costs in these models whose inputs and outputs are high-resolution. Although several prior works attempted to alleviate this challenge, none have successfully reduced the complexity and latency of large vision models while preserving their remarkable capabilities without requiring enormous efforts and GPU hours to re-train or fine-tune the models. To address the challenge, we propose a simple yet effective plug-and-play transformer block called Grid-Attention(GridAttn). The GridAttn integrates the proposed Grid Clustering module, Grid Distributing strategies, and Grid Recovering module with common MHA to enhance the large vision models’ computational efficiency and preserve their performance without the need for re-training or fine-tuning their parameters. We conduct extensive experiments on recent high-resolution tasks, including zero-shot instance segmentation (SAM, Expedit-SAM), text-to-image generation (Stable Diffusion V2.1), and semantic segmentation (SegFormer B0-B5). The experiments demonstrate that: Without any training or fine-tuning, GridAttn reduces GFlops by the range of [4.6%, 16.1%] and GPU inference latency by [8.2%, 21.4%], all while achieving equivalent performance (the performance bias ratio is less than 1%). Furthermore, the experiments present that GridAttn can also be trained from scratch or fine-tuned with very slight fine-tuning costs, resulting in a significantly improved performance-efficiency tradeoff. As a recommendation, we encourage the community to incorporate our GridAttn whenever deploying a well-trained transformer directly, fine-tuning a pre-trained one, or training a new one from scratch. The source code will be released in https://github.com/pengyuLPY/GridAttn."
Mitigating Background Shift in Class-Incremental Semantic Segmentation
Authors: Gilhan Park, WonJun Moon, SuBeen Lee, Tae-Young Kim, Jae-Pil Heo*
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "Class-Incremental Semantic Segmentation (CISS) aims to learn new classes without forgetting the old ones, using only the labels of the new classes. To achieve this, two popular strategies are employed: 1) pseudo-labeling and knowledge distillation to preserve prior knowledge; and 2) background weight transfer, which leverages the broad coverage of background in learning new classes by transferring background weight to the new class classifier. However, the first strategy heavily relies on the old model in detecting old classes while undetected pixels are regarded as the background, thereby leading to the background shift towards the old classes (i.e., misclassification of old class as background). Additionally, in the case of the second approach, initializing the new class classifier with background knowledge triggers a similar background shift issue, but towards the new classes. To address these issues, we propose a background-class separation framework for CISS. To begin with, selective pseudo-labeling and adaptive feature distillation are to distill only trustworthy past knowledge. On the other hand, we encourage the separation between the background and new classes with a novel orthogonal objective along with label-guided output distillation. Our state-of-the-art results validate the effectiveness of these proposed methods. Our code is available at: https://github.com/RoadoneP/ECCV2024_MBS."
Relation DETR: Exploring Explicit Position Relation Prior for Object Detection
Authors: Xiuquan Hou, Meiqin Liu*, Senlin Zhang, Ping Wei, Badong Chen, Xuguang Lan
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "This paper presents a general scheme for enhancing the convergence and performance of DETR (DEtection TRansformer). We investigate the slow convergence problem in transformers from a new perspective, suggesting that it arises from the self-attention that introduces no structural bias over inputs. To address this issue, we explore incorporating position relation prior as attention bias to augment object detection, following the verification of its statistical significance using a proposed quantitative macroscopic correlation (MC) metric. Our approach, termed Relation-DETR, introduces an encoder to construct position relation embeddings for progressive attention refinement, which further extends the traditional streaming pipeline of DETR into a contrastive relation pipeline to address the conflicts between non-duplicate predictions and positive supervision. Extensive experiments on both generic and task-specific datasets demonstrate the effectiveness of our approach. Under the same configurations, Relation-DETR achieves a significant improvement (+2.0% AP compared to DINO), state-of-the-art performance (51.7% AP for 1× and 52.1% AP for 2× settings), and a remarkably faster convergence speed (over 40% AP with only 2 training epochs) than existing DETR detectors on COCO val2017. Moreover, the proposed relation encoder serves as a universal plug-in-and-play component, bringing clear improvements for theoretically any DETR-like methods. Furthermore, we introduce a class-agnostic detection dataset, SA-Det-100k. The experimental results on the dataset illustrate that the proposed explicit position relation achieves a clear improvement of 1.3% AP, highlighting its potential towards universal object detection. The code and dataset are available at https://github.com/xiuqhou/Relation-DETR."
BKDSNN: Enhancing the Performance of Learning-based Spiking Neural Networks Training with Blurred Knowledge Distillation
Authors: Zekai Xu, Kang You, Qinghai Guo, Xiang Wang, Zhezhi He*
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "Spiking neural networks (SNNs), which mimic biological neural systems to convey information via discrete spikes, are well-known as brain-inspired models with excellent computing efficiency. By utilizing the surrogate gradient estimation for discrete spikes, learning-based SNN training methods that can achieve ultra-low inference latency (, number of time-step) have emerged recently. Nevertheless, due to the difficulty of deriving precise gradient for discrete spikes in learning-based methods, a distinct accuracy gap persists between SNNs and their artificial neural networks (ANNs) counterparts. To address the aforementioned issue, we propose a blurred knowledge distillation (BKD) technique, which leverages randomly blurred SNN features to restore and imitate the ANN features. Note that, our BKD is applied upon the feature map right before the last layer of SNNs, which can also mix with prior logits-based knowledge distillation for maximal accuracy boost. In the category of learning-based methods, our work achieves state-of-the-art performance for training SNNs on both static and neuromorphic datasets. On the ImageNet dataset, BKDSNN outperforms prior best results by 4.51% and 0.93% with the network topology of CNN and Transformer, respectively."
Agent Attention: On the Integration of Softmax and Linear Attention
Authors: Dongchen Han, Tianzhu Ye, Yizeng Han, Zhuofan Xia, Siyuan Pan, Pengfei Wan, Shiji Song, Gao Huang*
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "The attention module is the key component in Transformers. While the global attention mechanism offers high expressiveness, its excessive computational cost restricts its applicability in various scenarios. In this paper, we propose a novel attention paradigm, Agent Attention, to strike a favorable balance between computational efficiency and representation power. Specifically, the Agent Attention, denoted as a quadruple (Q, A, K, V ), introduces an additional set of agent tokens A into the conventional attention module. The agent tokens first act as the agent for the query tokens Q to aggregate information from K and V , and then broadcast the information back to Q. Given the number of agent tokens can be designed to be much smaller than the number of query tokens, agent attention is significantly more efficient than the widely adopted Softmax attention, while preserving global context modelling capability. Interestingly, we show that the proposed agent attention is equivalent to a generalized form of linear attention. Therefore, agent attention seamlessly integrates the powerful Softmax attention and the highly efficient linear attention. Extensive experiments demonstrate the effectiveness of agent attention with various vision Transformers and across diverse vision tasks, including image classification, object detection, semantic segmentation and image generation. Notably, agent attention has shown remarkable performance in high-resolution scenarios, owning to its linear attention nature. For instance, when applied to Stable Diffusion, our agent attention accelerates generation and substantially enhances image generation quality without any additional training. Code is available at https://github.com/LeapLabTHU/Agent-Attention"
Learning by Aligning 2D Skeleton Sequences and Multi-Modality Fusion
Authors: Quoc-Huy Tran*, Muhammad Ahmed, Murad Popattia, Muhammad Hassan Ahmed, Andrey Konin, Zeeshan Zia
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "This paper presents a self-supervised temporal video alignment framework which is useful for several fine-grained human activity understanding applications. In contrast with the state-of-the-art method of CASA, where sequences of 3D skeleton coordinates are taken directly as input, our key idea is to use sequences of 2D skeleton heatmaps as input. Unlike CASA which performs self-attention in the temporal domain only, we feed 2D skeleton heatmaps to a video transformer which performs self-attention both in the spatial and temporal domains for extracting effective spatiotemporal and contextual features. In addition, we introduce simple heatmap augmentation techniques based on 2D skeletons for self-supervised learning. Despite the lack of 3D information, our approach achieves not only higher accuracy but also better robustness against missing and noisy keypoints than CASA. Furthermore, extensive evaluations on three public datasets, i.e., Penn Action, IKEA ASM, and H2O, demonstrate that our approach outperforms previous methods in different fine-grained human activity understanding tasks. Finally, fusing 2D skeleton heatmaps with RGB videos yields the state-of-the-art on all metrics and datasets. To our best knowledge, our work is the first to utilize 2D skeleton heatmap inputs and the first to explore multi-modality fusion for temporal video alignment."
Resolving Scale Ambiguity in Multi-view 3D Reconstruction using Dual-Pixel Sensors
Authors: Kohei Ashida*, Hiroaki Santo, Fumio Okura, Yasuyuki Matsushita
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "Multi-view 3D reconstruction, namely structure-from-motion and multi-view stereo, is an essential component in 3D computer vision. In general, multi-view 3D reconstruction suffers from unknown scale ambiguity unless a reference object of known size is recorded together with the scene, or the camera poses are pre-calibrated. In this paper, we show that multi-view images recorded by a dual-pixel (DP) sensor allow us to automatically resolve the scale ambiguity without requiring a reference object or pre-calibration. Specifically, the observed defocus blurs in DP images provide sufficient information for determining the scale when paired together with the depth maps (up to scale) recovered from the multi-view 3D reconstruction. Based on this observation, we develop a simple yet effective linear solution method to determine the absolute scale in multi-view 3D reconstruction. Experiments demonstrate the effectiveness of the proposed method with diverse scenes recorded with different cameras/lenses. Code and data are available at https://github.com/kohei-ashida/dp-sfm."
Object-Oriented Anchoring and Modal Alignment in Multimodal Learning
Authors: Shibin Mei, Bingbing Ni*, Hang Wang, Chenglong Zhao, fengfa hu, Zhiming Pi, BiLian Ke
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "Modality alignment has been of paramount importance in recent developments of multimodal learning, which has inspired many innovations in multimodal networks and pre-training tasks. Single-stream networks can effectively leverage self-attention mechanisms to facilitate modality interactions but suffer from high computational complexity and limited applicability to downstream retrieval tasks. In contrast, dual-stream networks address these issues but ignore the significance of modality alignment. In this paper, we develop a multimodal learning method that integrates the advantages of modality alignment from single-stream networks into the dual-stream network by introducing object-oriented anchors to bridge alignment between image and text modalities. Object-oriented anchors are generated effectively and circumvent the need for object detection boxes as previous region-based approaches, while also preserving explicit semantics for modality interactions. Additionally, we design fine-grained token-level asymmetry alignment between modalities and multiview mining to promote modality alignment. To the best of our knowledge, we are the first to apply object-oriented tokens in multimodal pre-training, yielding significant benefits. Extensive experimental results validate the effectiveness of our method, demonstrating that the proposed method outperforms most previous arts in various downstream tasks, particularly when considering comparable data and model scales."
Towards Stable 3D Object Detection
Authors: Jiabao Wang, Qiang Meng, Guochao Liu, Liujiang Yan, Ke Wang, Ming-Ming Cheng, Qibin Hou*
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "In autonomous driving, the temporal stability of 3D object detection greatly impacts the driving safety. However, the detection stability cannot be accessed by existing metrics such as mAP and MOTA, and consequently is less explored by the community. To bridge this gap, this work proposes (), a new metric that can comprehensively evaluate the stability of 3D detectors in terms of confidence, box localization, extent, and heading. By benchmarking state-of-the-art object detectors on the Waymo Open Dataset, reveals interesting properties of object stability that have not been previously discovered by other metrics. To help models improve their stability, we further introduce a general and effective training strategy, called Prediction Consistency Learning (PCL). PCL essentially encourages the prediction consistency of the same objects under different timestamps and augmentations, leading to enhanced detection stability. Furthermore, we examine the effectiveness of PCL with the widely-used CenterPoint, and achieve a remarkable of 86.00 for vehicle class, surpassing the baseline by 5.48. We hope our work could serve as a reliable baseline and draw the community’s attention to this crucial issue in 3D object detection."
FYI: Flip Your Images for Dataset Distillation
Authors: Byunggwan Son*, Youngmin Oh, Donghyeon Baek, Bumsub Ham*
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "Dataset distillation synthesizes a small set of images from a large-scale real dataset such that synthetic and real images share similar behavioral properties (e.g, distributions of gradients or features) during a training process. Through extensive analyses on current methods and real datasets, together with empirical observations, we provide in this paper two important things to share for dataset distillation. First, object parts that appear on one side of a real image are highly likely to appear on the opposite side of another image within a dataset, which we call the bilateral equivalence. Second, the bilateral equivalence enforces synthetic images to duplicate discriminative parts of objects on both the left and right sides of the images, limiting the recognition of subtle differences between objects. To address this problem, we introduce a surprisingly simple yet effective technique for dataset distillation, dubbed FYI, that enables distilling rich semantics of real images into synthetic ones. To this end, FYI embeds a horizontal flipping technique into distillation processes, mitigating the influence of the bilateral equivalence, while capturing more details of objects. Experiments on CIFAR-10/100, Tiny-ImageNet, and ImageNet demonstrate that FYI can be seamlessly integrated into several state-of-the-art methods, without modifying training objectives and network architectures, and it improves the performance remarkably."
On-the-fly Category Discovery for LiDAR Semantic Segmentation
Authors: Hyeonseong Kim, Sung-Hoon Yoon, Minseok Kim, Kuk-Jin Yoon*
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "LiDAR semantic segmentation is important for understanding the surrounding environment in autonomous driving. Existing methods assume closed-set situations with the same training and testing label space. However, in the real world, unknown classes not encountered during training may appear during testing, making it difficult to apply existing methodologies. In this paper, we propose a novel on-the-fly category discovery method for LiDAR semantic segmentation, aiming to classify and segment both unknown and known classes instantaneously during test time, achieved solely by learning with known classes in training. To embed instant segmentation capability in an inductive setting, we adopt a hash coding-based model with an expandable prediction space as a baseline. Based on this, dual prototypical learning is proposed to enhance the recognition of the known classes by reducing the sensitivity to intra-class variance. Additionally, we propose a novel mixing-based category learning framework based on representation mixing to improve the discovery capability of unknown classes. The proposed mixing-based framework effectively models out-of-distribution representations and learns to semantically group them during training while distinguishing them from in-distribution representations. Extensive experiments on SemanticKITTI and SemanticPOSS datasets demonstrate the superiority of the proposed method compared to the baselines. The code is available at https://github.com/ hskim617/OCDSS."
Dual-Camera Smooth Zoom on Mobile Phones
Authors: Renlong Wu, Zhilu Zhang*, Yu Yang, Wangmeng Zuo
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "When zooming between dual cameras on a mobile, noticeable jumps in geometric content and image color occur in the preview, inevitably affecting the user’s zoom experience. In this work, we introduce a new task, , dual-camera smooth zoom (DCSZ) to achieve a smooth zoom preview. The frame interpolation (FI) technique is a potential solution but struggles with ground-truth collection. To address the issue, we suggest a data factory solution where continuous virtual cameras are assembled to generate DCSZ data by rendering reconstructed 3D models of the scene. In particular, we propose a novel dual-camera smooth zoom Gaussian Splatting (ZoomGS), where a camera-specific encoding is introduced to construct a specific 3D model for each virtual camera. With the proposed data factory, we construct a synthetic dataset for DCSZ, and we utilize it to fine-tune FI models. In addition, we collect real-world dual-zoom images without ground-truth for evaluation. Extensive experiments are conducted with multiple FI methods. The results show that the fine-tuned FI models achieve a significant performance improvement over the original ones on DCSZ task. The datasets, codes, and pre-trained models are available at https: //github.com/ZcsrenlongZ/ZoomGS."
ProtoComp: Diverse Point Cloud Completion with Controllable Prototype
Authors: Xumin Yu, Yanbo Wang, Jie Zhou, Jiwen Lu*
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

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
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

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*
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

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."