mtybilly/Instruct2DS huggingface dataset:
Benchmarking the Robustness of Temporal Action Detection Models Against Temporal Corruptions
Authors: Xiaoyong Chen, Jiaming Liang, Huisi Wu, Guangzhong Cao, Yong Guo Runhao Zeng
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Temporal action detection (TAD) aims to locate action positions and recognize action categories in long-term untrimmed videos. Although many methods have achieved promising results their robustness has not been thoroughly studied. In practice we observe that temporal information in videos can be occasionally corrupted such as missing or blurred frames. Interestingly existing methods often incur a significant performance drop even if only one frame is affected. To formally evaluate the robustness we establish two temporal corruption robustness benchmarks namely THUMOS14-C and ActivityNet-v1.3-C. In this paper we extensively analyze the robustness of seven leading TAD methods and obtain some interesting findings: 1) Existing methods are particularly vulnerable to temporal corruptions and end-to-end methods are often more susceptible than those with a pre-trained feature extractor; 2) Vulnerability mainly comes from localization error rather than classification error; 3) When corruptions occur in the middle of an action instance TAD models tend to yield the largest performance drop. Besides building a benchmark we further develop a simple but effective robust training method to defend against temporal corruptions through the FrameDrop augmentation and Temporal-Robust Consistency loss. Remarkably our approach not only improves robustness but also yields promising improvements on clean data. We believe that this study will serve as a benchmark for future research in robust video analysis. Source code and models are available at https://github.com/Alvin-Zeng/temporal-robustness-benchmark.
Open-World Human-Object Interaction Detection via Multi-modal Prompts
Authors: Bingliang Li, Ailing Zeng, Lei Zhang, Ruimao Zhang Jie Yang
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: In this paper we develop MP-HOI a powerful Multi-modal Prompt-based HOI detector designed to leverage both textual descriptions for open-set generalization and visual exemplars for handling high ambiguity in descriptions realizing HOI detection in the open world. Specifically it integrates visual prompts into existing language-guided-only HOI detectors to handle situations where textual descriptions face difficulties in generalization and to address complex scenarios with high interaction ambiguity. To facilitate MP-HOI training we build a large-scale HOI dataset named Magic-HOI which gathers six existing datasets into a unified label space forming over 186K images with 2.4K objects 1.2K actions and 20K HOI interactions. Furthermore to tackle the long-tail issue within the Magic-HOI dataset we introduce an automated pipeline for generating realistically annotated HOI images and present SynHOI a high-quality synthetic HOI dataset containing 100K images. Leveraging these two datasets MP-HOI optimizes the HOI task as a similarity learning process between multi-modal prompts and objects/interactions via a unified contrastive loss to learn generalizable and transferable objects/interactions representations from large-scale data. MP-HOI could serve as a generalist HOI detector surpassing the HOI vocabulary of existing expert models by more than 30 times. Concurrently our results demonstrate that MP-HOI exhibits remarkable zero-shot capability in real-world scenarios and consistently achieves a new state-of-the-art performance across various benchmarks. Our project homepage is available at https://MP-HOI.github.io/.
HMD-Poser: On-Device Real-time Human Motion Tracking from Scalable Sparse Observations
Authors: Yang Zhang, Tao Liu, Zhen Fan, Tianyuan Du, Zhuo Su, Xiaozheng Zheng, Zeming Li Peng Dai
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: It is especially challenging to achieve real-time human motion tracking on a standalone VR Head-Mounted Display (HMD) such as Meta Quest and PICO. In this paper we propose HMD-Poser the first unified approach to recover full-body motions using scalable sparse observations from HMD and body-worn IMUs. In particular it can support a variety of input scenarios such as HMD HMD+2IMUs HMD+3IMUs etc. The scalability of inputs may accommodate users' choices for both high tracking accuracy and easy-to-wear. A lightweight temporal-spatial feature learning network is proposed in HMD-Poser to guarantee that the model runs in real-time on HMDs. Furthermore HMD-Poser presents online body shape estimation to improve the position accuracy of body joints. Extensive experimental results on the challenging AMASS dataset show that HMD-Poser achieves new state-of-the-art results in both accuracy and real-time performance. We also build a new free-dancing motion dataset to evaluate HMD-Poser's on-device performance and investigate the performance gap between synthetic data and real-captured sensor data. Finally we demonstrate our HMD-Poser with a real-time Avatar-driving application on a commercial HMD. Our code and free-dancing motion dataset are available \href https://pico-ai-team.github.io/hmd-poser  here .
UniMODE: Unified Monocular 3D Object Detection
Authors: Xiaogang Xu, SerNam Lim, Hengshuang Zhao Zhuoling Li
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Realizing unified monocular 3D object detection including both indoor and outdoor scenes holds great importance in applications like robot navigation. However involving various scenarios of data to train models poses challenges due to their significantly different characteristics e.g. diverse geometry properties and heterogeneous domain distributions. To address these challenges we build a detector based on the bird's-eye-view (BEV) detection paradigm where the explicit feature projection is beneficial to addressing the geometry learning ambiguity when employing multiple scenarios of data to train detectors. Then we split the classical BEV detection architecture into two stages and propose an uneven BEV grid design to handle the convergence instability caused by the aforementioned challenges. Moreover we develop a sparse BEV feature projection strategy to reduce computational cost and a unified domain alignment method to handle heterogeneous domains. Combining these techniques a unified detector UniMODE is derived which surpasses the previous state-of-the-art on the challenging Omni3D dataset (a large-scale dataset including both indoor and outdoor scenes) by 4.9% \rm AP_ 3D  revealing the first successful generalization of a BEV detector to unified 3D object detection.
Sherpa3D: Boosting High-Fidelity Text-to-3D Generation via Coarse 3D Prior
Authors: Diankun Wu, Yi Wei, Yongming Rao, Yueqi Duan Fangfu Liu
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Recently 3D content creation from text prompts has demonstrated remarkable progress by utilizing 2D and 3D diffusion models. While 3D diffusion models ensure great multi-view consistency their ability to generate high-quality and diverse 3D assets is hindered by the limited 3D data. In contrast 2D diffusion models find a distillation approach that achieves excellent generalization and rich details without any 3D data. However 2D lifting methods suffer from inherent view-agnostic ambiguity thereby leading to serious multi-face Janus issues where text prompts fail to provide sufficient guidance to learn coherent 3D results. Instead of retraining a costly viewpoint-aware model we study how to fully exploit easily accessible coarse 3D knowledge to enhance the prompts and guide 2D lifting optimization for refinement. In this paper we propose Sherpa3D a new text-to-3D framework that achieves high-fidelity generalizability and geometric consistency simultaneously. Specifically we design a pair of guiding strategies derived from the coarse 3D prior generated by the 3D diffusion model: a structural guidance for geometric fidelity and a semantic guidance for 3D coherence. Employing the two types of guidance the 2D diffusion model enriches the 3D content with diversified and high-quality results. Extensive experiments show the superiority of our Sherpa3D over the state-of-the-art text-to-3D methods in terms of quality and 3D consistency.
Flexible Biometrics Recognition: Bridging the Multimodality Gap through Attention Alignment and Prompt Tuning
Authors: Dick Sigmund, Chen-Hui Chan, Andrew Beng Jin Teoh Leslie Ching Ow Tiong
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Periocular and face are complementary biometrics for identity management albeit with inherent limitations notably in scenarios involving occlusion due to sunglasses or masks. In response to these challenges we introduce Flexible Biometric Recognition (FBR) a novel framework designed to advance conventional face periocular and multimodal face-periocular biometrics across both intra- and cross-modality recognition tasks. FBR strategically utilizes the Multimodal Fusion Attention (MFA) and Multimodal Prompt Tuning (MPT) mechanisms within the Vision Transformer architecture. MFA facilitates the fusion of modalities ensuring cohesive alignment between facial and periocular embeddings while incorporating soft-biometrics to enhance the model's ability to discriminate between individuals. The fusion of three modalities is pivotal in exploring interrelationships between different modalities. Additionally MPT serves as a unifying bridge intertwining inputs and promoting cross-modality interactions while preserving their distinctive characteristics. The collaborative synergy of MFA and MPT enhances the shared features of the face and periocular with a specific emphasis on the ocular region yielding exceptional performance in both intra- and cross-modality recognition tasks. Rigorous experimentation across four benchmark datasets validates the noteworthy performance of the FBR model. The source code is available at https://github.com/MIS-DevWorks/FBR.
Multi-agent Collaborative Perception via Motion-aware Robust Communication Network
Authors: Yu Liu, Zhi Li, Shaohui Li, You He Shixin Hong
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Collaborative perception allows for information sharing between multiple agents such as vehicles and infrastructure to obtain a comprehensive view of the environment through communication and fusion. Current research on multi-agent collaborative perception systems often assumes ideal communication and perception environments and neglects the effect of real-world noise such as pose noise motion blur and perception noise. To address this gap in this paper we propose a novel motion-aware robust communication network (MRCNet) that mitigates noise interference and achieves accurate and robust collaborative perception. MRCNet consists of two main components: multi-scale robust fusion (MRF) addresses pose noise by developing cross-semantic multi-scale enhanced aggregation to fuse features of different scales while motion enhanced mechanism (MEM) captures motion context to compensate for information blurring caused by moving objects. Experimental results on popular collaborative 3D object detection datasets demonstrate that MRCNet outperforms competing methods in noisy scenarios with improved perception performance using less bandwidth.
The Manga Whisperer: Automatically Generating Transcriptions for Comics
Authors: Andrew Zisserman Ragav Sachdeva
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: In the past few decades Japanese comics commonly referred to as Manga have transcended both cultural and linguistic boundaries to become a true worldwide sensation. Yet the inherent reliance on visual cues and illustration within manga renders it largely inaccessible to individuals with visual impairments. In this work we seek to address this substantial barrier with the aim of ensuring that manga can be appreciated and actively engaged by everyone. Specifically we tackle the problem of diarisation i.e. generating a transcription of who said what and when in a fully automatic way. To this end we make the following contributions: (1) we present a unified model Magi that is able to (a) detect panels text boxes and character boxes (b) cluster characters by identity (without knowing the number of clusters apriori) and (c) associate dialogues to their speakers; (2) we propose a novel approach that is able to sort the detected text boxes in their reading order and generate a dialogue transcript; (3) we annotate an evaluation benchmark for this task using publicly available [English] manga pages.
Exploring Region-Word Alignment in Built-in Detector for Open-Vocabulary Object Detection
Authors: Qiuyu Zhao, Linyu Zheng, Hao Zeng, Zhiwei Ge, Tianhao Li, Sulong Xu Heng Zhang
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Open-vocabulary object detection aims to detect novel categories that are independent from the base categories used during training. Most modern methods adhere to the paradigm of learning vision-language space from a large-scale multi-modal corpus and subsequently transferring the acquired knowledge to off-the-shelf detectors like Faster-RCNN. However information attenuation or destruction may occur during the process of knowledge transfer due to the domain gap hampering the generalization ability on novel categories. To mitigate this predicament in this paper we present a novel framework named BIND standing for Bulit-IN Detector to eliminate the need for module replacement or knowledge transfer to off-the-shelf detectors. Specifically we design a two-stage training framework with an Encoder-Decoder structure. In the first stage an image-text dual encoder is trained to learn region-word alignment from a corpus of image-text pairs. In the second stage a DETR-style decoder is trained to perform detection on annotated object detection datasets. In contrast to conventional manually designed non-adaptive anchors which generate numerous redundant proposals we develop an anchor proposal network that generates anchor proposals with high likelihood based on candidates adaptively thereby substantially improving detection efficiency. Experimental results on two public benchmarks COCO and LVIS demonstrate that our method stands as a state-of-the-art approach for open-vocabulary object detection.
MovieChat: From Dense Token to Sparse Memory for Long Video Understanding
Authors: Wenhao Chai, Guanhong Wang, Yucheng Zhang, Haoyang Zhou, Feiyang Wu, Haozhe Chi, Xun Guo, Tian Ye, Yanting Zhang, Yan Lu, Jenq-Neng Hwang, Gaoang Wang Enxin Song
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Recently integrating video foundation models and large language models to build a video understanding system can overcome the limitations of specific pre-defined vision tasks. Yet existing systems can only handle videos with very few frames. For long videos the computation complexity memory cost and long-term temporal connection impose additional challenges. Taking advantage of the Atkinson-Shiffrin memory model with tokens in Transformers being employed as the carriers of memory in combination with our specially designed memory mechanism we propose the MovieChat to overcome these challenges. MovieChat achieves state-of-the-art performance in long video understanding along with the released MovieChat-1K benchmark with 1K long video and 14K manual annotations for validation of the effectiveness of our method. The code models and data can be found in https://rese1f.github.io/MovieChat.
Comparing the Decision-Making Mechanisms by Transformers and CNNs via Explanation Methods
Authors: Saeed Khorram, Li Fuxin Mingqi Jiang
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: In order to gain insights about the decision-making of different visual recognition backbones we propose two methodologies sub-explanation counting and cross-testing that systematically applies deep explanation algorithms on a dataset-wide basis and compares the statistics generated from the amount and nature of the explanations. These methodologies reveal the difference among networks in terms of two properties called compositionality and disjunctivism. Transformers and ConvNeXt are found to be more compositional in the sense that they jointly consider multiple parts of the image in building their decisions whereas traditional CNNs and distilled transformers are less compositional and more disjunctive which means that they use multiple diverse but smaller set of parts to achieve a confident prediction. Through further experiments we pinpointed the choice of normalization to be especially important in the compositionality of a model in that batch normalization leads to less compositionality while group and layer normalization lead to more. Finally we also analyze the features shared by different backbones and plot a landscape of different models based on their feature-use similarity.
A Unified Diffusion Framework for Scene-aware Human Motion Estimation from Sparse Signals
Authors: Jingya Wang, Kaiyang Ji, Lan Xu, Jingyi Yu, Ye Shi Jiangnan Tang
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Estimating full-body human motion via sparse tracking signals from head-mounted displays and hand controllers in 3D scenes is crucial to applications in AR/VR. One of the biggest challenges to this task is the one-to-many mapping from sparse observations to dense full-body motions which endowed inherent ambiguities. To help resolve this ambiguous problem we introduce a new framework to combine rich contextual information provided by scenes to benefit full-body motion tracking from sparse observations. To estimate plausible human motions given sparse tracking signals and 3D scenes we develop \text S ^2Fusion a unified framework fusing \underline S cene and sparse \underline S ignals with a conditional dif\underline Fusion  model. \text S ^2Fusion first extracts the spatial-temporal relations residing in the sparse signals via a periodic autoencoder and then produces time-alignment feature embedding as additional inputs. Subsequently by drawing initial noisy motion from a pre-trained prior \text S ^2Fusion utilizes conditional diffusion to fuse scene geometry and sparse tracking signals to generate full-body scene-aware motions. The sampling procedure of \text S ^2Fusion is further guided by a specially designed scene-penetration loss and phase-matching loss which effectively regularizes the motion of the lower body even in the absence of any tracking signals making the generated motion much more plausible and coherent. Extensive experimental results have demonstrated that our \text S ^2Fusion outperforms the state-of-the-art in terms of estimation quality and smoothness.
Single Domain Generalization for Crowd Counting
Authors: S.-H. Gary Chan Zhuoxuan Peng
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Due to its promising results density map regression has been widely employed for image-based crowd counting. The approach however often suffers from severe performance degradation when tested on data from unseen scenarios the so-called "domain shift" problem. To address the problem we investigate in this work single domain generalization (SDG) for crowd counting. The existing SDG approaches are mainly for image classification and segmentation and can hardly be extended to our case due to its regression nature and label ambiguity (i.e. ambiguous pixel-level ground truths). We propose MPCount a novel effective SDG approach even for narrow source distribution. MPCount stores diverse density values for density map regression and reconstructs domain-invariant features by means of only one memory bank a content error mask and attention consistency loss. By partitioning the image into grids it employs patch-wise classification as an auxiliary task to mitigate label ambiguity. Through extensive experiments on different datasets MPCount is shown to significantly improve counting accuracy compared to the state of the art under diverse scenarios unobserved in the training data characterized by narrow source distribution. Code is available at https://github.com/Shimmer93/MPCount.
Atlantis: Enabling Underwater Depth Estimation with Stable Diffusion
Authors: Shaodi You, Yu Li, Ying Fu Fan Zhang
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Monocular depth estimation has experienced significant progress on terrestrial images in recent years thanks to deep learning advancements. But it remains inadequate for underwater scenes primarily due to data scarcity. Given the inherent challenges of light attenuation and backscatter in water acquiring clear underwater images or precise depth is notably difficult and costly. To mitigate this issue learning-based approaches often rely on synthetic data or turn to self- or unsupervised manners. Nonetheless their performance is often hindered by domain gap and looser constraints. In this paper we propose a novel pipeline for generating photorealistic underwater images using accurate terrestrial depth. This approach facilitates the supervised training of models for underwater depth estimation effectively reducing the performance disparity between terrestrial and underwater environments. Contrary to previous synthetic datasets that merely apply style transfer to terrestrial images without scene content change our approach uniquely creates vivid non-existent underwater scenes by leveraging terrestrial depth data through the innovative Stable Diffusion model. Specifically we introduce a specialized Depth2Underwater ControlNet trained on prepared \ Underwater Depth Text\  data triplets for this generation task. Our newly developed dataset Atlantis enables terrestrial depth estimation models to achieve considerable improvements on unseen underwater scenes surpassing their terrestrial pretrained counterparts both quantitatively and qualitatively. Moreover we further show its practical utility by applying the improved depth in underwater image enhancement and its smaller domain gap from the LLVM perspective. Code and dataset are publicly available at https://github.com/zkawfanx/Atlantis.
Matching Anything by Segmenting Anything
Authors: Lei Ke, Martin Danelljan, Luigi Piccinelli, Mattia Segu, Luc Van Gool, Fisher Yu Siyuan Li
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: The robust association of the same objects across video frames in complex scenes is crucial for many applications especially object tracking. Current methods predominantly rely on labeled domain-specific video datasets which limits cross-domain generalization of learned similarity embeddings. We propose MASA a novel method for robust instance association learning capable of matching any objects within videos across diverse domains without tracking labels. Leveraging the rich object segmentation from the Segment Anything Model (SAM) MASA learns instance-level correspondence through exhausive data transformations. We treat the SAM outputs as dense object region proposals and learn to match those regions from a vast image collection. We further design a universal MASA adapter which can work in tandem with foundational segmentation or detection models and enable them to track any detected objects. Those combinations present strong zero-shot tracking ability in complex domains. Extensive tests on multiple challenging MOT and MOTS benchmarks indicate that the proposed method using only unlabelled static images achieves even better performance than state-of-the-art methods trained with fully annotated in-domain video sequences in zero-shot association. Our code is available at https://github.com/siyuanliii/masa.
Task-Aware Encoder Control for Deep Video Compression
Authors: Jixiang Luo, Xinjie Zhang, Tongda Xu, Guo Lu, Dailan He, Jing Geng, Yan Wang, Jun Zhang, Hongwei Qin Xingtong Ge
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Prior research on deep video compression (DVC) for machine tasks typically necessitates training a unique codec for each specific task mandating a dedicated decoder per task. In contrast traditional video codecs employ a flexible encoder controller enabling the adaptation of a single codec to different tasks through mechanisms like mode prediction. Drawing inspiration from this we introduce an innovative encoder controller for deep video compression for machines. This controller features a mode prediction and a Group of Pictures (GoP) selection module. Our approach centralizes control at the encoding stage allowing for adaptable encoder adjustments across different tasks such as detection and tracking while maintaining compatibility with a standard pre-trained DVC decoder. Empirical evidence demonstrates that our method is applicable across multiple tasks with various existing pre-trained DVCs. Moreover extensive experiments demonstrate that our method outperforms previous DVC by about 25% bitrate for different tasks with only one pre-trained decoder.
Multi-scale Dynamic and Hierarchical Relationship Modeling for Facial Action Units Recognition
Authors: Siyang Song, Cheng Luo, Songhe Deng, Weicheng Xie, Linlin Shen Zihan Wang
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Human facial action units (AUs) are mutually related in a hierarchical manner as not only they are associated with each other in both spatial and temporal domains but also AUs located in the same/close facial regions show stronger relationships than those of different facial regions. While none of existing approach thoroughly model such hierarchical inter-dependencies among AUs this paper proposes to comprehensively model multi-scale AU-related dynamic and hierarchical spatio-temporal relationship among AUs for their occurrences recognition. Specifically we first propose a novel multi-scale temporal differencing network with an adaptive weighting block to explicitly capture facial dynamics across frames at different spatial scales which specifically considers the heterogeneity of range and magnitude in different AUs' activation. Then a two-stage strategy is introduced to hierarchically model the relationship among AUs based on their spatial distribution (i.e. local and cross-region AU relationship modelling). Experimental results achieved on BP4D and DISFA show that our approach is the new state-of-the-art in the field of AU occurrence recognition. Our code is publicly available at https://github.com/CVI-SZU/MDHR.
Decoupled Pseudo-labeling for Semi-Supervised Monocular 3D Object Detection
Authors: Jiaming Li, Xiangru Lin, Wei Zhang, Xiao Tan, Junyu Han, Errui Ding, Jingdong Wang, Guanbin Li Jiacheng Zhang
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: We delve into pseudo-labeling for semi-supervised monocular 3D object detection (SSM3OD) and discover two primary issues: a misalignment between the prediction quality of 3D and 2D attributes and the tendency of depth supervision derived from pseudo-labels to be noisy leading to significant optimization conflicts with other reliable forms of supervision. To tackle these issues we introduce a novel decoupled pseudo-labeling (DPL) approach for SSM3OD. Our approach features a Decoupled Pseudo-label Generation (DPG) module designed to efficiently generate pseudo-labels by separately processing 2D and 3D attributes. This module incorporates a unique homography-based method for identifying dependable pseudo-labels in Bird's Eye View (BEV) space specifically for 3D attributes. Additionally we present a Depth Gradient Projection (DGP) module to mitigate optimization conflicts caused by noisy depth supervision of pseudo-labels effectively decoupling the depth gradient and removing conflicting gradients. This dual decoupling strategy--at both the pseudo-label generation and gradient levels--significantly improves the utilization of pseudo-labels in SSM3OD. Our comprehensive experiments on the KITTI benchmark demonstrate the superiority of our method over existing approaches.
Temporally Consistent Unbalanced Optimal Transport for Unsupervised Action Segmentation
Authors: Stephen Gould Ming Xu
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: We propose a novel approach to the action segmentation task for long untrimmed videos based on solving an optimal transport problem. By encoding a temporal consistency prior into a Gromov-Wasserstein problem we are able to decode a temporally consistent segmentation from a noisy affinity/matching cost matrix between video frames and action classes. Unlike previous approaches our method does not require knowing the action order for a video to attain temporal consistency. Furthermore our resulting (fused) Gromov-Wasserstein problem can be efficiently solved on GPUs using a few iterations of projected mirror descent. We demonstrate the effectiveness of our method in an unsupervised learning setting where our method is used to generate pseudo-labels for self-training. We evaluate our segmentation approach and unsupervised learning pipeline on the Breakfast 50-Salads YouTube Instructions and Desktop Assembly datasets yielding state-of-the-art results for the unsupervised video action segmentation task.
Learning Transferable Negative Prompts for Out-of-Distribution Detection
Authors: Guansong Pang, Xiao Bai, Wenjun Miao, Jin Zheng Tianqi Li
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Existing prompt learning methods have shown certain capabilities in Out-of-Distribution (OOD) detection but the lack of OOD images in the target dataset in their training can lead to mismatches between OOD images and In-Distribution (ID) categories resulting in a high false positive rate. To address this issue we introduce a novel OOD detection method named 'NegPrompt' to learn a set of negative prompts each representing a negative connotation of a given class label for delineating the boundaries between ID and OOD images. It learns such negative prompts with ID data only without any reliance on external outlier data. Further current methods assume the availability of samples of all ID classes rendering them ineffective in open-vocabulary learning scenarios where the inference stage can contain novel ID classes not present during training. In contrast our learned negative prompts are transferable to novel class labels. Experiments on various ImageNet benchmarks show that NegPrompt surpasses state-of-the-art prompt-learning-based OOD detection methods and maintains a consistent lead in hard OOD detection in closed- and open-vocabulary classification scenarios. Code is available at https://github.com/mala-lab/negprompt.
Long-Tail Class Incremental Learning via Independent Sub-prototype Construction
Authors: Xu Yang, Jie Yin, Kun Wei, Cheng Deng Xi Wang
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Long-tail class incremental learning (LT-CIL) is designed to perpetually acquire novel knowledge from an imbalanced and perpetually evolving data stream while ensuring the retention of previously acquired knowledge. The existing method only re-balances data distribution and ignores exploring the potential relationship between different samples causing non-robust representations and even severe forgetting in classes with few samples. In this paper we constructed two parallel spaces simultaneously: 1) Sub-prototype space and 2) Reminiscence space to learn robust representations while alleviating forgetfulness. Concretely we advance the concept of the sub-prototype space which amalgamates insights from diverse classes. This integration facilitates the mutual complementarity of varied knowledge thereby augmenting the attainment of more robust representations. Furthermore we introduce the reminiscence space which encapsulates each class distribution aiming to constraint model optimization and mitigate the phenomenon of forgetting. The tandem utilization of the two parallel spaces effectively alleviates the adverse consequences associated with imbalanced data distribution preventing forgetting without needing replay examples. Extensive experiments demonstrate that our method achieves state-of-the-art performance on various benchmarks.
Learning with Unreliability: Fast Few-shot Voxel Radiance Fields with Relative Geometric Consistency
Authors: Bangzhen Liu, Hao Tang, Bailin Deng, Shengfeng He Yingjie Xu
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: We propose a voxel-based optimization framework ReVoRF for few-shot radiance fields that strategically addresses the unreliability in pseudo novel view synthesis. Our method pivots on the insight that relative depth relationships within neighboring regions are more reliable than the absolute color values in disoccluded areas. Consequently we devise a bilateral geometric consistency loss that carefully navigates the trade-off between color fidelity and geometric accuracy in the context of depth consistency for uncertain regions. Moreover we present a reliability-guided learning strategy to discern and utilize the variable quality across synthesized views complemented by a reliability-aware voxel smoothing algorithm that smoothens the transition between reliable and unreliable data patches. Our approach allows for a more nuanced use of all available data promoting enhanced learning from regions previously considered unsuitable for high-quality reconstruction. Extensive experiments across diverse datasets reveal that our approach attains significant gains in efficiency and accuracy delivering rendering speeds of 3 FPS 7 mins to train a 360deg scene and a 5% improvement in PSNR over existing few-shot methods. Code is available at https://github.com/HKCLynn/ReVoRF
Towards Understanding and Improving Adversarial Robustness of Vision Transformers
Authors: Tanima Dutta Samyak Jain
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Recent literature has demonstrated that vision transformers (VITs) exhibit superior performance compared to convolutional neural networks (CNNs). The majority of recent research on adversarial robustness however has predominantly focused on CNNs. In this work we bridge this gap by analyzing the effectiveness of existing attacks on VITs. We demonstrate that due to the softmax computations in every attention block in VITs they are inherently vulnerable to floating point underflow errors. This can lead to a gradient masking effect resulting in suboptimal attack strength of well-known attacks like PGD Carlini and Wagner (CW) GAMA and Patch attacks. Motivated by this we propose Adaptive Attention Scaling (AAS) attack that can automatically find the optimal scaling factors of pre-softmax outputs using gradient-based optimization. We show that the proposed simple strategy can be incorporated with any existing adversarial attacks as well as adversarial training methods and achieved improved performance. On VIT-B16 we demonstrate an improved attack strength of upto 2.2% on CIFAR10 and upto 2.9% on CIFAR100 by incorporating the proposed AAS attack with state-of-the-art single attack methods like GAMA attack. Further we utilise the proposed AAS attack for every few epochs in existing adversarial training methods which is termed as Adaptive Attention Scaling Adversarial Training (AAS-AT). On incorporating AAS-AT with existing methods we outperform them on VITs over 1.3-3.5% on CIFAR10. We observe improved performance on ImageNet-100 as well.
EventEgo3D: 3D Human Motion Capture from Egocentric Event Streams
Authors: Hiroyasu Akada, Jian Wang, Diogo Luvizon, Christian Theobalt, Vladislav Golyanik Christen Millerdurai
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Monocular egocentric 3D human motion capture is a challenging and actively researched problem. Existing methods use synchronously operating visual sensors (e.g. RGB cameras) and often fail under low lighting and fast motions which can be restricting in many applications involving head-mounted devices. In response to the existing limitations this paper 1) introduces a new problem i.e. 3D human motion capture from an egocentric monocular event camera with a fisheye lens and 2) proposes the first approach to it called EventEgo3D (EE3D). Event streams have high temporal resolution and provide reliable cues for 3D human motion capture under high-speed human motions and rapidly changing illumination. The proposed EE3D framework is specifically tailored for learning with event streams in the LNES representation enabling high 3D reconstruction accuracy. We also design a prototype of a mobile head-mounted device with an event camera and record a real dataset with event observations and the ground-truth 3D human poses (in addition to the synthetic dataset). Our EE3D demonstrates robustness and superior 3D accuracy compared to existing solutions across various challenging experiments while supporting real-time 3D pose update rates of 140Hz.
Holistic Features are almost Sufficient for Text-to-Video Retrieval
Authors: Ruixiang Zhao, Zijie Xin, Bangxiang Lan, Xirong Li Kaibin Tian
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: For text-to-video retrieval (T2VR) which aims to retrieve unlabeled videos by ad-hoc textual queries CLIP-based methods currently lead the way. Compared to CLIP4Clip which is efficient and compact state-of-the-art models tend to compute video-text similarity through fine-grained cross-modal feature interaction and matching putting their scalability for large-scale T2VR applications into doubt. We propose TeachCLIP enabling a CLIP4Clip based student network to learn from more advanced yet computationally intensive models. In order to create a learning channel to convey fine-grained cross-modal knowledge from a heavy model to the student we add to CLIP4Clip a simple Attentional frame-Feature Aggregation (AFA) block which by design adds no extra storage / computation overhead at the retrieval stage. Frame-text relevance scores calculated by the teacher network are used as soft labels to supervise the attentive weights produced by AFA. Extensive experiments on multiple public datasets justify the viability of the proposed method. TeachCLIP has the same efficiency and compactness as CLIP4Clip yet has near-SOTA effectiveness.
A Call to Reflect on Evaluation Practices for Age Estimation: Comparative Analysis of the State-of-the-Art and a Unified Benchmark
Authors: Vojt?ch Franc Jakub Paplhám
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Comparing different age estimation methods poses a challenge due to the unreliability of published results stemming from inconsistencies in the benchmarking process. Previous studies have reported continuous performance improvements over the past decade using specialized methods; however our findings challenge these claims. This paper identifies two trivial yet persistent issues with the currently used evaluation protocol and describes how to resolve them. We offer an extensive comparative analysis for state-of-the-art facial age estimation methods. Surprisingly we find that the performance differences between the methods are negligible compared to the effect of other factors such as facial alignment facial coverage image resolution model architecture or the amount of data used for pretraining. We use the gained insights to propose using FaRL as the backbone model and demonstrate its effectiveness on all public datasets. We make the source code and exact data splits public on GitHub and in the supplementary material.
CosalPure: Learning Concept from Group Images for Robust Co-Saliency Detection
Authors: Qing Guo, Felix Juefei-Xu, Yihao Huang, Yang Liu, Geguang Pu Jiayi Zhu
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Co-salient object detection (CoSOD) aims to identify the common and salient (usually in the foreground) regions across a given group of images. Although achieving significant progress state-of-the-art CoSODs could be easily affected by some adversarial perturbations leading to substantial accuracy reduction. The adversarial perturbations can mislead CoSODs but do not change the high-level semantic information (e.g. concept) of the co-salient objects. In this paper we propose a novel robustness enhancement framework by first learning the concept of the co-salient objects based on the input group images and then leveraging this concept to purify adversarial perturbations which are subsequently fed to CoSODs for robustness enhancement. Specifically we propose CosalPure containing two modules i.e. group-image concept learning and concept-guided diffusion purification. For the first module we adopt a pre-trained text-to-image diffusion model to learn the concept of co-salient objects within group images where the learned concept is robust to adversarial examples. For the second module we map the adversarial image to the latent space and then perform diffusion generation by embedding the learned concept into the noise prediction function as an extra condition. Our method can effectively alleviate the influence of the SOTA adversarial attack containing different adversarial patterns including exposure and noise. The extensive results demonstrate that our method could enhance the robustness of CoSODs significantly.
Uncertainty-aware Action Decoupling Transformer for Action Anticipation
Authors: Nakul Agarwal, Shao-Yuan Lo, Kwonjoon Lee, Qiang Ji Hongji Guo
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Human action anticipation aims at predicting what people will do in the future based on past observations. In this paper we introduce Uncertainty-aware Action Decoupling Transformer (UADT) for action anticipation. Unlike existing methods that directly predict action in a verb-noun pair format we decouple the action anticipation task into verb and noun anticipations separately. The objective is to make the two decoupled tasks assist each other and eventually improve the action anticipation task. Specifically we propose a two-stream Transformer-based architecture which is composed of a verb-to-noun model and a noun-to-verb model. The verb-to-noun model leverages the verb information to improve the noun prediction and the other way around. We extend the model in a probabilistic manner and quantify the predictive uncertainty of each decoupled task to select features. In this way the noun prediction leverages the most informative and redundancy-free verb features and verb prediction works similarly. Finally the two streams are combined dynamically based on their uncertainties to make the joint action anticipation. We demonstrate the efficacy of our method by achieving state-of-the-art performance on action anticipation benchmarks including EPIC-KITCHENS EGTEA Gaze+ and 50-Salads.
MRFP: Learning Generalizable Semantic Segmentation from Sim-2-Real with Multi-Resolution Feature Perturbation
Authors: Prajwal Gurunath, Aniruddh Sikdar, Suresh Sundaram Sumanth Udupa
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Deep neural networks have shown exemplary performance on semantic scene understanding tasks on source domains but due to the absence of style diversity during training enhancing performance on unseen target domains using only single source domain data remains a challenging task. Generation of simulated data is a feasible alternative to retrieving large style-diverse real-world datasets as it is a cumbersome and budget-intensive process. However the large domain-specific inconsistencies between simulated and real-world data pose a significant generalization challenge in semantic segmentation. In this work to alleviate this problem we propose a novel Multi-Resolution Feature Perturbation (MRFP) technique to randomize domain-specific fine-grained features and perturb style of coarse features. Our experimental results on various urban-scene segmentation datasets clearly indicate that along with the perturbation of style-information perturbation of fine-feature components is paramount to learn domain invariant robust feature maps for semantic segmentation models. MRFP is a simple and computationally efficient transferable module with no additional learnable parameters or objective functions that helps state-of-the-art deep neural networks to learn robust domain invariant features for simulation-to-real semantic segmentation. Code is available at https://github.com/airl-iisc/MRFP.
S-DyRF: Reference-Based Stylized Radiance Fields for Dynamic Scenes
Authors: Zhiguo Cao, Yizheng Wu, Kewei Wang, Ke Xian, Zhe Wang, Guosheng Lin Xingyi Li
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Current 3D stylization methods often assume static scenes which violates the dynamic nature of our real world. To address this limitation we present S-DyRF a reference-based spatio-temporal stylization method for dynamic neural radiance fields. However stylizing dynamic 3D scenes is inherently challenging due to the limited availability of stylized reference images along the temporal axis. Our key insight lies in introducing additional temporal cues besides the provided reference. To this end we generate temporal pseudo-references from the given stylized reference. These pseudo-references facilitate the propagation of style information from the reference to the entire dynamic 3D scene. For coarse style transfer we enforce novel views and times to mimic the style details present in pseudo-references at the feature level. To preserve high-frequency details we create a collection of stylized temporal pseudo-rays from temporal pseudo-references. These pseudo-rays serve as detailed and explicit stylization guidance for achieving fine style transfer. Experiments on both synthetic and real-world datasets demonstrate that our method yields plausible stylized results of space-time view synthesis on dynamic 3D scenes.