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Chunlong Xia, Xinliang Wang, Feng Lv, Xin Hao, Yifeng Shi

Although Vision Transformer (ViT) has achieved significant success in computer vision it does not perform well in dense prediction tasks due to the lack of inner-patch information interaction and the limited diversity of feature scale. Most existing studies are devoted to designing vision-specific transformers to solve the above problems which introduce additional pre-training costs. Therefore we present a plain pre-training-free and feature-enhanced ViT backbone with Convolutional Multi-scale feature interaction named ViT-CoMer which facilitates bidirectional interaction between CNN and transformer. Compared to the state-of-the-art ViT-CoMer has the following advantages: (1) We inject spatial pyramid multi-receptive field convolutional features into the ViT architecture which effectively alleviates the problems of limited local information interaction and single-feature representation in ViT. (2) We propose a simple and efficient CNN-Transformer bidirectional fusion interaction module that performs multi-scale fusion across hierarchical features which is beneficial for handling dense prediction tasks. (3) We evaluate the performance of ViT-CoMer across various dense prediction tasks different frameworks and multiple advanced pre-training. Notably our ViT-CoMer-L achieves 64.3% AP on COCO val2017 without extra training data and 62.1% mIoU on ADE20K val both of which are comparable to state-of-the-art methods. We hope ViT-CoMer can serve as a new backbone for dense prediction tasks to facilitate future research. The code will be released at https://github.com/Traffic-X/ViT-CoMer.

Wei Yu, Jie Huang, Bing Li, Kaiwen Zheng, Qi Zhu, Man Zhou, Feng Zhao

Image enhancement algorithms have made remarkable advancements in recent years but directly applying them to Ultra-high-definition (UHD) images presents intractable computational overheads. Therefore previous straightforward solutions employ resampling techniques to reduce the resolution by adopting a "Downsampling-Enhancement-Upsampling" processing paradigm. However this paradigm disentangles the resampling operators and inner enhancement algorithms which results in the loss of information that is favored by the model further leading to sub-optimal outcomes. In this paper we propose a novel method of Learning Model-Aware Resampling (LMAR) which learns to customize resampling by extracting model-aware information from the UHD input image under the guidance of model knowledge. Specifically our method consists of two core designs namely compensatory kernel estimation and steganographic resampling. At the first stage we dynamically predict compensatory kernels tailored to the specific input and resampling scales. At the second stage the image-wise compensatory information is derived with the compensatory kernels and embedded into the rescaled input images. This promotes the representation of the newly derived downscaled inputs to be more consistent with the full-resolution UHD inputs as perceived by the model. Our LMAR enables model-aware and model-favored resampling while maintaining compatibility with existing resampling operators. Extensive experiments on multiple UHD image enhancement datasets and different backbones have shown consistent performance gains after correlating resizer and enhancer e.g. up to 1.2dB PSNR gain for x1.8 resampling scale on UHD-LOL4K. The code is available at \href https://github.com/YPatrickW/LMAR https://github.com/YPatrickW/LMAR .

Jianping Jiang, Xinyu Zhou, Bingxuan Wang, Xiaoming Deng, Chao Xu, Boxin Shi

Reliable hand mesh reconstruction (HMR) from commonly-used color and depth sensors is challenging especially under scenarios with varied illuminations and fast motions. Event camera is a highly promising alternative for its high dynamic range and dense temporal resolution properties but it lacks key texture appearance for hand mesh reconstruction. In this paper we propose EvRGBHand -- the first approach for 3D hand mesh reconstruction with an event camera and an RGB camera compensating for each other. By fusing two modalities of data across time space and information dimensionsEvRGBHand can tackle overexposure and motion blur issues in RGB-based HMR and foreground scarcity and background overflow issues in event-based HMR. We further propose EvRGBDegrader which allows our model to generalize effectively in challenging scenes even when trained solely on standard scenes thus reducing data acquisition costs. Experiments on real-world data demonstrate that EvRGBHand can effectively solve the challenging issues when using either type of camera alone via retaining the merits of both and shows the potential of generalization to outdoor scenes and another type of event camera. Our code models and dataset will be made public after acceptance.

Rui Li, Tobias Fischer, Mattia Segu, Marc Pollefeys, Luc Van Gool, Federico Tombari

Recovering the 3D scene geometry from a single view is a fundamental yet ill-posed problem in computer vision. While classical depth estimation methods infer only a 2.5D scene representation limited to the image plane recent approaches based on radiance fields reconstruct a full 3D representation. However these methods still struggle with occluded regions since inferring geometry without visual observation requires (i) semantic knowledge of the surroundings and (ii) reasoning about spatial context. We propose KYN a novel method for single-view scene reconstruction that reasons about semantic and spatial context to predict each point's density. We introduce a vision-language modulation module to enrich point features with fine-grained semantic information. We aggregate point representations across the scene through a language-guided spatial attention mechanism to yield per-point density predictions aware of the 3D semantic context. We show that KYN improves 3D shape recovery compared to predicting density for each 3D point in isolation. We achieve state-of-the-art results in scene and object reconstruction on KITTI-360 and show improved zero-shot generalization compared to prior work. Project page: https://ruili3.github.io/kyn

Wenfeng Song, Xingliang Jin, Shuai Li, Chenglizhao Chen, Aimin Hao, Xia Hou, Ning Li, Hong Qin

Computer animation's quest to bridge content and style has historically been a challenging venture with previous efforts often leaning toward one at the expense of the other. This paper tackles the inherent challenge of content-style duality ensuring a harmonious fusion where the core narrative of the content is both preserved and elevated through stylistic enhancements. We propose a novel Multi-condition Motion Latent Diffusion Model (MCM-LDM) for Arbitrary Motion Style Transfer (AMST). Our MCM-LDM significantly emphasizes preserving trajectories recognizing their fundamental role in defining the essence and fluidity of motion content. Our MCM-LDM's cornerstone lies in its ability first to disentangle and then intricately weave together motion's tripartite components: motion trajectory motion content and motion style. The critical insight of MCM-LDM is to embed multiple conditions with distinct priorities. The content channel serves as the primary flow guiding the overall structure and movement while the trajectory and style channels act as auxiliary components and synchronize with the primary one dynamically. This mechanism ensures that multi-conditions can seamlessly integrate into the main flow enhancing the overall animation without overshadowing the core content. Empirical evaluations underscore the model's proficiency in achieving fluid and authentic motion style transfers setting a new benchmark in the realm of computer animation. The source code and model are available at https://github.com/XingliangJin/MCM-LDM.git.

Ziming Hong, Li Shen, Tongliang Liu

Recently non-transferable learning (NTL) was proposed to restrict models' generalization toward the target domain(s) which serves as state-of-the-art solutions for intellectual property (IP) protection. However the robustness of the established "transferability barrier" for degrading the target domain performance has not been well studied. In this paper we first show that the generalization performance of NTL models is widely impaired on third-party domains (i.e. the unseen domain in the NTL training stage). We explore the impairment patterns and find that: due to the dominant generalization of non-transferable task NTL models tend to make target-domain-consistent predictions on third-party domains even though only a slight distribution shift from the third-party domain to the source domain. Motivated by these findings we uncover the potential risks of NTL by proposing a simple but effective method (dubbed as TransNTL) to recover the target domain performance with few source domain data. Specifically by performing a group of different perturbations on the few source domain data we obtain diverse third-party domains that evoke the same impairment patterns as the unavailable target domain. Then we fine-tune the NTL model under an impairment-repair self-distillation framework where the source-domain predictions are used to teach the model itself how to predict on third-party domains thus repairing the impaired generalization. Empirically experiments on standard NTL benchmarks show that the proposed TransNTL reaches up to 72% target-domain improvements by using only 10% source domain data. Finally we also explore a feasible defense method and empirically demonstrate its effectiveness.

Jiamian Wang, Guohao Sun, Pichao Wang, Dongfang Liu, Sohail Dianat, Majid Rabbani, Raghuveer Rao, Zhiqiang Tao

The increasing prevalence of video clips has sparked growing interest in text-video retrieval. Recent advances focus on establishing a joint embedding space for text and video relying on consistent embedding representations to compute similarity. However the text content in existing datasets is generally short and concise making it hard to fully describe the redundant semantics of a video. Correspondingly a single text embedding may be less expressive to capture the video embedding and empower the retrieval. In this study we propose a new stochastic text modeling method T-MASS i.e. text is modeled as a stochastic embedding to enrich text embedding with a flexible and resilient semantic range yielding a text mass. To be specific we introduce a similarity-aware radius module to adapt the scale of the text mass upon the given text-video pairs. Plus we design and develop a support text regularization to further control the text mass during the training. The inference pipeline is also tailored to fully exploit the text mass for accurate retrieval. Empirical evidence suggests that T-MASS not only effectively attracts relevant text-video pairs while distancing irrelevant ones but also enables the determination of precise text embeddings for relevant pairs. Our experimental results show a substantial improvement of T-MASS over baseline (3% 6.3% by R@1). Also T-MASS achieves state-of-the-art performance on five benchmark datasets including MSRVTT LSMDC DiDeMo VATEX and Charades.

Yan Huang, Zhang Zhang, Qiang Wu, Yi Zhong, Liang Wang

In various domains such as surveillance and smart retail pedestrian retrieval centering on person re-identification (Re-ID) plays a pivotal role. Existing Re-ID methodologies often overlook subtle internal attribute variations which are crucial for accurately identifying individuals with changing appearances. In response our paper introduces the Attribute-Guided Pedestrian Retrieval (AGPR) task focusing on integrating specified attributes with query images to refine retrieval results. Although there has been progress in attribute-driven image retrieval there remains a notable gap in effectively blending robust Re-ID models with intra-class attribute variations. To bridge this gap we present the Attribute-Guided Transformer-based Pedestrian Retrieval (ATPR) framework. ATPR adeptly merges global ID recognition with local attribute learning ensuring a cohesive linkage between the two. Furthermore to effectively handle the complexity of attribute interconnectivity ATPR organizes attributes into distinct groups and applies both inter-group correlation and intra-group decorrelation regularizations. Our extensive experiments on a newly established benchmark using the RAP dataset demonstrate the effectiveness of ATPR within the AGPR paradigm.

Fengyu Yang, Chao Feng, Ziyang Chen, Hyoungseob Park, Daniel Wang, Yiming Dou, Ziyao Zeng, Xien Chen, Rit Gangopadhyay, Andrew Owens 等

The ability to associate touch with other modalities has huge implications for humans and computational systems. However multimodal learning with touch remains challenging due to the expensive data collection process and non-standardized sensor outputs. We introduce UniTouch a unified tactile model for vision-based touch sensors connected to multiple modalities including vision language and sound. We achieve this by aligning our UniTouch embeddings to pretrained image embeddings already associated with a variety of other modalities. We further propose learnable sensor-specific tokens allowing the model to learn from a set of heterogeneous tactile sensors all at the same time. UniTouch is capable of conducting various touch sensing tasks in the zero-shot setting from robot grasping prediction to touch image question answering. To the best of our knowledge UniTouch is the first to demonstrate such capabilities.

Yuqi Yang, Peng-Tao Jiang, Qibin Hou, Hao Zhang, Jinwei Chen, Bo Li

Previous multi-task dense prediction methods based on the Mixture of Experts (MoE) have received great performance but they neglect the importance of explicitly modeling the global relations among all tasks. In this paper we present a novel decoder-focused method for multi-task dense prediction called Mixture-of-Low-Rank-Experts (MLoRE). To model the global task relationships MLoRE adds a generic convolution path to the original MoE structure where each task feature can go through this path for explicit parameter sharing. Furthermore to control the parameters and computational cost brought by the increase in the number of experts we take inspiration from LoRA and propose to leverage the low-rank format of a vanilla convolution in the expert network. Since the low-rank experts have fewer parameters and can be dynamically parameterized into the generic convolution the parameters and computational cost do not change much with the increase of experts. Benefiting from this design we increase the number of experts and its reception field to enlarge the representation capacity facilitating multiple dense tasks learning in a unified network. Extensive experiments on the PASCAL-Context and NYUD-v2 benchmarks show that our MLoRE achieves superior performance compared to previous state-of-the-art methods on all metrics. Our code is available at https://github.com/YuqiYang213/MLoRE.

Tianchen Deng, Guole Shen, Tong Qin, Jianyu Wang, Wentao Zhao, Jingchuan Wang, Danwei Wang, Weidong Chen

Neural implicit scene representations have recently shown encouraging results in dense visual SLAM. However existing methods produce low-quality scene reconstruction and low-accuracy localization performance when scaling up to large indoor scenes and long sequences. These limitations are mainly due to their single global radiance field with finite capacity which does not adapt to large scenarios. Their end-to-end pose networks are also not robust enough with the growth of cumulative errors in large scenes. To this end we introduce PLGSLAM a neural visual SLAM system capable of high-fidelity surface reconstruction and robust camera tracking in real-time. To handle large-scale indoor scenes PLGSLAM proposes a progressive scene representation method which dynamically allocates new local scene representation trained with frames within a local sliding window. This allows us to scale up to larger indoor scenes and improves robustness (even under pose drifts). In local scene representation PLGSLAM utilizes tri-planes for local high-frequency features with multi-layer perceptron (MLP) networks for the low-frequency feature achieving smoothness and scene completion in unobserved areas. Moreover we propose local-to-global bundle adjustment method with a global keyframe database to address the increased pose drifts on long sequences. Experimental results demonstrate that PLGSLAM achieves state-of-the-art scene reconstruction results and tracking performance across various datasets and scenarios (both in small and large-scale indoor environments).

George Cazenavette, Avneesh Sud, Thomas Leung, Ben Usman

Due to the high potential for abuse of GenAI systems the task of detecting synthetic images has recently become of great interest to the research community. Unfortunately existing image space detectors quickly become obsolete as new high-fidelity text-to-image models are developed at blinding speed. In this work we propose a new synthetic image detector that uses features obtained by inverting an open-source pre-trained Stable Diffusion model. We show that these inversion features enable our detector to generalize well to unseen generators of high visual fidelity (e.g. DALL*E 3) even when the detector is trained only on lower fidelity fake images generated via Stable Diffusion. This detector achieves new state-of-the-art across multiple training and evaluation setups. Moreover we introduce a new challenging evaluation protocol that uses reverse image search to mitigate stylistic and thematic biases in the detector evaluation. We show that the resulting evaluation scores align well with detectors' in-the-wild performance and release these datasets as public benchmarks for future research.

Lei Lai, Eshed Ohn-Bar, Sanjay Arora, John Seon Keun Yi

We present a highly scalable self-training framework for incrementally adapting vision-based end-to-end autonomous driving policies in a semi-supervised manner i.e. over a continual stream of incoming video data. To facilitate large-scale model training (e.g. open web or unlabeled data) we do not assume access to ground-truth labels and instead estimate pseudo-label policy targets for each video. Our framework comprises three key components: knowledge distillation a sample purification module and an exploration and knowledge retention mechanism. First given sequential image frames we pseudo-label the data and estimate uncertainty using an ensemble of inverse dynamics models. The uncertainty is used to select the most informative samples to add to an experience replay buffer. We specifically select high-uncertainty pseudo-labels to facilitate the exploration and learning of new and diverse driving skills. However in contrast to prior work in continual learning that assumes ground-truth labeled samples the uncertain pseudo-labels can introduce significant noise. Thus we also pair the exploration with a label refinement module which makes use of consistency constraints to re-label the noisy exploratory samples and effectively learn from diverse data. Trained as a complete never-ending learning system we demonstrate state-of-the-art performance on training from domain-changing data as well as millions of images from the open web.

Takahiro Shirakawa, Seiichi Uchida

Layout-aware text-to-image generation is a task to generate multi-object images that reflect layout conditions in addition to text conditions. The current layout-aware text-to-image diffusion models still have several issues including mismatches between the text and layout conditions and quality degradation of generated images. This paper proposes a novel layout-aware text-to-image diffusion model called NoiseCollage to tackle these issues. During the denoising process NoiseCollage independently estimates noises for individual objects and then crops and merges them into a single noise. This operation helps avoid condition mismatches; in other words it can put the right objects in the right places. Qualitative and quantitative evaluations show that NoiseCollage outperforms several state-of-the-art models. These successful results indicate that the crop-and-merge operation of noises is a reasonable strategy to control image generation. We also show that NoiseCollage can be integrated with ControlNet to use edges sketches and pose skeletons as additional conditions. Experimental results show that this integration boosts the layout accuracy of ControlNet. The code is available at https://github.com/univ-esuty/noisecollage.

Zhixing Zhang, Bichen Wu, Xiaoyan Wang, Yaqiao Luo, Luxin Zhang, Yinan Zhao, Peter Vajda, Dimitris Metaxas, Licheng Yu

Recent advances in diffusion models have successfully enabled text-guided image inpainting. While it seems straightforward to extend such editing capability into the video domain there have been fewer works regarding text-guided video inpainting. Given a video a masked region at its initial frame and an editing prompt it requires a model to do infilling at each frame following the editing guidance while keeping the out-of-mask region intact. There are three main challenges in text-guided video inpainting: (i) temporal consistency of the edited video (ii) supporting different inpainting types at different structural fidelity levels and (iii) dealing with variable video length. To address these challenges we introduce Any-Length Video Inpainting with Diffusion Model dubbed as AVID. At its core our model is equipped with effective motion modules and adjustable structure guidance for fixed-length video inpainting. Building on top of that we propose a novel Temporal MultiDiffusion sampling pipeline with a middle-frame attention guidance mechanism facilitating the generation of videos with any desired duration. Our comprehensive experiments show our model can robustly deal with various inpainting types at different video duration ranges with high quality.

Chanyoung Kim, Woojung Han, Dayun Ju, Seong Jae Hwang

Semantic segmentation has innately relied on extensive pixel-level annotated data leading to the emergence of unsupervised methodologies. Among them leveraging self-supervised Vision Transformers for unsupervised semantic segmentation (USS) has been making steady progress with expressive deep features. Yet for semantically segmenting images with complex objects a predominant challenge remains: the lack of explicit object-level semantic encoding in patch-level features. This technical limitation often leads to inadequate segmentation of complex objects with diverse structures. To address this gap we present a novel approach EAGLE which emphasizes object-centric representation learning for unsupervised semantic segmentation. Specifically we introduce EiCue a spectral technique providing semantic and structural cues through an eigenbasis derived from the semantic similarity matrix of deep image features and color affinity from an image. Further by incorporating our object-centric contrastive loss with EiCue we guide our model to learn object-level representations with intra- and inter-image object-feature consistency thereby enhancing semantic accuracy. Extensive experiments on COCO-Stuff Cityscapes and Potsdam-3 datasets demonstrate the state-of-the-art USS results of EAGLE with accurate and consistent semantic segmentation across complex scenes.

Shih-Po Lee, Zijia Lu, Zekun Zhang, Minh Hoai, Ehsan Elhamifar

We present a new egocentric procedural error dataset containing videos with various types of errors as well as normal videos and propose a new framework for procedural error detection using error-free training videos only. Our framework consists of an action segmentation model and a contrastive step prototype learning module to segment actions and learn useful features for error detection. Based on the observation that interactions between hands and objects often inform action and error understanding we propose to combine holistic frame features with relations features which we learn by building a graph using active object detection followed by a Graph Convolutional Network. To handle errors unseen during training we use our contrastive step prototype learning to learn multiple prototypes for each step capturing variations of error-free step executions. At inference time we use feature-prototype similarities for error detection. By experiments on three datasets we show that our proposed framework outperforms state-of-the-art video anomaly detection methods for error detection and provides smooth action and error predictions.

Haoyu Chen, Hao Tang, Ehsan Adeli, Guoying Zhao

3D pose transfer that aims to transfer the desired pose to a target mesh is one of the most challenging 3D generation tasks. Previous attempts rely on well-defined parametric human models or skeletal joints as driving pose sources. However to obtain those clean pose sources cumbersome but necessary pre-processing pipelines are inevitable hindering implementations of the real-time applications. This work is driven by the intuition that the robustness of the model can be enhanced by introducing adversarial samples into the training leading to a more invulnerable model to the noisy inputs which even can be further extended to directly handling the real-world data like raw point clouds/scans without intermediate processing. Furthermore we propose a novel 3D pose Masked Autoencoder (3D-PoseMAE) a customized MAE that effectively learns 3D extrinsic presentations (i.e. pose). 3D-PoseMAE facilitates learning from the aspect of extrinsic attributes by simultaneously generating adversarial samples that perturb the model and learning the arbitrary raw noisy poses via a multi-scale masking strategy. Both qualitative and quantitative studies show that the transferred meshes given by our network result in much better quality. Besides we demonstrate the strong generalizability of our method on various poses different domains and even raw scans. Experimental results also show meaningful insights that the intermediate adversarial samples generated in the training can successfully attack the existing pose transfer models.

Yan-Shuo Liang, Wu-Jun Li

Continual learning requires the model to learn multiple tasks sequentially. In continual learning the model should possess the ability to maintain its performance on old tasks (stability) and the ability to adapt to new tasks continuously (plasticity). Recently parameter-efficient fine-tuning (PEFT) which involves freezing a pre-trained model and injecting a small number of learnable parameters to adapt to downstream tasks has gained increasing popularity in continual learning. Although existing continual learning methods based on PEFT have demonstrated superior performance compared to those not based on PEFT most of them do not consider how to eliminate the interference of the new task on the old tasks which inhibits the model from making a good trade-off between stability and plasticity. In this work we propose a new PEFT method called interference-free low-rank adaptation (InfLoRA) for continual learning. InfLoRA injects a small number of parameters to reparameterize the pre-trained weights and shows that fine-tuning these injected parameters is equivalent to fine-tuning the pre-trained weights within a subspace. Furthermore InfLoRA designs this subspace to eliminate the interference of the new task on the old tasks making a good trade-off between stability and plasticity. Experimental results show that InfLoRA outperforms existing state-of-the-art continual learning methods on multiple datasets.

Shuliang Ning, Duomin Wang, Yipeng Qin, Zirong Jin, Baoyuan Wang, Xiaoguang Han

In this paper we propose a novel virtual try-on from unconstrained designs (ucVTON) task to enable photorealistic synthesis of personalized composite clothing on input human images. Unlike prior arts constrained by specific input types our method allows flexible specification of style (text or image) and texture (full garment cropped sections or texture patches) conditions. To address the entanglement challenge when using full garment images as conditions we develop a two-stage pipeline with explicit disentanglement of style and texture. In the first stage we generate a human parsing map reflecting the desired style conditioned on the input. In the second stage we composite textures onto the parsing map areas based on the texture input. To represent complex and non-stationary textures that have never been achieved in previous fashion editing works we first propose extracting hierarchical and balanced CLIP features and applying position encoding in VTON. Experiments demonstrate superior synthesis quality and personalization enabled by our method. The flexible control over style and texture mixing brings virtual try-on to a new level of user experience for online shopping and fashion design.