Visual grounding aims at localizing the target object in image which is most related to the given free-form natural language query. As labeling the position of target object is labor-intensive, the weakly supervised methods, where only image-sentence annotations are required during model training have recently received increasing attention. Most of the existing weakly-supervised methods first generate region proposals via pre-trained object detectors and then employ either cross-modal similarity score or reconstruction loss as the criteria to select proposal from them. However, due to the cross-modal heterogeneous gap, these method often suffer from high confidence spurious association and model prone to error propagation. In this paper, we propose Confidence-aware Pseudo-label Learning (CPL) to overcome the above limitations. Specifically, we first adopt both the uni-modal and cross-modal pre-trained models and propose conditional prompt engineering to automatically generate multiple `descriptive, realistic and diverse' pseudo language queries for each region proposal, and then establish reliable cross-modal association for model training based on the uni-modal similarity score (between pseudo and real text queries). Secondly, we propose a confidence-aware pseudo label verification module which reduces the amount of noise encountered in the training process and the risk of error propagation. Experiments on five widely used datasets validate the efficacy of our proposed components and demonstrate state-of-the-art performance.
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Interactive image segmentation enables annotators to efficiently perform pixel-level annotation for segmentation tasks. However, the existing interactive segmentation pipeline suffers from inefficient computations of interactive models because of the following two issues. First, annotators' later click is based on models' feedback of annotators' former click. This serial interaction is unable to utilize model's parallelism capabilities. Second, in each interaction step, the model handles the invariant image along with the sparse variable clicks, resulting in a process that's highly repetitive and redundant. For efficient computations, we propose a method named InterFormer that follows a new pipeline to address these issues. InterFormer extracts and preprocesses the computationally time-consuming part i.e. image processing from the existing process. Specifically, InterFormer employs a large vision transformer (ViT) on high-performance devices to preprocess images in parallel, and then uses a lightweight module called interactive multi-head self attention (I-MSA) for interactive segmentation. Furthermore, the I-MSA module's deployment on low-power devices extends the practical application of interactive segmentation. The I-MSA module utilizes the preprocessed features to efficiently response to the annotator inputs in real-time. The experiments on several datasets demonstrate the effectiveness of InterFormer, which outperforms previous interactive segmentation models in terms of computational efficiency and segmentation quality, achieve real-time high-quality interactive segmentation on CPU-only devices. The code is available at https://github.com/YouHuang67/InterFormer.
In recent years, there has been increasing interest in applying stylization on 3D scenes from a reference style image, in particular onto neural radiance fields (NeRF). While performing stylization directly on NeRF guarantees appearance consistency over arbitrary novel views, it is a challenging problem to guide the transfer of patterns from the style image onto different parts of the NeRF scene. In this work, we propose a stylization framework for NeRF based on local style transfer. In particular, we use a hash-grid encoding to learn the embedding of the appearance and geometry components, and show that the mapping defined by the hash table allows us to control the stylization to a certain extent. Stylization is then achieved by optimizing the appearance branch while keeping the geometry branch fixed. To support local style transfer, we propose a new loss function that utilizes a segmentation network and bipartite matching to establish region correspondences between the style image and the content images obtained from volume rendering. Our experiments show that our method yields plausible stylization results with novel view synthesis while having flexible controllability via manipulating and customizing the region correspondences.
The motivation of the semi-supervised domain adaptation (SSDA) is to train a model by leveraging knowledge acquired from the plentiful labeled source combined with extremely scarce labeled target data to achieve the lowest error on the unlabeled target data at the testing time. However, due to inter-domain and intra-domain discrepancies, the improvement of classification accuracy is limited. To solve these, we propose the Trico-training method that utilizes a multilayer perceptron (MLP) classifier and two graph convolutional network (GCN) classifiers called inter-view GCN and intra-view GCN classifiers. The first co-training strategy exploits a correlation between MLP and inter-view GCN classifiers to minimize the inter-domain discrepancy, in which the inter-view GCN classifier provides its pseudo labels to teach the MLP classifier, which encourages class representation alignment across domains. In contrast, the MLP classifier gives feedback to the inter-view GCN classifier by using a new concept, 'pseudo-edge', for neighbor's feature aggregation. Doing this increases the data structure mining ability of the inter-view GCN classifier; thus, the quality of generated pseudo labels is improved. The second co-training strategy between MLP and intra-view GCN is conducted in a similar way to reduce the intra-domain discrepancy by enhancing the correlation between labeled and unlabeled target data. Due to an imbalance in classification accuracy between inter-view and intra-view GCN classifiers, we propose the third co-training strategy that encourages them to cooperate to address this problem. We verify the effectiveness of the proposed method on three standard SSDA benchmark datasets: Office-31, Office-Home, and DomainNet. The extended experimental results show that our method surpasses the prior state-of-the-art approaches in SSDA.
In recent years, many video tasks have achieved breakthroughs by utilizing the vision transformer and establishing spatial-temporal decoupling for feature extraction. Although multi-view 3D reconstruction also faces multiple images as input, it cannot immediately inherit their success due to completely ambiguous associations between unstructured views. There is not usable prior relationship, which is similar to the temporally-coherence property in a video. To solve this problem, we propose a novel transformer network for Unstructured Multiple Images (UMIFormer). It exploits transformer blocks for decoupled intra-view encoding and designed blocks for token rectification that mine the correlation between similar tokens from different views to achieve decoupled inter-view encoding. Afterward, all tokens acquired from various branches are compressed into a fixed-size compact representation while preserving rich information for reconstruction by leveraging the similarities between tokens. We empirically demonstrate on ShapeNet and confirm that our decoupled learning method is adaptable for unstructured multiple images. Meanwhile, the experiments also verify our model outperforms existing SOTA methods by a large margin. Code will be available at https://github.com/GaryZhu1996/UMIFormer.
We propose a novel unsupervised backlit image enhancement method, abbreviated as CLIP-LIT, by exploring the potential of Contrastive Language-Image Pre-Training (CLIP) for pixel-level image enhancement. We show that the open-world CLIP prior not only aids in distinguishing between backlit and well-lit images, but also in perceiving heterogeneous regions with different luminance, facilitating the optimization of the enhancement network. Unlike high-level and image manipulation tasks, directly applying CLIP to enhancement tasks is non-trivial, owing to the difficulty in finding accurate prompts. To solve this issue, we devise a prompt learning framework that first learns an initial prompt pair by constraining the text-image similarity between the prompt (negative/positive sample) and the corresponding image (backlit image/well-lit image) in the CLIP latent space. Then, we train the enhancement network based on the text-image similarity between the enhanced result and the initial prompt pair. To further improve the accuracy of the initial prompt pair, we iteratively fine-tune the prompt learning framework to reduce the distribution gaps between the backlit images, enhanced results, and well-lit images via rank learning, boosting the enhancement performance. Our method alternates between updating the prompt learning framework and enhancement network until visually pleasing results are achieved. Extensive experiments demonstrate that our method outperforms state-of-the-art methods in terms of visual quality and generalization ability, without requiring any paired data.
This paper explores Deployment Authorization (DPA) as a means of restricting the generalization capabilities of vision models on certain domains to protect intellectual property. Nevertheless, the current advancements in DPA are predominantly confined to fully supervised settings. Such settings require the accessibility of annotated images from any unauthorized domain, rendering the DPA approach impractical for real-world applications due to its exorbitant costs. To address this issue, we propose Source-Only Deployment Authorization (SDPA), which assumes that only authorized domains are accessible during training phases, and the model's performance on unauthorized domains must be suppressed in inference stages. Drawing inspiration from distributional robust statistics, we present a lightweight method called Domain-Specified Optimization (DSO) for SDPA that degrades the model's generalization over a divergence ball. DSO comes with theoretical guarantees on the convergence property and its authorization performance. As a complementary of SDPA, we also propose Target-Combined Deployment Authorization (TPDA), where unauthorized domains are partially accessible, and simplify the DSO method to a perturbation operation on the pseudo predictions, referred to as Target-Dependent Domain-Specified Optimization (TDSO). We demonstrate the effectiveness of our proposed DSO and TDSO methods through extensive experiments on six image benchmarks, achieving dominant performance on both SDPA and TDPA settings.
Weakly supervised object localization (WSOL) aims to localize objects based on only image-level labels as supervision. Recently, transformers have been introduced into WSOL, yielding impressive results. The self-attention mechanism and multilayer perceptron structure in transformers preserve long-range feature dependency, facilitating complete localization of the full object extent. However, current transformer-based methods predict bounding boxes using category-agnostic attention maps, which may lead to confused and noisy object localization. To address this issue, we propose a novel Category-aware Allocation TRansformer (CATR) that learns category-aware representations for specific objects and produces corresponding category-aware attention maps for object localization. First, we introduce a Category-aware Stimulation Module (CSM) to induce learnable category biases for self-attention maps, providing auxiliary supervision to guide the learning of more effective transformer representations. Second, we design an Object Constraint Module (OCM) to refine the object regions for the category-aware attention maps in a self-supervised manner. Extensive experiments on the CUB-200-2011 and ILSVRC datasets demonstrate that the proposed CATR achieves significant and consistent performance improvements over competing approaches.
Growing leakage and misuse of visual information raise security and privacy concerns, which promotes the development of information protection. Existing adversarial perturbations-based methods mainly focus on the de-identification against deep learning models. However, the inherent visual information of the data has not been well protected. In this work, inspired by the Type-I adversarial attack, we propose an Adversarial Visual Information Hiding (AVIH) method to protect the visual privacy of data. Specifically, the method generates obfuscating adversarial perturbations to obscure the visual information of the data. Meanwhile, it maintains the hidden objectives to be correctly predicted by models. In addition, our method does not modify the parameters of the applied model, which makes it flexible for different scenarios. Experimental results on the recognition and classification tasks demonstrate that the proposed method can effectively hide visual information and hardly affect the performances of models. The code is available at https://github.com/suzhigangssz/AVIH.
ActFormer: A GAN-based Transformer towards General Action-Conditioned 3D Human Motion Generation
PDF ↗We present a GAN-based Transformer for general action-conditioned 3D human motion generation, including not only single-person actions but also multi-person interactive actions. Our approach consists of a powerful Action-conditioned motion TransFormer (ActFormer) under a GAN training scheme, equipped with a Gaussian Process latent prior. Such a design combines the strong spatio-temporal representation capacity of Transformer, superiority in generative modeling of GAN, and inherent temporal correlations from the latent prior. Furthermore, ActFormer can be naturally extended to multi-person motions by alternately modeling temporal correlations and human interactions with Transformer encoders. To further facilitate research on multi-person motion generation, we introduce a new synthetic dataset of complex multi-person combat behaviors. Extensive experiments on NTU-13, NTU RGB+D 120, BABEL and the proposed combat dataset show that our method can adapt to various human motion representations and achieve superior performance over the state-of-the-art methods on both single-person and multi-person motion generation tasks, demonstrating a promising step towards a general human motion generator.
Weakly supervised object localization (WSOL) remains challenging when learning object localization models from image category labels. Conventional methods that discriminatively train activation models ignore representative yet less discriminative object parts. In this study, we propose a generative prompt model (GenPromp), defining the first generative pipeline to localize less discriminative object parts by formulating WSOL as a conditional image denoising procedure. During training, GenPromp converts image category labels to learnable prompt embeddings which are fed to a generative model to conditionally recover the input image with noise and learn representative embeddings. During inference, GenPromp combines the representative embeddings with discriminative embeddings (queried from an off-the-shelf vision-language model) for both representative and discriminative capacity. The combined embeddings are finally used to generate multi-scale high-quality attention maps, which facilitate localizing full object extent. Experiments on CUB-200-2011 and ILSVRC show that GenPromp respectively outperforms the best discriminative models by 5.2% and 5.6% (Top-1 Loc), setting a solid baseline for WSOL with the generative model. Code is available at https://github.com/callsys/GenPromp.
Efficient detectors for edge devices are often optimized for parameters or speed count metrics, which remain in weak correlation with the energy of detectors. However, some vision applications of convolutional neural networks, such as always-on surveillance cameras, are critical for energy constraints. This paper aims to serve as a baseline by designing detectors to reach tradeoffs between energy and performance from two perspectives: 1) We extensively analyze various CNNs to identify low-energy architectures, including selecting activation functions, convolutions operators, and feature fusion structures on necks. These underappreciated details in past work seriously affect the energy consumption of detectors; 2) To break through the dilemmatic energy-performance problem, we propose a balanced detector driven by energy using discovered low-energy components named FemtoDet. In addition to the novel construction, we improve FemtoDet by considering convolutions and training strategy optimizations. Specifically, we develop a new instance boundary enhancement (IBE) module for convolution optimization to overcome the contradiction between the limited capacity of CNNs and detection tasks in diverse spatial representations, and propose a recursive warm-restart (RecWR) for optimizing training strategy to escape the sub-optimization of light-weight detectors by considering the data shift produced in popular augmentations. As a result, FemtoDet with only 68.77k parameters achieves a competitive score of 46.3 AP50 on PASCAL VOC and 1.11 W & 64.47 FPS on Qualcomm Snapdragon 865 CPU platforms. Extensive experiments on COCO and TJU-DHD datasets indicate that the proposed method achieves competitive results in diverse scenes.
Class-incremental learning (CIL) has achieved remarkable successes in learning new classes consecutively while overcoming catastrophic forgetting on old categories. However, most existing CIL methods unreasonably assume that all old categories have the same forgetting pace, and neglect negative influence of forgetting heterogeneity among different old classes on forgetting compensation. To surmount the above challenges, we develop a novel Heterogeneous Forgetting Compensation (HFC) model, which can resolve heterogeneous forgetting of easy-to-forget and hard-to-forget old categories from both representation and gradient aspects. Specifically, we design a task-semantic aggregation block to alleviate heterogeneous forgetting from representation aspect. It aggregates local category information within each task to learn task-shared global representations. Moreover, we develop two novel plug-and-play losses: a gradient-balanced forgetting compensation loss and a gradient-balanced relation distillation loss to alleviate forgetting from gradient aspect. They consider gradient-balanced compensation to rectify forgetting heterogeneity of old categories and heterogeneous relation consistency. Experiments on several representative datasets illustrate effectiveness of our HFC model. The code is available at https://github.com/JiahuaDong/HFC.
In digital photography, autofocus is a key feature that aids high-quality image capture, and modern approaches use the phase patterns arising from dual-pixel sensors as important focus cues. However, dual-pixel data is prone to multiple error sources in its image capturing process, including lens shading or distortions due to the inherent optical characteristics of the lens. We observe that, while these degradations are hard to model using prior knowledge, they are correlated with the spatial position of the pixels within the image sensor area, and we propose a learning-based autofocus model with positional encodings (PE) to capture these patterns. Specifically, we introduce RoI-PE, which encodes the spatial position of our focusing region-of-interest (RoI) on the imaging plane. Learning with RoI-PE allows the model to be more robust to spatially-correlated degradations. In addition, we also propose to encode the current focal position of lens as lens-PE, which allows us to significantly reduce the computational complexity of the autofocus model. Experimental results clearly demonstrate the effectiveness of using the proposed position encodings for automatic focusing based on dual-pixel data.
Continual Test-Time Adaptation (CTTA) generalizes conventional Test-Time Adaptation (TTA) by assuming that the target domain is dynamic over time rather than stationary. In this paper, we explore Multi-Modal Continual Test-Time Adaptation (MM-CTTA) as a new extension of CTTA for 3D semantic segmentation. The key to MM-CTTA is to adaptively attend to the reliable modality while avoiding catastrophic forgetting during continual domain shifts, which is out of the capability of previous TTA or CTTA methods. To fulfill this gap, we propose an MM-CTTA method called Continual Cross-Modal Adaptive Clustering (CoMAC) that addresses this task from two perspectives. On one hand, we propose an adaptive dual-stage mechanism to generate reliable cross-modal predictions by attending to the reliable modality based on the class-wise feature-centroid distance in the latent space. On the other hand, to perform test-time adaptation without catastrophic forgetting, we design class-wise momentum queues that capture confident target features for adaptation while stochastically restoring pseudo-source features to revisit source knowledge. We further introduce two new benchmarks to facilitate the exploration of MM-CTTA in the future. Our experimental results show that our method achieves state-of-the-art performance on both benchmarks. Visit our project website at https://sites.google.com/view/mmcotta.
LoLep: Single-View View Synthesis with Locally-Learned Planes and Self-Attention Occlusion Inference
PDF ↗We propose a novel method, LoLep, which regresses Locally-Learned planes from a single RGB image to represent scenes accurately, thus generating better novel views. Without the depth information, regressing appropriate plane locations is a challenging problem. To solve this issue, we pre-partition the disparity space into bins and design a disparity sampler to regress local offsets for multiple planes in each bin. However, only using such a sampler makes the network not convergent; we further propose two optimizing strategies that combine with different disparity distributions of datasets and propose an occlusion-aware reprojection loss as a simple yet effective geometric supervision technique. We also introduce a self-attention mechanism to improve occlusion inference and present a Block-Sampling Self-Attention (BS-SA) module to address the problem of applying self-attention to large feature maps. We demonstrate the effectiveness of our approach and generate state-of-the-art results on different datasets. Compared to MINE, our approach has an LPIPS reduction of 4.8% 9.0% and an RV reduction of 74.9% 83.5%. We also evaluate the performance on real-world images and demonstrate the benefits. We will release the source code at the time of publication.
Privacy-preserving federated learning allows multiple users to jointly train a model with coordination of a central server. The server only learns the final aggregation result, thereby preventing leakage of the users' (private) training data from the individual model updates. However, keeping the individual updates private allows malicious users to degrade the model accuracy without being detected, also known as Byzantine attacks. Best existing defenses against Byzantine workers rely on robust rank-based statistics, e.g., setting robust bounds via the median of updates, to find malicious updates. However, implementing privacy-preserving rank-based statistics, especially median-based, is nontrivial and unscalable in the secure domain, as it requires sorting of all individual updates. We establish the first private robustness check that uses high break point rank-based statistics on aggregated model updates. By exploiting randomized clustering, we significantly improve the scalability of our defense without compromising privacy. We leverage the derived statistical bounds in zero-knowledge proofs to detect and remove malicious updates without revealing the private user updates. Our novel framework, zPROBE, enables Byzantine resilient and secure federated learning. We show the effectiveness of zPROBE on several computer vision benchmarks. Empirical evaluations demonstrate that zPROBE provides a low overhead solution to defend against state-of-the-art Byzantine attacks while preserving privacy.
Token compression aims to speed up large-scale vision transformers (e.g. ViTs) by pruning (dropping) or merging tokens. It is an important but challenging task. Although recent advanced approaches achieved great success, they need to carefully handcraft a compression rate (i.e. number of tokens to remove), which is tedious and leads to sub-optimal performance. To tackle this problem, we propose Differentiable Compression Rate (DiffRate), a novel token compression method that has several appealing properties prior arts do not have. First, DiffRate enables propagating the loss function's gradient onto the compression ratio, which is considered as a non-differentiable hyperparameter in previous work. In this case, different layers can automatically learn different compression rates layer-wisely without extra overhead. Second, token pruning and merging can be naturally performed simultaneously in DiffRate, while they were isolated in previous works. Third, extensive experiments demonstrate that DiffRate achieves state-of-the-art performance. For example, by applying the learned layer-wise compression rates to an off-the-shelf ViT-H (MAE) model, we achieve a 40% FLOPs reduction and a 1.5x throughput improvement, with a minor accuracy drop of 0.16% on ImageNet without fine-tuning, even outperforming previous methods with fine-tuning. Codes and models are available at https://github.com/OpenGVLab/DiffRate.
Previous research has studied the task of segmenting cinematic videos into scenes and into narrative acts. However, these studies have overlooked the essential task of multimodal alignment and fusion for effectively and efficiently processing long-form videos (>60min). In this paper, we introduce Multimodal alignmEnt aGgregation and distillAtion (MEGA) for cinematic long-video segmentation. MEGA tackles the challenge by leveraging multiple media modalities. The method coarsely aligns inputs of variable lengths and different modalities with alignment positional encoding. To maintain temporal synchronization while reducing computation, we further introduce an enhanced bottleneck fusion layer which uses temporal alignment. Additionally, MEGA employs a novel contrastive loss to synchronize and transfer labels across modalities, enabling act segmentation from labeled synopsis sentences on video shots. Our experimental results show that MEGA outperforms state-of-the-art methods on MovieNet dataset for scene segmentation (with an Average Precision improvement of +1.19%) and on TRIPOD dataset for act segmentation (with a Total Agreement improvement of +5.51%).
Large-scale Vision-Language Models, such as CLIP, learn powerful image-text representations that have found numerous applications, from zero-shot classification to text-to-image generation. Despite that, their capabilities for solving novel discriminative tasks via prompting fall behind those of large language models, such as GPT-3. Here we explore the idea of visual prompt engineering for solving computer vision tasks beyond classification by editing in image space instead of text. In particular, we discover an emergent ability of CLIP, where, by simply drawing a red circle around an object, we can direct the model's attention to that region, while also maintaining global information. We show the power of this simple approach by achieving state-of-the-art in zero-shot referring expressions comprehension and strong performance in keypoint localization tasks. Finally, we draw attention to some potential ethical concerns of large language-vision models.