The significant amount of manual efforts in annotating pixel-level labels has triggered the advancement of unsupervised saliency learning. However without supervision signals state-of-the-art methods can only infer region-level saliency. In this paper we propose to explore the unsupervised salient instance detection (USID) problem for a more fine-grained visual understanding. Our key observation is that self-supervised transformer features may exhibit local similarities as well as different levels of contrast to other regions which provide informative cues to identify salient instances. Hence we propose SCoCo a novel network that models saliency coherence and contrast for USID. SCoCo includes two novel modules: (1) a global background adaptation (GBA) module with a scene-level contrastive loss to extract salient regions from the scene by searching the adaptive "saliency threshold" in the self-supervised transformer features and (2) a locality-aware similarity (LAS) module with an instance-level contrastive loss to group salient regions into instances by modeling the in-region saliency coherence and cross-region saliency contrasts. Extensive experiments show that SCoCo outperforms state-of-the-art weakly-supervised SID methods and carefully designed unsupervised baselines and has comparable performances to fully-supervised SID methods.
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Post-training quantization (PTQ) converts a pre-trained full-precision (FP) model into a quantized model in a training-free manner. Determining suitable quantization parameters such as scaling factors and weight rounding is the primary strategy for mitigating the impact of quantization noise (calibration) and restoring the performance of the quantized models. However the existing activation calibration methods have never considered information degradation between pre- (FP) and post-quantized activations. In this study we introduce a well-defined distributional metric from information theory mutual information into PTQ calibration. We aim to calibrate the quantized activations by maximizing the mutual information between the pre- and post-quantized activations. To realize this goal we establish a contrastive learning (CL) framework for the PTQ calibration where the quantization parameters are optimized through a self-supervised proxy task. Specifically by leveraging CL during the PTQ process we can benefit from pulling the positive pairs of quantized and FP activations collected from the same input samples while pushing negative pairs from different samples. Thanks to the ingeniously designed critic function we avoid the unwanted but often-encountered collision solution in CL especially in calibration scenarios where the amount of calibration data is limited. Additionally we provide a theoretical guarantee that minimizing our designed loss is equivalent to maximizing the desired mutual information. Consequently the quantized activations retain more information which ultimately enhances the performance of the quantized network. Experimental results show that our method can effectively serve as an add-on module to existing SoTA PTQ methods.
In this paper we tackle the problem of self-supervised video alignment and activity progress prediction using in-the-wild videos. Our proposed self-supervised representation learning method carefully addresses different action orderings redundant actions and background frames to generate improved video representations compared to previous methods. Our model generalizes temporal cycle-consistency learning to allow for more flexibility in determining cycle-consistent neighbors. More specifically to handle repeated actions we propose a multi-neighbor cycle consistency and a multi-cycle-back regression loss by finding multiple soft nearest neighbors using a Gaussian Mixture Model. To handle background and redundant frames we introduce a context-dependent drop function in our framework discouraging the alignment of droppable frames. On the other hand to learn from videos of multiple activities jointly we propose a multi-head crosstask network allowing us to embed a video and estimate progress without knowing its activity label. Experiments on multiple datasets show that our method outperforms the state-of-the-art for video alignment and progress prediction.
We introduce SynCLR a novel approach for learning visual representations exclusively from synthetic images without any real data. We synthesize a large dataset of image captions using LLMs then use an off-the-shelf text-to-image model to generate multiple images corresponding to each synthetic caption. We perform visual representation learning on these synthetic images via contrastive learning treating images sharing the same caption as positive pairs. The resulting representations demonstrate remarkable transferability competing favorably with other general-purpose visual representation learners such as CLIP and DINO v2 in image classification tasks. Furthermore in dense prediction tasks such as semantic segmentation SynCLR outperforms previous self-supervised methods by a significant margin e.g. improving over MAE and iBOT by 5.0 and 3.1 mIoU on ADE20k for ViT-B/16.
Current self-supervised methods can primarily be categorized into contrastive learning and masked image modeling. Extensive studies have demonstrated that combining these two approaches can achieve state-of-the-art performance. However these methods essentially reinforce the global consistency of contrastive learning without taking into account the conflicts between these two approaches which hinders their generalizability to arbitrary scenarios. In this paper we theoretically prove that MAE serves as a patch-level contrastive learning where each patch within an image is considered as a distinct category. This presents a significant conflict with global-level contrastive learning which treats all patches in an image as an identical category. To address this conflict this work abandons the non-generalizable global-level constraints and proposes explicit patch-level contrastive learning as a solution. Specifically this work employs the encoder of MAE to generate dual-branch features which then perform patch-level learning through a decoder. In contrast to global-level data augmentation in contrastive learning our approach leverages patch-level feature augmentation to mitigate interference from global-level learning. Consequently our approach can learn heterogeneous representations from a single image while avoiding the conflicts encountered by previous methods. Massive experiments affirm the potential of our method for learning from arbitrary scenarios.
Single point-supervised object detection is gaining attention due to its cost-effectiveness. However existing approaches focus on generating horizontal bounding boxes (HBBs) while ignoring oriented bounding boxes (OBBs) commonly used for objects in aerial images. This paper proposes PointOBB the first single Point-based OBB generation method for oriented object detection. PointOBB operates through the collaborative utilization of three distinctive views: an original view a resized view and a rotated/flipped (rot/flp) view. Upon the original view we leverage the resized and rot/flp views to build a scale augmentation module and an angle acquisition module respectively. In the former module a Scale-Sensitive Consistency (SSC) loss is designed to enhance the deep network's ability to perceive the object scale. For accurate object angle predictions the latter module incorporates self-supervised learning to predict angles which is associated with a scale-guided Dense-to-Sparse (DS) matching strategy for aggregating dense angles corresponding to sparse objects. The resized and rot/flp views are switched using a progressive multi-view switching strategy during training to achieve coupled optimization of scale and angle. Experimental results on the DIOR-R and DOTA-v1.0 datasets demonstrate that PointOBB achieves promising performance and significantly outperforms potential point-supervised baselines.
From SAM to CAMs: Exploring Segment Anything Model for Weakly Supervised Semantic Segmentation
PDF ↗Weakly Supervised Semantic Segmentation (WSSS) aims to learn the concept of segmentation using image-level class labels. Recent WSSS works have shown promising results by using the Segment Anything Model (SAM) a foundation model for segmentation during the inference phase. However we observe that these methods can still be vulnerable to the noise of class activation maps (CAMs) serving as initial seeds. As a remedy this paper introduces From-SAM-to-CAMs (S2C) a novel WSSS framework that directly transfers the knowledge of SAM to the classifier during the training process enhancing the quality of CAMs itself. S2C comprises SAM-segment Contrasting (SSC) and a CAM-based prompting module (CPM) which exploit SAM at the feature and logit levels respectively. SSC performs prototype-based contrasting using SAM's automatic segmentation results. It constrains each feature to be close to the prototype of its segment and distant from prototypes of the others. Meanwhile CPM extracts prompts from the CAM of each class and uses them to generate class-specific segmentation masks through SAM. The masks are aggregated into unified self-supervision based on the confidence score designed to consider the reliability of both SAM and CAMs. S2C achieves a new state-of-the-art performance across all benchmarks outperforming existing studies by significant margins. The code is available at https://github.com/sangrockEG/S2C.
DSL-FIQA: Assessing Facial Image Quality via Dual-Set Degradation Learning and Landmark-Guided Transformer
PDF ↗Generic Face Image Quality Assessment (GFIQA) evaluates the perceptual quality of facial images which is crucial in improving image restoration algorithms and selecting high-quality face images for downstream tasks. We present a novel transformer-based method for GFIQA which is aided by two unique mechanisms. First a novel Dual-Set Degradation Representation Learning (DSL) mechanism uses facial images with both synthetic and real degradations to decouple degradation from content ensuring generalizability to real-world scenarios. This self-supervised method learns degradation features on a global scale providing a robust alternative to conventional methods that use local patch information in degradation learning. Second our transformer leverages facial landmarks to emphasize visually salient parts of a face image in evaluating its perceptual quality. We also introduce a balanced and diverse Comprehensive Generic Face IQA (CGFIQA-40k) dataset of 40K images carefully designed to overcome the biases in particular the imbalances in skin tone and gender representation in existing datasets. Extensive analysis and evaluation demonstrate the robustness of our method marking a significant improvement over prior methods.
3D occupancy prediction is an important task for the robustness of vision-centric autonomous driving which aims to predict whether each point is occupied in the surrounding 3D space. Existing methods usually require 3D occupancy labels to produce meaningful results. However it is very laborious to annotate the occupancy status of each voxel. In this paper we propose SelfOcc to explore a self-supervised way to learn 3D occupancy using only video sequences. We first transform the images into the 3D space (e.g. bird's eye view) to obtain 3D representation of the scene. We directly impose constraints on the 3D representations by treating them as signed distance fields. We can then render 2D images of previous and future frames as self-supervision signals to learn the 3D representations. We propose an MVS-embedded strategy to directly optimize the SDF-induced weights with multiple depth proposals. Our SelfOcc outperforms the previous best method SceneRF by 58.7% using a single frame as input on SemanticKITTI and is the first self-supervised work that produces reasonable 3D occupancy for surround cameras on nuScenes. SelfOcc produces high-quality depth and achieves state-of-the-art results on novel depth synthesis monocular depth estimation and surround-view depth estimation on the SemanticKITTI KITTI-2015 and nuScenes respectively. Code: https://github.com/huang-yh/SelfOcc.
Investigating and Mitigating the Side Effects of Noisy Views for Self-Supervised Clustering Algorithms in Practical Multi-View Scenarios
PDF ↗Multi-view clustering (MVC) aims at exploring category structures among multi-view data in self-supervised manners. Multiple views provide more information than single views and thus existing MVC methods can achieve satisfactory performance. However their performance might seriously degenerate when the views are noisy in practical multi-view scenarios. In this paper we formally investigate the drawback of noisy views and then propose a theoretically grounded deep MVC method (namely MVCAN) to address this issue. Specifically we propose a novel MVC objective that enables un-shared parameters and inconsistent clustering predictions across multiple views to reduce the side effects of noisy views. Furthermore a two-level multi-view iterative optimization is designed to generate robust learning targets for refining individual views' representation learning. Theoretical analysis reveals that MVCAN works by achieving the multi-view consistency complementarity and noise robustness. Finally experiments on extensive public datasets demonstrate that MVCAN outperforms state-of-the-art methods and is robust against the existence of noisy views.
The Object Goal navigation (ObjectNav) task requires the agent to navigate to a specified target in an unseen environment. Since the environment layout is unknown the agent needs to infer the unknown contextual objects from partially observations thereby deducing the likely location of the target. Previous end-to-end RL methods capture contextual relationships through implicit representations while they lack notion of geometry. Alternatively modular methods construct local maps for recording the observed geometric structure of unseen environment however lacking the reasoning of contextual relation limits the exploration efficiency. In this work we propose the self-supervised generative map (SGM) a modular method that learns the explicit context relation via self-supervised learning. The SGM is trained to leverage both episodic observations and general knowledge to reconstruct the masked pixels of a cropped global map. During navigation the agent maintains an incomplete local semantic map meanwhile the unknown regions of the local map are generated by the pre-trained SGM. Based on the generated map the agent sets the predicted location of the target as the goal and moves towards it. Experiments on Gibson MP3D and HM3D show the effectiveness of our method.
Establishing accurate 3D correspondences between shapes stands as a pivotal challenge with profound implications for computer vision and robotics. However existing self-supervised methods for this problem assume perfect input shape alignment restricting their real-world applicability. In this work we introduce a novel self-supervised Rotation-Invariant 3D correspondence learner with Local Shape Transform dubbed RIST that learns to establish dense correspondences between shapes even under challenging intra-class variations and arbitrary orientations. Specifically RIST learns to dynamically formulate an SO(3)-invariant local shape transform for each point which maps the SO(3)-equivariant global shape descriptor of the input shape to a local shape descriptor. These local shape descriptors are provided as inputs to our decoder to facilitate point cloud self- and cross-reconstruction. Our proposed self-supervised training pipeline encourages semantically corresponding points from different shapes to be mapped to similar local shape descriptors enabling RIST to establish dense point-wise correspondences. RIST demonstrates state-of-the-art performances on 3D part label transfer and semantic keypoint transfer given arbitrarily rotated point cloud pairs outperforming existing methods by significant margins.
PhysPT: Physics-aware Pretrained Transformer for Estimating Human Dynamics from Monocular Videos
PDF ↗While current methods have shown promising progress on estimating 3D human motion from monocular videos their motion estimates are often physically unrealistic because they mainly consider kinematics. In this paper we introduce Physics-aware Pretrained Transformer (PhysPT) which improves kinematics-based motion estimates and infers motion forces. PhysPT exploits a Transformer encoder-decoder backbone to effectively learn human dynamics in a self-supervised manner. Moreover it incorporates physics principles governing human motion. Specifically we build a physics-based body representation and contact force model. We leverage them to impose novel physics-inspired training losses (i.e. force loss contact loss and Euler-Lagrange loss) enabling PhysPT to capture physical properties of the human body and the forces it experiences. Experiments demonstrate that once trained PhysPT can be directly applied to kinematics-based estimates to significantly enhance their physical plausibility and generate favourable motion forces. Furthermore we show that these physically meaningful quantities translate into improved accuracy of an important downstream task: human action recognition.
OrCo: Towards Better Generalization via Orthogonality and Contrast for Few-Shot Class-Incremental Learning
PDF ↗Few-Shot Class-Incremental Learning (FSCIL) introduces a paradigm in which the problem space expands with limited data. FSCIL methods inherently face the challenge of catastrophic forgetting as data arrives incrementally making models susceptible to overwriting previously acquired knowledge. Moreover given the scarcity of labeled samples available at any given time models may be prone to overfitting and find it challenging to strike a balance between extensive pretraining and the limited incremental data. To address these challenges we propose the OrCo framework built on two core principles: features' orthogonality in the representation space and contrastive learning. In particular we improve the generalization of the embedding space by employing a combination of supervised and self-supervised contrastive losses during the pretraining phase. Additionally we introduce OrCo loss to address challenges arising from data limitations during incremental sessions. Through feature space perturbations and orthogonality between classes the OrCo loss maximizes margins and reserves space for the following incremental data. This in turn ensures the accommodation of incoming classes in the feature space without compromising previously acquired knowledge. Our experimental results showcase state-of-the-art performance across three benchmark datasets including mini-ImageNet CIFAR100 and CUB datasets. Code is available at https://github.com/noorahmedds/OrCo
Diffusion Transformer (DiT) has emerged as the new trend of generative diffusion models on image generation. In view of extremely slow convergence in typical DiT recent breakthroughs have been driven by mask strategy that significantly improves the training efficiency of DiT with additional intra-image contextual learning. Despite this progress mask strategy still suffers from two inherent limitations: (a) training-inference discrepancy and (b) fuzzy relations between mask reconstruction & generative diffusion process resulting in sub-optimal training of DiT. In this work we address these limitations by novelly unleashing the self-supervised discrimination knowledge to boost DiT training. Technically we frame our DiT in a teacher-student manner. The teacher-student discriminative pairs are built on the diffusion noises along the same Probability Flow Ordinary Differential Equation (PF-ODE). Instead of applying mask reconstruction loss over both DiT encoder and decoder we decouple DiT encoder and decoder to separately tackle discriminative and generative objectives. In particular by encoding discriminative pairs with student and teacher DiT encoders a new discriminative loss is designed to encourage the inter-image alignment in the self-supervised embedding space. After that student samples are fed into student DiT decoder to perform the typical generative diffusion task. Extensive experiments are conducted on ImageNet dataset and our method achieves a competitive balance between training cost and generative capacity.
CuVLER: Enhanced Unsupervised Object Discoveries through Exhaustive Self-Supervised Transformers
PDF ↗In this paper we introduce VoteCut an innovative method for unsupervised object discovery that leverages feature representations from multiple self-supervised models. VoteCut employs normalized-cut based graph partitioning clustering and a pixel voting approach. Additionally We present CuVLER (Cut-Vote-and-LEaRn) a zero-shot model trained using pseudo-labels generated by VoteCut and a novel soft target loss to refine segmentation accuracy. Through rigorous evaluations across multiple datasets and several unsupervised setups our methods demonstrate significant improvements in comparison to previous state-of-the-art models. Our ablation studies further highlight the contributions of each component revealing the robustness and efficacy of our approach. Collectively VoteCut and CuVLER pave the way for future advancements in image segmentation.
To synthesize high-fidelity samples diffusion models typically require auxiliary data to guide the generation process. However it is impractical to procure the painstaking patch-level annotation effort required in specialized domains like histopathology and satellite imagery; it is often performed by domain experts and involves hundreds of millions of patches. Modern-day self-supervised learning (SSL) representations encode rich semantic and visual information. In this paper we posit that such representations are expressive enough to act as proxies to fine-grained human labels. We introduce a novel approach that trains diffusion models conditioned on embeddings from SSL. Our diffusion models successfully project these features back to high-quality histopathology and remote sensing images. In addition we construct larger images by assembling spatially consistent patches inferred from SSL embeddings preserving long-range dependencies. Augmenting real data by generating variations of real images improves downstream classifier accuracy for patch-level and larger image-scale classification tasks. Our models are effective even on datasets not encountered during training demonstrating their robustness and generalizability. Generating images from learned embeddings is agnostic to the source of the embeddings. The SSL embeddings used to generate a large image can either be extracted from a reference image or sampled from an auxiliary model conditioned on any related modality (e.g. class labels text genomic data). As proof of concept we introduce the text-to-large image synthesis paradigm where we successfully synthesize large pathology and satellite images out of text descriptions.
Perceiving the world and forecasting its future state is a critical task for self-driving. Supervised approaches leverage annotated object labels to learn a model of the world --- traditionally with object detections and trajectory predictions or temporal bird's-eye-view (BEV) occupancy fields. However these annotations are expensive and typically limited to a set of predefined categories that do not cover everything we might encounter on the road. Instead we learn to perceive and forecast a continuous 4D (spatio-temporal) occupancy field with self-supervision from LiDAR data. This unsupervised world model can be easily and effectively transferred to downstream tasks. We tackle point cloud forecasting by adding a lightweight learned renderer and achieve state-of-the-art performance in Argoverse 2 nuScenes and KITTI. To further showcase its transferability we fine-tune our model for BEV semantic occupancy forecasting and show that it outperforms the fully supervised state-of-the-art especially when labeled data is scarce. Finally when compared to prior state-of-the-art on spatio-temporal geometric occupancy prediction our 4D world model achieves a much higher recall of objects from classes relevant to self-driving.
ChAda-ViT : Channel Adaptive Attention for Joint Representation Learning of Heterogeneous Microscopy Images
PDF ↗Unlike color photography images which are consistently encoded into RGB channels biological images encompass various modalities where the type of microscopy and the meaning of each channel varies with each experiment. Importantly the number of channels can range from one to a dozen and their correlation is often comparatively much lower than RGB as each of them brings specific information content. This aspect is largely overlooked by methods designed out of the bioimage field and current solutions mostly focus on intra-channel spatial attention often ignoring the relationship between channels yet crucial in most biological applications. Importantly the variable channel type and count prevent the projection of several experiments to a unified representation for large scale pre-training. In this study we propose ChAda-ViT a novel Channel Adaptive Vision Transformer architecture employing an Inter-Channel Attention mechanism on images with an arbitrary number order and type of channels. We also introduce IDRCell100k a bioimage dataset with a rich set of 79 experiments covering 7 microscope modalities with a multitude of channel types and channel counts varying from 1 to 10 per experiment. Our proposed architecture trained in a self-supervised manner outperforms existing approaches in several biologically relevant downstream tasks. Additionally it can be used to bridge the gap for the first time between assays with different microscopes channel numbers or types by embedding various image and experimental modalities into a unified biological image representation. The latter should facilitate interdisciplinary studies and pave the way for better adoption of deep learning in biological image-based analyses.
Learning-based isosurface extraction methods have recently emerged as a robust and efficient alternative to axiomatic techniques. However the vast majority of such approaches rely on supervised training with axiomatically computed ground truths thus potentially inheriting biases and data artefacts of the corresponding axiomatic methods. Steering away from such dependencies we propose a self-supervised training scheme to the Neural Dual Contouring meshing framework resulting in our method: Self-Supervised Dual Contouring (SDC). Instead of optimizing predicted mesh vertices with supervised training we use two novel self-supervised loss functions that encourage the consistency between distances to the generated mesh up to the first order. Meshes reconstructed by SDC surpass existing data-driven methods in capturing intricate details while being more robust to possible irregularities in the input. Furthermore we use the same self-supervised training objective linking inferred mesh and input SDF to regularize the training process of Deep Implicit Networks (DINs). We demonstrate that the resulting DINs produce higher-quality implicit functions ultimately leading to more accurate and detail-preserving surfaces compared to prior baselines for different input modalities. Finally we demonstrate that our self-supervised losses improve meshing performance in the single-view reconstruction task by enabling joint training of predicted SDF and resulting output mesh.