4arXiv cs.LG (Machine Learning)·
AutoBackSwap: Automated background swapping to reduce spurious correlation reliance in image classifiers
Researchers introduce Automated Background Swapping (AutoBackSwap), a data augmentation method that uses a secondary network to disentangle foreground and background in images, inpaint backgrounds, and recombine them to reduce classifier reliance on spurious background correlations. The approach requires patch-wise labeling of only a few hundred samples to train the secondary network and scale augmentation to the full dataset. Notably, AutoBackSwap remains effective even when no training samples break the spurious correlation, outperforming prior methods across multiple image classification benchmarks.
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Robust Adversarial Inputs: Multi-Scale Fooling of Neural Network Classifiers
OpenAI researchers created adversarial images that reliably fool neural network classifiers even when viewed from varied scales and perspectives. This directly challenges the assumption that self-driving car vision systems are robust to adversarial attacks due to their multi-angle image capture. The finding has implications for the security of deployed vision systems in safety-critical applications.
AUDITS: A Comprehensive Benchmark for Image Manipulation Localization Across Multiple Analysis Axes
Researchers introduce AUDITS (Analysis Under Domain-shifts, qualIty, Type, and Size), a benchmark of over 530K images designed to evaluate image manipulation detection across multiple axes including domain shift, manipulation type, and size. The dataset draws from user and news photos and incorporates recent diffusion-based inpaintings. Experiments assess the robustness of existing manipulation detection methods under various domain shifts, aiming to advance development of more generalizable detection approaches.
DeepMind Launches Backstory: Experimental AI Tool for Image Context and Origin
DeepMind has released an experimental AI tool called Backstory that helps users explore the context and origin of images encountered online. The tool appears aimed at helping people better understand and verify visual content they encounter on the web. This is a product-level announcement from a Tier 1 lab, though the body provides minimal technical detail about the underlying approach.
Label-Free Bias Identification in Vision Models via Gradient Probes on Concept Decompositions
This paper introduces a post-hoc, label-free method for identifying spurious correlations in frozen vision classifiers without requiring bias annotations, group labels, or retraining. The approach applies non-negative matrix factorization to intermediate activations to extract interpretable concept vectors, then ranks them using a gradient-based bias estimator derived from misclassified examples. On Colored MNIST, Waterbirds, and CelebA benchmarks, the method recovers known spurious cues and improves worst-group accuracy by up to 17.9 percentage points on Waterbirds by suppressing top-ranked concepts at inference time. Notably, the method surfaces decision-relevant directions that do not always coincide with annotated attributes, offering both an auditing tool and a debiasing handle for deployed models.
Channel-wise Vector Quantization (CVQ): A New Image Tokenization Paradigm with Next-Channel Prediction
Researchers introduce Channel-wise Vector Quantization (CVQ), which replaces conventional patch-wise discrete tokens with channel-wise tokens that represent an image as discrete levels of visual detail. Built on CVQ, the Channel-wise Autoregressive (CAR) model uses a 'next-channel prediction' objective, generating images by progressively refining from global structure to fine-grained attributes. CVQ achieves 100% codebook utilization with a 16K+ codebook and the CAR model scores 86.7 on DPG and 0.79 on GenEval for text-to-image generation. The approach offers a structural alternative to raster-order patch-based autoregressive image generation.
ActiveSAM: Training-free open-vocabulary segmentation via image-conditional class pruning on SAM 3
ActiveSAM is a training-free, zero-shot inference framework that wraps Segment Anything Model 3 (SAM 3) to perform open-vocabulary semantic segmentation more efficiently. It estimates an image-conditioned active class subset at low resolution before running full-resolution decoding only on retained classes, using bucketed prompt multiplexing and margin-aware background calibration. Across eight benchmarks, it outperforms the prior state-of-the-art SegEarth-OV3 by ~1.4 mIoU on average while running up to 5.5x faster on large-vocabulary datasets, with strong robustness to image corruption relevant to autonomous driving and embodied AI.
Apple researchers propose Feature Auto-Encoder to speed diffusion training via compressed DINOv2 embeddings
Researchers at Apple introduced Feature Auto-Encoder (FAE), a latent diffusion image generator that compresses DINOv2 vision encoder embeddings before learning to denoise them, then expands them back for decoding. The approach achieves comparable image quality to state-of-the-art diffusion models while training roughly 7x faster on ImageNet class-conditional generation. The key insight is that shrinking semantically rich vision embeddings reduces compute during diffusion training without sacrificing the representational benefits of large pretrained encoders.
Gazer: Training-free semantic correction for autoregressive visual models using MLLM feedback
Researchers introduce Gazer, a training-free framework that integrates multimodal large language model feedback into the sampling loop of autoregressive visual models (AVMs) to correct semantic errors during generation. The system operates in two stages: Reflective Diagnosis identifies semantic errors in intermediate generation states, and Semantic Correction rewinds and adjusts the generation trajectory to better match the target prompt. Experiments on compositional image and video benchmarks show improved semantic alignment and compositional accuracy across multiple AVMs without additional training. The work addresses a known weakness of next-scale prediction AVMs, where semantic errors accumulate across discrete generation scales.