arXiv subjects

Congyang Ou

Publications and source records attributed to Congyang Ou.

4 recordsLinked to original sources

arXiv2026

When Vision Becomes Text: Visual Token Pruning via Cross-Modal Residual Guidance in VLMs

Abundant visual information strengthens vision-language model (VLM) perception, yet massive visual tokens raise inference costs. Existing visual token pruning methods rely on similarity-based guidance, which exploits pairwise text-vision and vision-vision token correlations for compression. However, such methods only capture local layer-level signals and overlook the whole inference process in VLM. In this paper, we revisit VLM inference and present a new efficient guidance scheme that complements similarity-based guidance. In particular, we identify a key observation: as LLM layers deepen, text tokens continuously aggregate visual information via self-attention and progressively absorb partial visual content into textual representations. To quantify this phenomenon, we propose Cross Modal Absorption (CMA) from a geometric representation perspective to measure how much visual information is absorbed by text, revealing that more visual tokens in deeper layers can be approximately explained by the text subspace. We accordingly propose Cross Modal Residual (CMR). It projects visual tokens onto the text subspace via Tikhonov regularized least squares and exploits reconstruction residuals to quantify visual information that cannot be explained by text. Finally, based on CMR, we present SIEVE, a training-free visual token compression method that combines CMR, text-attention relevance, and residual-space diversity to retain task-relevant and complementary tokens. Experiments on diverse VLM architectures verify the effectiveness of SIEVE. For instance, on LLaVA-NeXT-7B, SIEVE keeps only $11.1\%$ of visual tokens while preserving $97.5\%$ of the original average performance, achieving $3.62\times$ prefill speedup, $2.49\times$ end-to-end speedup, and a $6.02\times$ KV-cache reduction.

cs.CV↗

arXiv2026

CC-Pan: Channel-wise Compression based Diffusion for Efficient Pan-Sharpening

Recently, diffusion models have brought novel insights to pan-sharpening and notably boosted fusion precision. However, most existing models perform diffusion in the pixel space and train distinct models for different multispectral (MS) sensors, suffering from high inference latency and sensor-specific limitations. In this paper, we present CC-Pan, a cross-sensor latent diffusion framework for efficient pan-sharpening. Specifically, CC-Pan trains a band-wise single-channel variational autoencoder (VAE) to encode high-resolution multispectral (HRMS) images into compact latent representations, naturally supporting MS images with varying band counts across different sensors and establishing a basis for inference acceleration. Spectral physical properties, along with PAN and MS images, are then injected into the diffusion backbone through carefully designed unidirectional and bidirectional interactive control structures, achieving high-precision spatial--spectral fusion in the latent diffusion process. Furthermore, a lightweight region-based cross-band attention (RCBA) module is incorporated at the central layer of the diffusion model, reinforcing inter-band spectral connections to boost spectral consistency and further elevate fusion precision. Extensive experimental results on GaoFen-2, QuickBird, and WorldView-3 demonstrate that CC-Pan outperforms state-of-the-art diffusion-based methods across all three benchmarks, attains a $2$--$3\times$ inference speedup, and exhibits robust cross-sensor generalization capability on the held-out WorldView-2 sensor without any sensor-specific retraining.

cs.CV↗

arXiv2026

TRIO: Token Reduction via Inference-Objective Guidance for Efficient Vision-Language Models

Recently, reducing redundant visual tokens in vision-language models (VLMs) to accelerate VLM inference has emerged as a hot topic. However, most existing methods rely on heuristics constructed based on inter-visual-token similarity or cross-modal visual-text similarity, which gives rise to certain limitations in compression performance and practical deployment. In contrast, we propose TRIO from the perspective of inference objectives, which transforms visual token compression into preserving output result invariance and selects tokens primarily by their importance to this goal. Specifically, vision tokens are reordered with the guidance of token-level gradient saliency generated by our designed layer-local proxy loss, a coarse constraint from the current layer to the final result. Then the most valuable vision tokens are selected following the non-maximum suppression (NMS) principle.The proposed TRIO is training-free and compatible with FlashAttention, friendly to practical application and deployment. It can be deployed independently as an encoder-free method, or combined with encoder compression approaches like VisionZip for use as an encoder-involved method. On LLaVA-Next-7B, TRIO retains just 11.1\% of visual tokens but maintains 97.2\% of the original performance, with a 2.75$\times$ prefill speedup, 2.14$\times$ inference speedup, 6.22$\times$ lower FLOPs, and 6.05$\times$ reduced KV Cache overhead.Our code is available at https://github.com/ocy1/TRIO.

cs.CV↗

arXiv2024

Cosine Annealing Optimized Denoising Diffusion Error Correction Codes

To address the issue of increased bit error rates during the later stages of linear search in denoising diffusion error correction codes, we propose a novel method that optimizes denoising diffusion error correction codes (ECC) using cosine annealing. In response to the challenge of decoding long codewords, the proposed method employs a variance adjustment strategy during the reverse diffusion process, rather than maintaining a constant variance. By leveraging cosine annealing, this method effectively lowers the bit error rate and enhances decoding effciency. This letter extensively validates the approach through experiments and demonstrates signifcant improvements in bit error rate reduction and iteration effciency compared to existing methods. This advancement offers a promising solution for improving ECC decoding performance, potentially impacting secure digital communication practices.

cs.IT↗